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	<title>CiteULike: Tag extraction</title>
	<description>CiteULike: Tag extraction</description>


	<link>http://www.citeulike.org/tag/extraction</link>
	<dc:publisher>CiteULike.org</dc:publisher>
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<item rdf:about="http://www.citeulike.org/user/ZiqiZhang/article/1222866">
    <title>Is Knowledge-Free Induction of Multiword Unit Dictionary Headwords a Solved Problem?</title>
    <link>http://www.citeulike.org/user/ZiqiZhang/article/1222866</link>
    <description>&lt;i&gt;&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;We seek a knowledge-free method for inducing multiword units from text corpora for use as machine-readable dictionary headwords. We provide two major evaluations of nine existing collocation-finders and illustrate the continuing need for improvement. We use Latent Semantic Analysis to make modest gains in performance, but we show the significant challenges encountered in trying this approach. 1</description>
    <dc:title>Is Knowledge-Free Induction of Multiword Unit Dictionary Headwords a Solved Problem?</dc:title>

    <dc:creator>Patrick Schone</dc:creator>
    <dc:creator>Daniel Jurafsky</dc:creator>
    <dc:date>2007-04-12T20:28:28-00:00</dc:date>
    <prism:category>extraction</prism:category>
    <prism:category>keyphraseterm</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/ZiqiZhang/article/1197384">
    <title>Learning to extract keyphrases from text</title>
    <link>http://www.citeulike.org/user/ZiqiZhang/article/1197384</link>
    <description>&lt;i&gt;(1999)&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;Many academic journals ask their authors to provide a list of about five to fifteen key words, to appear on the first page of each article. Since these key words are often phrases of two or more words, we prefer to call them keyphrases. There is a surprisingly wide variety of tasks for which keyphrases are useful, as we discuss in this paper. Recent commercial software, such as Microsoft's Word 97 and Verity's Search 97, includes algorithms that automatically extract keyphrases from ...</description>
    <dc:title>Learning to extract keyphrases from text</dc:title>

    <dc:creator>P Turney</dc:creator>
    <dc:source>(1999)</dc:source>
    <dc:date>2007-03-30T07:53:48-00:00</dc:date>
    <prism:publicationYear>1999</prism:publicationYear>
    <prism:category>extraction</prism:category>
    <prism:category>keyphrase</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/ZiqiZhang/article/1223919">
    <title>KEA: Practical Automatic Keyphrase Extraction</title>
    <link>http://www.citeulike.org/user/ZiqiZhang/article/1223919</link>
    <description>&lt;i&gt;(1999), pp. 254-255.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;Keyphrases provide semantic metadata that summarize and characterize documents. This paper describes Kea, an algorithm for automatically extracting keyphrases from text. Kea identifies candidate keyphrases using lexical methods, calculates feature values for each candidate, and uses a machine -learning algorithm to predict which candidates are good keyphrases. The machine learning scheme first builds a prediction model using training documents with known keyphrases, and then uses the model to...</description>
    <dc:title>KEA: Practical Automatic Keyphrase Extraction</dc:title>

    <dc:creator>Ian Witten</dc:creator>
    <dc:creator>Gordon Paynter</dc:creator>
    <dc:creator>Eibe Frank</dc:creator>
    <dc:creator>Carl Gutwin</dc:creator>
    <dc:creator>Craig</dc:creator>
    <dc:source>(1999), pp. 254-255.</dc:source>
    <dc:date>2007-04-13T10:16:30-00:00</dc:date>
    <prism:publicationYear>1999</prism:publicationYear>
    <prism:startingPage>254</prism:startingPage>
    <prism:endingPage>255</prism:endingPage>
    <prism:category>extraction</prism:category>
    <prism:category>keyphrase</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/yangxian/article/1765997">
    <title>Sampling, information extraction and summarisation of hidden web databases</title>
    <link>http://www.citeulike.org/user/yangxian/article/1765997</link>
    <description>&lt;i&gt;Data Knowl. Eng., Vol. 59, No. 2. (November 2006), pp. 213-230.&lt;/i&gt;</description>
    <dc:title>Sampling, information extraction and summarisation of hidden web databases</dc:title>

    <dc:creator>Yih-Ling Hedley</dc:creator>
    <dc:creator>Muhammad Younas</dc:creator>
    <dc:creator>Anne James</dc:creator>
    <dc:creator>Mark Sanderson</dc:creator>
    <dc:identifier>doi:10.1016/j.datak.2006.01.009</dc:identifier>
    <dc:source>Data Knowl. Eng., Vol. 59, No. 2. (November 2006), pp. 213-230.</dc:source>
    <dc:date>2007-10-14T01:34:16-00:00</dc:date>
    <prism:publicationYear>2006</prism:publicationYear>
    <prism:publicationName>Data Knowl. Eng.</prism:publicationName>
    <prism:volume>59</prism:volume>
    <prism:number>2</prism:number>
    <prism:startingPage>213</prism:startingPage>
    <prism:endingPage>230</prism:endingPage>
    <prism:publisher>Elsevier Science Publishers B. V.</prism:publisher>
    <prism:category>extraction</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/yangxian/article/453818">
    <title>Methods for Domain-Independent Information Extraction</title>
    <link>http://www.citeulike.org/user/yangxian/article/453818</link>
    <description>&lt;i&gt;&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;Our KNOWITALL system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an autonomous, domain-independent, and scalable manner.</description>
    <dc:title>Methods for Domain-Independent Information Extraction</dc:title>

    <dc:creator>From Web</dc:creator>
    <dc:date>2005-12-31T14:49:21-00:00</dc:date>
    <prism:category>extraction</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/Yamo20/article/934291">
    <title>Novel method of DNA extraction from bones assisted DNA identification of World Trade Center victims</title>
    <link>http://www.citeulike.org/user/Yamo20/article/934291</link>
    <description>&lt;i&gt;International Congress Series, Vol. 1261 (April 2004), pp. 553-555.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;DNA identification of many mass fatality victims of the 11 September 2001 attack on the World Trade Centers (WTC) in New York City required development of new analytical methods. Development of novel STR multiplex sets with improved performance using challenged DNA samples is described in an accompanying paper. Here we describe modifications and improvements to procedures used to extract DNA from bone fragments found at the site.</description>
    <dc:title>Novel method of DNA extraction from bones assisted DNA identification of World Trade Center victims</dc:title>

    <dc:creator>T Bille</dc:creator>
    <dc:creator>R Wingrove</dc:creator>
    <dc:creator>M Holland</dc:creator>
    <dc:creator>C Holland</dc:creator>
    <dc:creator>C Cave</dc:creator>
    <dc:creator>J Schumm</dc:creator>
    <dc:identifier>doi:10.1016/S0531-5131(03)01831-4</dc:identifier>
    <dc:source>International Congress Series, Vol. 1261 (April 2004), pp. 553-555.</dc:source>
    <dc:date>2006-11-07T12:18:51-00:00</dc:date>
    <prism:publicationYear>2004</prism:publicationYear>
    <prism:publicationName>International Congress Series</prism:publicationName>
    <prism:volume>1261</prism:volume>
    <prism:startingPage>553</prism:startingPage>
    <prism:endingPage>555</prism:endingPage>
    <prism:category>bone</prism:category>
    <prism:category>dna</prism:category>
    <prism:category>extraction</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/xiaoyan2006/article/1158486">
    <title>Automatic document metadata extraction using support vector machines</title>
    <link>http://www.citeulike.org/user/xiaoyan2006/article/1158486</link>
    <description>&lt;i&gt;(2003), pp. 37-48.&lt;/i&gt;</description>
    <dc:title>Automatic document metadata extraction using support vector machines</dc:title>

    <dc:creator>Hui Han</dc:creator>
    <dc:creator>Lee Giles</dc:creator>
    <dc:creator>Eren Manavoglu</dc:creator>
    <dc:creator>Hongyuan Zha</dc:creator>
    <dc:creator>Zhenyue Zhang</dc:creator>
    <dc:creator>Edward Fox</dc:creator>
    <dc:source>(2003), pp. 37-48.</dc:source>
    <dc:date>2007-03-13T19:31:57-00:00</dc:date>
    <prism:publicationYear>2003</prism:publicationYear>
    <prism:startingPage>37</prism:startingPage>
    <prism:endingPage>48</prism:endingPage>
    <prism:publisher>IEEE Computer Society</prism:publisher>
    <prism:category>extraction</prism:category>
    <prism:category>jcdl07</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/xiaoyan2006/article/1158458">
    <title>Bibliographic attribute extraction from erroneous references based on a statistical model</title>
    <link>http://www.citeulike.org/user/xiaoyan2006/article/1158458</link>
    <description>&lt;i&gt;(2003), pp. 49-60.&lt;/i&gt;</description>
    <dc:title>Bibliographic attribute extraction from erroneous references based on a statistical model</dc:title>

    <dc:creator>Atsuhiro Takasu</dc:creator>
    <dc:source>(2003), pp. 49-60.</dc:source>
    <dc:date>2007-03-13T18:17:41-00:00</dc:date>
    <prism:publicationYear>2003</prism:publicationYear>
    <prism:startingPage>49</prism:startingPage>
    <prism:endingPage>60</prism:endingPage>
    <prism:publisher>IEEE Computer Society</prism:publisher>
    <prism:category>extraction</prism:category>
    <prism:category>jcdl07</prism:category>
    <prism:category>syllabus</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/wubianwei/article/1444">
    <title>A two-phase sampling technique for information extraction from hidden web databases</title>
    <link>http://www.citeulike.org/user/wubianwei/article/1444</link>
    <description>&lt;i&gt;(2004), pp. 1-8.&lt;/i&gt;</description>
    <dc:title>A two-phase sampling technique for information extraction from hidden web databases</dc:title>

    <dc:creator>YL Hedley</dc:creator>
    <dc:creator>M Younas</dc:creator>
    <dc:creator>A James</dc:creator>
    <dc:creator>M Sanderson</dc:creator>
    <dc:identifier>doi:10.1145/1031453.1031456</dc:identifier>
    <dc:source>(2004), pp. 1-8.</dc:source>
    <dc:date>2004-12-02T14:35:58-00:00</dc:date>
    <prism:publicationYear>2004</prism:publicationYear>
    <prism:startingPage>1</prism:startingPage>
    <prism:endingPage>8</prism:endingPage>
    <prism:publisher>ACM Press</prism:publisher>
    <prism:category>a</prism:category>
    <prism:category>databases</prism:category>
    <prism:category>extraction</prism:category>
    <prism:category>for</prism:category>
    <prism:category>from</prism:category>
    <prism:category>hidden</prism:category>
    <prism:category>information</prism:category>
    <prism:category>sampling</prism:category>
    <prism:category>technique</prism:category>
    <prism:category>two-phase</prism:category>
    <prism:category>web</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/wittawat/article/1365557">
    <title>Information extraction supported question answering</title>
    <link>http://www.citeulike.org/user/wittawat/article/1365557</link>
    <description>&lt;i&gt;&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;This paper discusses the use of our information extraction (IE) system, Textract, in the questionanswering (QA) track of the recently held TREC-8 tests. One of our major objectives is to examine how IE can help IR (Information Retrieval) in applications like QA. Our study shows: (i) IE can provide solid support for QA; (ii) low-level IE like Named Entity tagging is often a necessary component in handling most types of questions; (iii) a robust natural language shallow parser provides a...</description>
    <dc:title>Information extraction supported question answering</dc:title>

    <dc:creator>R Srihari</dc:creator>
    <dc:creator>W Li</dc:creator>
    <dc:date>2007-06-05T13:01:19-00:00</dc:date>
    <prism:category>answering</prism:category>
    <prism:category>extraction</prism:category>
    <prism:category>information</prism:category>
    <prism:category>question</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/whshen/article/850133">
    <title>One-Pass Evaluation of Region Algebra Expressions</title>
    <link>http://www.citeulike.org/user/whshen/article/850133</link>
    <description>&lt;i&gt;&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;A region algebra is a collection of operations that apply to lists of regions | a useful model for querying text databases. In this paper, we address the problem of evaluating a region algebra expression. A naive method is to evaluate the operations one at a time, storing intermediate results and reading them back in as needed. We describe an alternative method that merges the argument region lists and performs the evaluation using a single pass over this merged list. This is often more ecient...</description>
    <dc:title>One-Pass Evaluation of Region Algebra Expressions</dc:title>

    <dc:creator>Matthew Lai</dc:creator>
    <dc:creator>Frank</dc:creator>
    <dc:date>2006-09-19T21:11:55-00:00</dc:date>
    <prism:category>algebra</prism:category>
    <prism:category>extraction</prism:category>
    <prism:category>region</prism:category>
    <prism:category>span</prism:category>
    <prism:category>text</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/whshen/article/850131">
    <title>Nested Text-Region Algebra</title>
    <link>http://www.citeulike.org/user/whshen/article/850131</link>
    <description>&lt;i&gt;&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;So called region algebras operating on sets of text fragments have been proposed and implemented as query languages for text documents. Text documents often comprise nested regions like lists within lists or procedures within procedures. Earlier versions of region algebra do not support querying nested regions. We address this deficiency by proposing a new, unrestricted region algebra. The new algebra allows dynamic definition of nested regions. This makes it suitable for querying without any...</description>
    <dc:title>Nested Text-Region Algebra</dc:title>

    <dc:creator>Jani Jaakkola</dc:creator>
    <dc:creator>Pekka Kilpeläinen</dc:creator>
    <dc:date>2006-09-19T21:10:08-00:00</dc:date>
    <prism:category>algebra</prism:category>
    <prism:category>extraction</prism:category>
    <prism:category>region</prism:category>
    <prism:category>text</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/weinkauf/article/1703478">
    <title>A Unified Feature Extraction Architecture</title>
    <link>http://www.citeulike.org/user/weinkauf/article/1703478</link>
    <description>&lt;i&gt;Active Flow Control (2007), pp. 119-133.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;We present a unified feature extraction architecture consisting of only three core algorithms that allows to extract and track a rich variety of geometrically defined, local and global features evolving in scalar and vector fields. The architecture builds upon the concepts of Feature Flow Fields and Connectors, which can be implemented using the three core algorithms finding zeros, integrating and intersecting stream objects. We apply our methods to extract and track the topology and vortex core lines both in steady and unsteady flow fields.</description>
    <dc:title>A Unified Feature Extraction Architecture</dc:title>

    <dc:creator>Tino Weinkauf</dc:creator>
    <dc:creator>Jan Sahner</dc:creator>
    <dc:creator>Holger Theisel</dc:creator>
    <dc:creator>Hans-Christian Hege</dc:creator>
    <dc:creator>Hans-Peter Seidel</dc:creator>
    <dc:identifier>doi:10.1007/978-3-540-71439-2_8</dc:identifier>
    <dc:source>Active Flow Control (2007), pp. 119-133.</dc:source>
    <dc:date>2007-09-28T01:35:18-00:00</dc:date>
    <prism:publicationYear>2007</prism:publicationYear>
    <prism:publicationName>Active Flow Control</prism:publicationName>
    <prism:startingPage>119</prism:startingPage>
    <prism:endingPage>133</prism:endingPage>
    <prism:category>extraction</prism:category>
    <prism:category>feature</prism:category>
    <prism:category>flow</prism:category>
    <prism:category>topology</prism:category>
    <prism:category>visualization</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/vmoa/article/816388">
    <title>Feature extraction methods for character recognition-A survey</title>
    <link>http://www.citeulike.org/user/vmoa/article/816388</link>
    <description>&lt;i&gt;Pattern Recognition, Vol. 29, No. 4. (April 1996), pp. 641-662.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;This paper presents an overview of feature extraction methods for off-line recognition of segmented (isolated) characters. Selection of a feature extraction method is probably the single most important factor in achieving high recognition performance in character recognition systems. Different feature extraction methods are designed for different representations of the characters, such as solid binary characters, character contours, skeletons (thinned characters) or gray-level subimages of each individual character. The feature extraction methods are discussed in terms of invariance properties, reconstructability and expected distortions and variability of the characters. The problem of choosing the appropriate feature extraction method for a given application is also discussed. When a few promising feature extraction methods have been identified, they need to be evaluated experimentally to find the best method for the given application.</description>
    <dc:title>Feature extraction methods for character recognition-A survey</dc:title>

    <dc:creator>Due</dc:creator>
    <dc:creator>Anil Jain</dc:creator>
    <dc:creator>Torfinn Taxt</dc:creator>
    <dc:identifier>doi:10.1016/0031-3203(95)00118-2</dc:identifier>
    <dc:source>Pattern Recognition, Vol. 29, No. 4. (April 1996), pp. 641-662.</dc:source>
    <dc:date>2006-08-25T07:00:22-00:00</dc:date>
    <prism:publicationYear>1996</prism:publicationYear>
    <prism:publicationName>Pattern Recognition</prism:publicationName>
    <prism:volume>29</prism:volume>
    <prism:number>4</prism:number>
    <prism:startingPage>641</prism:startingPage>
    <prism:endingPage>662</prism:endingPage>
    <prism:category>extraction</prism:category>
    <prism:category>feature</prism:category>
    <prism:category>methods</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/vmoa/article/820299">
    <title>A novel approach for structural feature extraction: Contour vs. direction</title>
    <link>http://www.citeulike.org/user/vmoa/article/820299</link>
    <description>&lt;i&gt;Pattern Recognition Letters, Vol. 25, No. 9. (2 July 2004), pp. 975-988.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;The paper presents a novel approach for extracting structural features from segmented cursive handwriting. The proposed approach is based on the contour code and stroke direction. The contour code feature utilises the rate of change of slope along the contour profile in addition to other properties such as the ascender and descender count, start point and end point. The direction feature identifies individual line segments or strokes from the character's outer boundary or thinned representation and highlights each character's pertinent direction information. Each feature is investigated employing a benchmark database and the experimental results using the proposed contour code based structural feature are very promising. A comparative evaluation with the directional feature and existing transition feature is included.</description>
    <dc:title>A novel approach for structural feature extraction: Contour vs. direction</dc:title>

    <dc:creator>Brijesh Verma</dc:creator>
    <dc:creator>Michael Blumenstein</dc:creator>
    <dc:creator>Moumita Ghosh</dc:creator>
    <dc:identifier>doi:10.1016/j.patrec.2004.02.013</dc:identifier>
    <dc:source>Pattern Recognition Letters, Vol. 25, No. 9. (2 July 2004), pp. 975-988.</dc:source>
    <dc:date>2006-08-29T01:25:31-00:00</dc:date>
    <prism:publicationYear>2004</prism:publicationYear>
    <prism:publicationName>Pattern Recognition Letters</prism:publicationName>
    <prism:volume>25</prism:volume>
    <prism:number>9</prism:number>
    <prism:startingPage>975</prism:startingPage>
    <prism:endingPage>988</prism:endingPage>
    <prism:category>contour</prism:category>
    <prism:category>extraction</prism:category>
    <prism:category>feature</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/vlachmore/article/1927460">
    <title>Kernel methods for relation extraction</title>
    <link>http://www.citeulike.org/user/vlachmore/article/1927460</link>
    <description>&lt;i&gt;(2003)&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;We present an application of kernel methods to extracting relations from unstructured natural language sources. We introduce kernels defined over shallow parse representations of text, and design efficient algorithms for computing the kernels. We use the devised kernels in conjunction with Support Vector Machine and Voted Perceptron learning algorithms for the task of extracting person-affiliation and organization-location relations from text. We experimentally evaluate the proposed...</description>
    <dc:title>Kernel methods for relation extraction</dc:title>

    <dc:creator>D Zelenko</dc:creator>
    <dc:creator>C Aone</dc:creator>
    <dc:creator>A Richardella</dc:creator>
    <dc:source>(2003)</dc:source>
    <dc:date>2007-11-16T18:06:02-00:00</dc:date>
    <prism:publicationYear>2003</prism:publicationYear>
    <prism:category>extraction</prism:category>
    <prism:category>kernel</prism:category>
    <prism:category>methods</prism:category>
    <prism:category>relation</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/vlachmore/article/454000">
    <title>Weakly-supervised relation classification for information extraction</title>
    <link>http://www.citeulike.org/user/vlachmore/article/454000</link>
    <description>&lt;i&gt;(2004), pp. 581-588.&lt;/i&gt;</description>
    <dc:title>Weakly-supervised relation classification for information extraction</dc:title>

    <dc:creator>Zhu Zhang</dc:creator>
    <dc:identifier>doi:10.1145/1031171.1031279</dc:identifier>
    <dc:source>(2004), pp. 581-588.</dc:source>
    <dc:date>2006-01-02T01:56:12-00:00</dc:date>
    <prism:publicationYear>2004</prism:publicationYear>
    <prism:startingPage>581</prism:startingPage>
    <prism:endingPage>588</prism:endingPage>
    <prism:publisher>ACM Press</prism:publisher>
    <prism:category>bootstrapping</prism:category>
    <prism:category>extraction</prism:category>
    <prism:category>relation</prism:category>
    <prism:category>svm</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/vlachmore/article/1381474">
    <title>Dependency tree kernels for relation extraction</title>
    <link>http://www.citeulike.org/user/vlachmore/article/1381474</link>
    <description>&lt;i&gt;(2004)&lt;/i&gt;</description>
    <dc:title>Dependency tree kernels for relation extraction</dc:title>

    <dc:creator>Aron Culotta</dc:creator>
    <dc:creator>Jeffrey Sorensen</dc:creator>
    <dc:identifier>doi:10.3115/1218955.1219009</dc:identifier>
    <dc:source>(2004)</dc:source>
    <dc:date>2007-06-12T13:31:32-00:00</dc:date>
    <prism:publicationYear>2004</prism:publicationYear>
    <prism:publisher>Association for Computational Linguistics</prism:publisher>
    <prism:category>dependency</prism:category>
    <prism:category>extraction</prism:category>
    <prism:category>kernels</prism:category>
    <prism:category>parsing</prism:category>
    <prism:category>relation</prism:category>
    <prism:category>tree</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/vlachmore/article/81954">
    <title>Textpresso: an ontology-based information retrieval and extraction system for biological literature.</title>
    <link>http://www.citeulike.org/user/vlachmore/article/81954</link>
    <description>&lt;i&gt;PLoS Biol, Vol. 2, No. 11. (November 2004)&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;We have developed Textpresso, a new text-mining system for scientific literature whose capabilities go far beyond those of a simple keyword search engine. Textpresso's two major elements are a collection of the full text of scientific articles split into individual sentences, and the implementation of categories of terms for which a database of articles and individual sentences can be searched. The categories are classes of biological concepts (e.g., gene, allele, cell or cell group, phenotype, etc.) and classes that relate two objects (e.g., association, regulation, etc.) or describe one (e.g., biological process, etc.). Together they form a catalog of types of objects and concepts called an ontology. After this ontology is populated with terms, the whole corpus of articles and abstracts is marked up to identify terms of these categories. The current ontology comprises 33 categories of terms. A search engine enables the user to search for one or a combination of these tags and/or keywords within a sentence or document, and as the ontology allows word meaning to be queried, it is possible to formulate semantic queries. Full text access increases recall of biological data types from 45% to 95%. Extraction of particular biological facts, such as gene-gene interactions, can be accelerated significantly by ontologies, with Textpresso automatically performing nearly as well as expert curators to identify sentences; in searches for two uniquely named genes and an interaction term, the ontology confers a 3-fold increase of search efficiency. Textpresso currently focuses on Caenorhabditis elegans literature, with 3,800 full text articles and 16,000 abstracts. The lexicon of the ontology contains 14,500 entries, each of which includes all versions of a specific word or phrase, and it includes all categories of the Gene Ontology database. Textpresso is a useful curation tool, as well as search engine for researchers, and can readily be extended to other organism-specific corpora of text. Textpresso can be accessed at http://www.textpresso.org or via WormBase at http://www.wormbase.org.</description>
    <dc:title>Textpresso: an ontology-based information retrieval and extraction system for biological literature.</dc:title>

    <dc:creator>HM Müller</dc:creator>
    <dc:creator>EE Kenny</dc:creator>
    <dc:creator>PW Sternberg</dc:creator>
    <dc:identifier>doi:10.1371/journal.pbio.0020309</dc:identifier>
    <dc:source>PLoS Biol, Vol. 2, No. 11. (November 2004)</dc:source>
    <dc:date>2005-01-22T16:50:43-00:00</dc:date>
    <prism:publicationYear>2004</prism:publicationYear>
    <prism:publicationName>PLoS Biol</prism:publicationName>
    <prism:issn>1545-7885</prism:issn>
    <prism:volume>2</prism:volume>
    <prism:number>11</prism:number>
    <prism:category>bionlp</prism:category>
    <prism:category>extraction</prism:category>
    <prism:category>gene_ontology</prism:category>
    <prism:category>information</prism:category>
    <prism:category>ontology</prism:category>
    <prism:category>retrieval</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/vlachmore/article/1102204">
    <title>RelExRelation extraction using dependency parse trees</title>
    <link>http://www.citeulike.org/user/vlachmore/article/1102204</link>
    <description>&lt;i&gt;Bioinformatics, Vol. 23, No. 3. (1 February 2007), pp. 365-371.&lt;/i&gt;</description>
    <dc:title>RelExRelation extraction using dependency parse trees</dc:title>

    <dc:creator>Katrin Fundel</dc:creator>
    <dc:creator>Robert Kuffner</dc:creator>
    <dc:creator>Ralf Zimmer</dc:creator>
    <dc:identifier>doi:10.1093/bioinformatics/btl616</dc:identifier>
    <dc:source>Bioinformatics, Vol. 23, No. 3. (1 February 2007), pp. 365-371.</dc:source>
    <dc:date>2007-02-12T07:01:49-00:00</dc:date>
    <prism:publicationYear>2007</prism:publicationYear>
    <prism:publicationName>Bioinformatics</prism:publicationName>
    <prism:issn>1367-4803</prism:issn>
    <prism:volume>23</prism:volume>
    <prism:number>3</prism:number>
    <prism:startingPage>365</prism:startingPage>
    <prism:endingPage>371</prism:endingPage>
    <prism:publisher>Oxford University Press</prism:publisher>
    <prism:category>bionlp</prism:category>
    <prism:category>dependency</prism:category>
    <prism:category>extraction</prism:category>
    <prism:category>parsing</prism:category>
    <prism:category>relation</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/vlachmore/article/177153">
    <title>Co-occurrence based meta-analysis of scientific texts: retrieving biological relationships between genes.</title>
    <link>http://www.citeulike.org/user/vlachmore/article/177153</link>
    <description>&lt;i&gt;Bioinformatics, Vol. 21, No. 9. (1 May 2005), pp. 2049-2058.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;MOTIVATION: The advent of high-throughput experiments in molecular biology creates a need for methods to efficiently extract and use information for large numbers of genes. Recently, the associative concept space (ACS) has been developed for the representation of information extracted from biomedical literature. The ACS is a Euclidean space in which thesaurus concepts are positioned and the distances between concepts indicates their relatedness. The ACS uses co-occurrence of concepts as a source of information. In this paper we evaluate how well the system can retrieve functionally related genes and we compare its performance with a simple gene co-occurrence method. RESULTS: To assess the performance of the ACS we composed a test set of five groups of functionally related genes. With the ACS good scores were obtained for four of the five groups. When compared to the gene co-occurrence method, the ACS is capable of revealing more functional biological relations and can achieve results with less literature available per gene. Hierarchical clustering was performed on the ACS output, as a potential aid to users, and was found to provide useful clusters. Our results suggest that the algorithm can be of value for researchers studying large numbers of genes. AVAILABILITY: The ACS program is available upon request from the authors. CONTACT: r.jelier@erasmusmc.nl.</description>
    <dc:title>Co-occurrence based meta-analysis of scientific texts: retrieving biological relationships between genes.</dc:title>

    <dc:creator>R Jelier</dc:creator>
    <dc:creator>G Jenster</dc:creator>
    <dc:creator>LC Dorssers</dc:creator>
    <dc:creator>CC van der Eijk</dc:creator>
    <dc:creator>EM van Mulligen</dc:creator>
    <dc:creator>B Mons</dc:creator>
    <dc:creator>JA Kors</dc:creator>
    <dc:source>Bioinformatics, Vol. 21, No. 9. (1 May 2005), pp. 2049-2058.</dc:source>
    <dc:date>2005-05-03T09:38:43-00:00</dc:date>
    <prism:publicationYear>2005</prism:publicationYear>
    <prism:publicationName>Bioinformatics</prism:publicationName>
    <prism:issn>1367-4803</prism:issn>
    <prism:volume>21</prism:volume>
    <prism:number>9</prism:number>
    <prism:startingPage>2049</prism:startingPage>
    <prism:endingPage>2058</prism:endingPage>
    <prism:category>bionlp</prism:category>
    <prism:category>extraction</prism:category>
    <prism:category>relation</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/vlachmore/article/2281284">
    <title>Mining of relations between proteins over biomedical scientific literature using a deep-linguistic approach</title>
    <link>http://www.citeulike.org/user/vlachmore/article/2281284</link>
    <description>&lt;i&gt;Artif. Intell. Med., Vol. 39, No. 2. (February 2007), pp. 127-136.&lt;/i&gt;</description>
    <dc:title>Mining of relations between proteins over biomedical scientific literature using a deep-linguistic approach</dc:title>

    <dc:creator>Fabio Rinaldi</dc:creator>
    <dc:creator>Gerold Schneider</dc:creator>
    <dc:creator>Kaarel Kaljurand</dc:creator>
    <dc:creator>Michael Hess</dc:creator>
    <dc:creator>Christos Andronis</dc:creator>
    <dc:creator>Ourania Konstandi</dc:creator>
    <dc:creator>Andreas Persidis</dc:creator>
    <dc:identifier>doi:10.1016/j.artmed.2006.08.005</dc:identifier>
    <dc:source>Artif. Intell. Med., Vol. 39, No. 2. (February 2007), pp. 127-136.</dc:source>
    <dc:date>2008-01-23T17:59:37-00:00</dc:date>
    <prism:publicationYear>2007</prism:publicationYear>
    <prism:publicationName>Artif. Intell. Med.</prism:publicationName>
    <prism:issn>0933-3657</prism:issn>
    <prism:volume>39</prism:volume>
    <prism:number>2</prism:number>
    <prism:startingPage>127</prism:startingPage>
    <prism:endingPage>136</prism:endingPage>
    <prism:publisher>Elsevier Science Publishers Ltd.</prism:publisher>
    <prism:category>bionlp</prism:category>
    <prism:category>extraction</prism:category>
    <prism:category>relation</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/vlachmore/article/451608">
    <title>Snowball: Extracting Relations from Large Plain-Text Collections</title>
    <link>http://www.citeulike.org/user/vlachmore/article/451608</link>
    <description>&lt;i&gt;(2000)&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;Text documents often contain valuable structured data that is hidden in regular English sentences. This data is best exploited if available as a relational table that we could use for answering precise queries or for running data mining tasks. We explore a technique for extracting such tables from document collections that requires only a handful of training examples from users. These examples are used to generate extraction patterns, that in turn result in new tuples being extracted from the...</description>
    <dc:title>Snowball: Extracting Relations from Large Plain-Text Collections</dc:title>

    <dc:creator>Eugene Agichtein</dc:creator>
    <dc:creator>Luis Gravano</dc:creator>
    <dc:source>(2000)</dc:source>
    <dc:date>2005-12-28T01:32:25-00:00</dc:date>
    <prism:publicationYear>2000</prism:publicationYear>
    <prism:category>extraction</prism:category>
    <prism:category>relation</prism:category>
    <prism:category>rules</prism:category>
    <prism:category>seed</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/vlachmore/article/1185721">
    <title>Corrective feedback and persistent learning for information extraction</title>
    <link>http://www.citeulike.org/user/vlachmore/article/1185721</link>
    <description>&lt;i&gt;Artif. Intell., Vol. 170, No. 14. (October 2006), pp. 1101-1122.&lt;/i&gt;</description>
    <dc:title>Corrective feedback and persistent learning for information extraction</dc:title>

    <dc:creator>Aron Culotta</dc:creator>
    <dc:creator>Trausti Kristjansson</dc:creator>
    <dc:creator>Andrew Mccallum</dc:creator>
    <dc:creator>Paul Viola</dc:creator>
    <dc:identifier>doi:10.1016/j.artint.2006.08.001</dc:identifier>
    <dc:source>Artif. Intell., Vol. 170, No. 14. (October 2006), pp. 1101-1122.</dc:source>
    <dc:date>2007-03-25T01:13:08-00:00</dc:date>
    <prism:publicationYear>2006</prism:publicationYear>
    <prism:publicationName>Artif. Intell.</prism:publicationName>
    <prism:issn>0004-3702</prism:issn>
    <prism:volume>170</prism:volume>
    <prism:number>14</prism:number>
    <prism:startingPage>1101</prism:startingPage>
    <prism:endingPage>1122</prism:endingPage>
    <prism:publisher>Elsevier Science Publishers Ltd.</prism:publisher>
    <prism:category>conditional</prism:category>
    <prism:category>extraction</prism:category>
    <prism:category>feedback</prism:category>
    <prism:category>fields</prism:category>
    <prism:category>information</prism:category>
    <prism:category>random</prism:category>
    <prism:category>user</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/vimalthilak/article/2439517">
    <title>Polarization-based index of refraction and reflection angle estimation for remote sensing applications</title>
    <link>http://www.citeulike.org/user/vimalthilak/article/2439517</link>
    <description>&lt;i&gt;Appl. Opt., Vol. 46, No. 30. (20 October 2007), pp. 7527-7536.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;A passive-polarization-based imaging system records the polarization state of light reflected by objects that are illuminated with an unpolarized and generally uncontrolled source. Such systems can be useful in many remote sensing applications including target detection, object segmentation, and material classification. We present a method to jointly estimate the complex index of refraction and the reflection angle (reflected zenith angle) of a target from multiple measurements collected by a passive polarimeter. An expression for the degree of polarization is derived from the microfacet polarimetric bidirectional reflectance model for the case of scattering in the plane of incidence. Using this expression, we develop a nonlinear least-squares estimation algorithm for extracting an apparent index of refraction and the reflection angle from a set of polarization measurements collected from multiple source positions. Computer simulation results show that the estimation accuracy generally improves with an increasing number of source position measurements. Laboratory results indicate that the proposed method is effective for recovering the reflection angle and that the estimated index of refraction provides a feature vector that is robust to the reflection angle.</description>
    <dc:title>Polarization-based index of refraction and reflection angle estimation for remote sensing applications</dc:title>

    <dc:creator>Vimal Thilak</dc:creator>
    <dc:creator>David Voelz</dc:creator>
    <dc:creator>Charles Creusere</dc:creator>
    <dc:source>Appl. Opt., Vol. 46, No. 30. (20 October 2007), pp. 7527-7536.</dc:source>
    <dc:date>2008-02-28T03:01:13-00:00</dc:date>
    <prism:publicationYear>2007</prism:publicationYear>
    <prism:publicationName>Appl. Opt.</prism:publicationName>
    <prism:volume>46</prism:volume>
    <prism:number>30</prism:number>
    <prism:startingPage>7527</prism:startingPage>
    <prism:endingPage>7536</prism:endingPage>
    <prism:publisher>OSA</prism:publisher>
    <prism:category>extraction</prism:category>
    <prism:category>illumination-invariant</prism:category>
    <prism:category>image</prism:category>
    <prism:category>imaging</prism:category>
    <prism:category>parameters</prism:category>
    <prism:category>passive</prism:category>
    <prism:category>pattern</prism:category>
    <prism:category>polarimetry</prism:category>
    <prism:category>processing</prism:category>
    <prism:category>recognition</prism:category>
    <prism:category>shape</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/tulaydemir/article/899496">
    <title>Wrapper induction for information extraction</title>
    <link>http://www.citeulike.org/user/tulaydemir/article/899496</link>
    <description>&lt;i&gt;(1997)&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;The Internet presents numerous sources of useful information---telephone directories, product catalogs, stock quotes, weather forecasts, etc. Recently, many systems have been built that automatically gather and manipulate such information on a user's behalf. However, these resources are usually formatted for use by people (e.g., the relevant content is embedded in HTML pages), so extracting their content is difficult. Wrappers are often used for this purpose. A wrapper is a procedure for...</description>
    <dc:title>Wrapper induction for information extraction</dc:title>

    <dc:creator>N Kushmerick</dc:creator>
    <dc:creator>D Weld</dc:creator>
    <dc:creator>R Doorenbos</dc:creator>
    <dc:source>(1997)</dc:source>
    <dc:date>2006-10-16T16:03:00-00:00</dc:date>
    <prism:publicationYear>1997</prism:publicationYear>
    <prism:category>extraction</prism:category>
    <prism:category>ie</prism:category>
    <prism:category>wrapper</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/tulaydemir/article/1758402">
    <title>A Survey of Web Information Extraction Systems</title>
    <link>http://www.citeulike.org/user/tulaydemir/article/1758402</link>
    <description>&lt;i&gt;Knowledge and Data Engineering, IEEE Transactions on, Vol. 18, No. 10. (2006), pp. 1411-1428.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;The Internet presents a huge amount of useful information which is usually formatted for its users, which makes it difficult to extract relevant data from various sources. Therefore, the availability of robust, flexible Information Extraction (IE) systems that transform the Web pages into program-friendly structures such as a relational database will become a great necessity. Although many approaches for data extraction from Web pages have been developed, there has been limited effort to compare such tools. Unfortunately, in only a few cases can the results generated by distinct tools be directly compared since the addressed extraction tasks are different. This paper surveys the major Web data extraction approaches and compares them in three dimensions: the task domain, the automation degree, and the techniques used. The criteria of the first dimension explain why an IE system fails to handle some Web sites of particular structures. The criteria of the second dimension classify IE systems based on the techniques used. The criteria of the third dimension measure the degree of automation for IE systems. We believe these criteria provide qualitatively measures to evaluate various IE approaches.</description>
    <dc:title>A Survey of Web Information Extraction Systems</dc:title>

    <dc:creator>Chia-Hui Chang</dc:creator>
    <dc:creator>M Kayed</dc:creator>
    <dc:creator>MR Girgis</dc:creator>
    <dc:creator>KF Shaalan</dc:creator>
    <dc:source>Knowledge and Data Engineering, IEEE Transactions on, Vol. 18, No. 10. (2006), pp. 1411-1428.</dc:source>
    <dc:date>2007-10-12T02:58:00-00:00</dc:date>
    <prism:publicationYear>2006</prism:publicationYear>
    <prism:publicationName>Knowledge and Data Engineering, IEEE Transactions on</prism:publicationName>
    <prism:volume>18</prism:volume>
    <prism:number>10</prism:number>
    <prism:startingPage>1411</prism:startingPage>
    <prism:endingPage>1428</prism:endingPage>
    <prism:category>extraction</prism:category>
    <prism:category>survey</prism:category>
    <prism:category>web</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/tulaydemir/article/494100">
    <title>Automatic information extraction from semi-structured Web pages by pattern discovery</title>
    <link>http://www.citeulike.org/user/tulaydemir/article/494100</link>
    <description>&lt;i&gt;Decis. Support Syst., Vol. 35, No. 1. (April 2003), pp. 129-147.&lt;/i&gt;</description>
    <dc:title>Automatic information extraction from semi-structured Web pages by pattern discovery</dc:title>

    <dc:creator>Chia-Hui Chang</dc:creator>
    <dc:creator>Chun-Nan Hsu</dc:creator>
    <dc:creator>Shao-Cheng Lui</dc:creator>
    <dc:identifier>doi:10.1016/S0167-9236(02)00100-8</dc:identifier>
    <dc:source>Decis. Support Syst., Vol. 35, No. 1. (April 2003), pp. 129-147.</dc:source>
    <dc:date>2006-02-06T07:31:53-00:00</dc:date>
    <prism:publicationYear>2003</prism:publicationYear>
    <prism:publicationName>Decis. Support Syst.</prism:publicationName>
    <prism:issn>0167-9236</prism:issn>
    <prism:volume>35</prism:volume>
    <prism:number>1</prism:number>
    <prism:startingPage>129</prism:startingPage>
    <prism:endingPage>147</prism:endingPage>
    <prism:publisher>Elsevier Science Publishers B. V.</prism:publisher>
    <prism:category>2003</prism:category>
    <prism:category>automatic</prism:category>
    <prism:category>extraction</prism:category>
    <prism:category>iepad</prism:category>
    <prism:category>pattern</prism:category>
    <prism:category>semi-structured</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/tulaydemir/article/530795">
    <title>Automatic Web Information Extraction in the ROADRUNNER System</title>
    <link>http://www.citeulike.org/user/tulaydemir/article/530795</link>
    <description>&lt;i&gt;(2002), pp. 264-277.&lt;/i&gt;</description>
    <dc:title>Automatic Web Information Extraction in the ROADRUNNER System</dc:title>

    <dc:creator>Valter Crescenzi</dc:creator>
    <dc:creator>Giansalvatore Mecca</dc:creator>
    <dc:creator>Paolo Merialdo</dc:creator>
    <dc:source>(2002), pp. 264-277.</dc:source>
    <dc:date>2006-03-04T06:08:15-00:00</dc:date>
    <prism:publicationYear>2002</prism:publicationYear>
    <prism:startingPage>264</prism:startingPage>
    <prism:endingPage>277</prism:endingPage>
    <prism:publisher>Springer-Verlag</prism:publisher>
    <prism:category>automatic</prism:category>
    <prism:category>extraction</prism:category>
    <prism:category>html</prism:category>
    <prism:category>roadrunner</prism:category>
    <prism:category>system</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/tulaydemir/article/520460">
    <title>Automatic web news extraction using tree edit distance</title>
    <link>http://www.citeulike.org/user/tulaydemir/article/520460</link>
    <description>&lt;i&gt;(2004)&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;The Web poses itself as the largest data repository ever available in the history of humankind. Major efforts have been made in order to provide efficient access to relevant information within this huge repository of data. Although several techniques have been developed to the problem of Web data extraction, their use is still not spread, mostly because of the need for high human intervention and the low quality of the extraction results. In this paper, we present a domain-oriented approach to...</description>
    <dc:title>Automatic web news extraction using tree edit distance</dc:title>

    <dc:creator>D Reis</dc:creator>
    <dc:creator>P Golgher</dc:creator>
    <dc:creator>A Silva</dc:creator>
    <dc:creator>A Laender</dc:creator>
    <dc:source>(2004)</dc:source>
    <dc:date>2006-02-25T06:20:37-00:00</dc:date>
    <prism:publicationYear>2004</prism:publicationYear>
    <prism:category>extraction</prism:category>
    <prism:category>news</prism:category>
    <prism:category>tree</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/tulaydemir/article/876099">
    <title>Automatic Segmentation of Text into Structured Records</title>
    <link>http://www.citeulike.org/user/tulaydemir/article/876099</link>
    <description>&lt;i&gt;(2001)&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;In this paper we present a method for automatically segmenting unformatted text records into structured elements. Several useful data sources today are human-generated as continuous text whereas convenient usage requires the data to be organized as structured records. A prime motivation is the warehouse address cleaning problem of transforming dirty addresses stored in large corporate databases as a single text field into subfields like &#34;City&#34; and &#34;Street&#34;. Existing tools rely on hand-tuned,...</description>
    <dc:title>Automatic Segmentation of Text into Structured Records</dc:title>

    <dc:creator>Vinayak Borkar</dc:creator>
    <dc:creator>Kaustubh Deshmukh</dc:creator>
    <dc:creator>Sunita Sarawagi</dc:creator>
    <dc:source>(2001)</dc:source>
    <dc:date>2006-09-28T08:53:55-00:00</dc:date>
    <prism:publicationYear>2001</prism:publicationYear>
    <prism:category>extraction</prism:category>
    <prism:category>hmm</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/tulaydemir/article/1766116">
    <title>A redundancy-based method for the extraction of relation instances from the Web</title>
    <link>http://www.citeulike.org/user/tulaydemir/article/1766116</link>
    <description>&lt;i&gt;Int. J. Hum.-Comput. Stud., Vol. 65, No. 9. (September 2007), pp. 816-831.&lt;/i&gt;</description>
    <dc:title>A redundancy-based method for the extraction of relation instances from the Web</dc:title>

    <dc:creator>Viktor de Boer</dc:creator>
    <dc:creator>Maarten van Someren</dc:creator>
    <dc:creator>Bob Wielinga</dc:creator>
    <dc:identifier>doi:10.1016/j.ijhcs.2007.05.002</dc:identifier>
    <dc:source>Int. J. Hum.-Comput. Stud., Vol. 65, No. 9. (September 2007), pp. 816-831.</dc:source>
    <dc:date>2007-10-14T03:11:55-00:00</dc:date>
    <prism:publicationYear>2007</prism:publicationYear>
    <prism:publicationName>Int. J. Hum.-Comput. Stud.</prism:publicationName>
    <prism:issn>1071-5819</prism:issn>
    <prism:volume>65</prism:volume>
    <prism:number>9</prism:number>
    <prism:startingPage>816</prism:startingPage>
    <prism:endingPage>831</prism:endingPage>
    <prism:publisher>Academic Press, Inc.</prism:publisher>
    <prism:category>extraction</prism:category>
    <prism:category>ontology</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/tulaydemir/article/1766076">
    <title>Automating the extraction of data from HTML tables with unknown structure</title>
    <link>http://www.citeulike.org/user/tulaydemir/article/1766076</link>
    <description>&lt;i&gt;Data Knowl. Eng., Vol. 54, No. 1. (July 2005), pp. 3-28.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;Data on the Web in HTML tables is mostly structured, but we usually do not know the structure in advance. Thus, we cannot directly query for data of interest. We propose a solution to this problem based on document-independent extraction ontologies. Our solution entails elements of table understanding, data integration, and wrapper creation. Table understanding allows us to find tables of interest within a Web page, recognize attributes and values within the table, pair attributes with values, and form records. Data-integration techniques allow us to match source records with a target schema. Ontologically specified wrappers allow us to extract data from source records into a target schema. Experimental results show that we can successfully locate data of interest in tables and map the data from source HTML tables with unknown structure to a given target database schema. We can thus “directly” query source data with unknown structure through a known target schema.</description>
    <dc:title>Automating the extraction of data from HTML tables with unknown structure</dc:title>

    <dc:creator>David Embley</dc:creator>
    <dc:creator>Cui Tao</dc:creator>
    <dc:creator>Stephen Liddle</dc:creator>
    <dc:identifier>doi:10.1016/j.datak.2004.10.004</dc:identifier>
    <dc:source>Data Knowl. Eng., Vol. 54, No. 1. (July 2005), pp. 3-28.</dc:source>
    <dc:date>2007-10-14T02:40:17-00:00</dc:date>
    <prism:publicationYear>2005</prism:publicationYear>
    <prism:publicationName>Data Knowl. Eng.</prism:publicationName>
    <prism:volume>54</prism:volume>
    <prism:number>1</prism:number>
    <prism:startingPage>3</prism:startingPage>
    <prism:endingPage>28</prism:endingPage>
    <prism:publisher>Elsevier Science Publishers B. V.</prism:publisher>
    <prism:category>2005</prism:category>
    <prism:category>automatic</prism:category>
    <prism:category>extraction</prism:category>
    <prism:category>ontology</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/tomisc/article/2919798">
    <title>Extraction of oil from Jatropha curcas L. seed kernels by combination of ultrasonication and aqueous enzymatic oil extraction</title>
    <link>http://www.citeulike.org/user/tomisc/article/2919798</link>
    <description>&lt;i&gt;Bioresource Technology, Vol. 96, No. 1. (January 2005), pp. 121-123.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;Use of ultrasonication as a pretreatment before aqueous oil extraction and aqueous enzymatic oil extraction was found to be useful in the case of extraction of oil from the seeds of Jatropha curcas L. The use of ultrasonication for 10 min at pH 9.0 followed by aqueous oil extraction gave a yield of 67%. However, the maximum yield of 74% was obtained by ultrasonication for 5 min followed by aqueous enzymatic oil extraction using an alkaline protease at pH 9.0. Use of ultrasonication also resulted in reducing the process time from 18 to 6 h.</description>
    <dc:title>Extraction of oil from Jatropha curcas L. seed kernels by combination of ultrasonication and aqueous enzymatic oil extraction</dc:title>

    <dc:creator>Shweta Shah</dc:creator>
    <dc:creator>Aparna Sharma</dc:creator>
    <dc:creator>MN Gupta</dc:creator>
    <dc:identifier>doi:10.1016/j.biortech.2004.02.026</dc:identifier>
    <dc:source>Bioresource Technology, Vol. 96, No. 1. (January 2005), pp. 121-123.</dc:source>
    <dc:date>2008-06-23T21:20:29-00:00</dc:date>
    <prism:publicationYear>2005</prism:publicationYear>
    <prism:publicationName>Bioresource Technology</prism:publicationName>
    <prism:volume>96</prism:volume>
    <prism:number>1</prism:number>
    <prism:startingPage>121</prism:startingPage>
    <prism:endingPage>123</prism:endingPage>
    <prism:category>extraction</prism:category>
    <prism:category>jatropha</prism:category>
    <prism:category>oil</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/tkosaka/article/1586540">
    <title>Does Lootable Wealth Breed Disorder?: A Political Economy of Extraction Framework</title>
    <link>http://www.citeulike.org/user/tkosaka/article/1586540</link>
    <description>&lt;i&gt;Comparative Political Studies, Vol. 39, No. 8. (1 October 2006), pp. 943-968.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;This article proposes a political economy of extraction framework that explains political order and state collapse as alternative outcomes in the face of lootable wealth. Different types of institutions of extraction can be built on lootable resources--with divergent effects on political stability. If rulers are able to forge institutions of extraction that give them control of revenues generated by lootable resources, then these resources can contribute to political order by providing the income with which to govern. Conversely, the breakdown or absence of such institutions increases the risk of civil war by making it easier for rebels to organize. The framework is used to explain two puzzling cases that experienced sharply contrasting political trajectories in the face of lootable resources: Sierra Leone and Burma. A focus on institutions of extraction provides a stronger understanding of the wide range of political outcomes--from chaos, to dictatorship, to democracy--in resource-rich countries. 10.1177/0010414006288724</description>
    <dc:title>Does Lootable Wealth Breed Disorder?: A Political Economy of Extraction Framework</dc:title>

    <dc:creator>Richard Snyder</dc:creator>
    <dc:identifier>doi:10.1177/0010414006288724</dc:identifier>
    <dc:source>Comparative Political Studies, Vol. 39, No. 8. (1 October 2006), pp. 943-968.</dc:source>
    <dc:date>2007-08-23T18:25:19-00:00</dc:date>
    <prism:publicationYear>2006</prism:publicationYear>
    <prism:publicationName>Comparative Political Studies</prism:publicationName>
    <prism:volume>39</prism:volume>
    <prism:number>8</prism:number>
    <prism:startingPage>943</prism:startingPage>
    <prism:endingPage>968</prism:endingPage>
    <prism:category>burma</prism:category>
    <prism:category>civil_war</prism:category>
    <prism:category>extraction</prism:category>
    <prism:category>institution</prism:category>
    <prism:category>lootable_wealth</prism:category>
    <prism:category>natural_resources</prism:category>
    <prism:category>order</prism:category>
    <prism:category>political_economy</prism:category>
    <prism:category>sierra_leone</prism:category>
    <prism:category>_zzz_cp_keio20080628</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/tejasgn/article/1105321">
    <title>Augmented Reality Visualization for Laparoscopic Surgery</title>
    <link>http://www.citeulike.org/user/tejasgn/article/1105321</link>
    <description>&lt;i&gt;(1998), pp. 934-943.&lt;/i&gt;</description>
    <dc:title>Augmented Reality Visualization for Laparoscopic Surgery</dc:title>

    <dc:creator>Henry Fuchs</dc:creator>
    <dc:creator>Mark Livingston</dc:creator>
    <dc:creator>Ramesh Raskar</dc:creator>
    <dc:creator>D`nardo Colucci</dc:creator>
    <dc:creator>Kurtis Keller</dc:creator>
    <dc:creator>Andrei State</dc:creator>
    <dc:creator>Jessica Crawford</dc:creator>
    <dc:creator>Paul Rademacher</dc:creator>
    <dc:creator>Samuel Drake</dc:creator>
    <dc:creator>Anthony Meyer</dc:creator>
    <dc:source>(1998), pp. 934-943.</dc:source>
    <dc:date>2007-02-13T17:05:41-00:00</dc:date>
    <prism:publicationYear>1998</prism:publicationYear>
    <prism:startingPage>934</prism:startingPage>
    <prism:endingPage>943</prism:endingPage>
    <prism:publisher>Springer-Verlag</prism:publisher>
    <prism:category>depth</prism:category>
    <prism:category>extraction</prism:category>
    <prism:category>laproscope</prism:category>
    <prism:category>stereo</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/sparfait/article/1104631">
    <title>Tumour grading from magnetic resonance spectroscopy: a comparison of feature extraction with variable selection.</title>
    <link>http://www.citeulike.org/user/sparfait/article/1104631</link>
    <description>&lt;i&gt;Stat Med, Vol. 22, No. 1. (15 January 2003), pp. 147-164.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;Magnetic resonance spectroscopy (MRS) provides a non-invasive measurement of the biochemistry of living tissue. However, signal variation due to tissue heterogeneity causes considerable mixing between different disease categories, making accurate class assignments difficult. This paper compares a systematic methodology for classifier design using multivariate bayesian variable selection (MBVS), with one based on feature extraction using independent component analysis (ICA). We illustrate the methodology and assess the classification performance using a data set comprising 41 magnetic resonance spectra acquired in vivo from two grades of brain tumour, namely low- and medium-grade astrocytic tumours, labelled astrocytomas (AST), and high-grade gliomas and glioblastomas labelled glioblastomas (GL). The aim of this study is threefold. First, to describe the application of the alternative methodologies to MRS, then to benchmark their classification performance, and finally to interpret the classification models in terms of biologically relevant signals derived from the spectra. The classification performance is assessed using the bootstrap method and by application to a test sample in a retrospective study.</description>
    <dc:title>Tumour grading from magnetic resonance spectroscopy: a comparison of feature extraction with variable selection.</dc:title>

    <dc:creator>Y Huang</dc:creator>
    <dc:creator>PJ Lisboa</dc:creator>
    <dc:creator>W El-Deredy</dc:creator>
    <dc:identifier>doi:10.1002/sim.1321</dc:identifier>
    <dc:source>Stat Med, Vol. 22, No. 1. (15 January 2003), pp. 147-164.</dc:source>
    <dc:date>2007-02-13T08:23:56-00:00</dc:date>
    <prism:publicationYear>2003</prism:publicationYear>
    <prism:publicationName>Stat Med</prism:publicationName>
    <prism:issn>0277-6715</prism:issn>
    <prism:volume>22</prism:volume>
    <prism:number>1</prism:number>
    <prism:startingPage>147</prism:startingPage>
    <prism:endingPage>164</prism:endingPage>
    <prism:category>extraction</prism:category>
    <prism:category>feature</prism:category>
    <prism:category>mrs</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/seungwon/article/791436">
    <title>Adaptive web information extraction</title>
    <link>http://www.citeulike.org/user/seungwon/article/791436</link>
    <description>&lt;i&gt;Commun. ACM, Vol. 49, No. 5. (May 2006), pp. 78-84.&lt;/i&gt;</description>
    <dc:title>Adaptive web information extraction</dc:title>

    <dc:creator>Dawn Gregg</dc:creator>
    <dc:creator>Steven Walczak</dc:creator>
    <dc:identifier>doi:10.1145/1125944.1125945</dc:identifier>
    <dc:source>Commun. ACM, Vol. 49, No. 5. (May 2006), pp. 78-84.</dc:source>
    <dc:date>2006-08-09T19:53:46-00:00</dc:date>
    <prism:publicationYear>2006</prism:publicationYear>
    <prism:publicationName>Commun. ACM</prism:publicationName>
    <prism:issn>0001-0782</prism:issn>
    <prism:volume>49</prism:volume>
    <prism:number>5</prism:number>
    <prism:startingPage>78</prism:startingPage>
    <prism:endingPage>84</prism:endingPage>
    <prism:publisher>ACM Press</prism:publisher>
    <prism:category>extraction</prism:category>
    <prism:category>information</prism:category>
    <prism:category>retrieval</prism:category>
    <prism:category>web</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/selvasukumar/article/1632698">
    <title>Purification of Nucleic Acids by Extraction with Phenol:Chloroform</title>
    <link>http://www.citeulike.org/user/selvasukumar/article/1632698</link>
    <description>&lt;i&gt;Cold Spring Harbor Protocols, Vol. 2006, No. 1. (1 May 2006), pdb.prot4455.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;10.1101/pdb.prot4455</description>
    <dc:title>Purification of Nucleic Acids by Extraction with Phenol:Chloroform</dc:title>

    <dc:creator>Joseph Sambrook</dc:creator>
    <dc:creator>David Russell</dc:creator>
    <dc:identifier>doi:10.1101/pdb.prot4455</dc:identifier>
    <dc:source>Cold Spring Harbor Protocols, Vol. 2006, No. 1. (1 May 2006), pdb.prot4455.</dc:source>
    <dc:date>2007-09-07T22:49:34-00:00</dc:date>
    <prism:publicationYear>2006</prism:publicationYear>
    <prism:publicationName>Cold Spring Harbor Protocols</prism:publicationName>
    <prism:volume>2006</prism:volume>
    <prism:number>1</prism:number>
    <prism:startingPage>pdb.prot4455</prism:startingPage>
    <prism:category>chloroform</prism:category>
    <prism:category>extraction</prism:category>
    <prism:category>phenol</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/seb314/article/843472">
    <title>Hierarchical mesh decomposition using fuzzy clustering and cuts</title>
    <link>http://www.citeulike.org/user/seb314/article/843472</link>
    <description>&lt;i&gt;ACM Transactions on Graphics, Vol. 22, No. 3. (July 2003), pp. 954-961.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;Cutting up a complex object into simpler sub-objects is a fundamental problem in various disciplines. In image processing, images are segmented while in computational geometry, solid polyhedra are decomposed. In recent years, in computer graphics, polygonal meshes are decomposed into sub-meshes. In this paper we propose a novel hierarchical mesh decomposition algorithm. Our algorithm computes a decomposition into the meaningful components of a given mesh, which generally refers to segmentation at regions of deep concavities. The algorithm also avoids over-segmentation and jaggy boundaries between the components. Finally, we demonstrate the utility of the algorithm in control-skeleton extraction.</description>
    <dc:title>Hierarchical mesh decomposition using fuzzy clustering and cuts</dc:title>

    <dc:creator>Sagi Katz</dc:creator>
    <dc:creator>Ayellet Tal</dc:creator>
    <dc:identifier>doi:10.1145/882262.882369</dc:identifier>
    <dc:source>ACM Transactions on Graphics, Vol. 22, No. 3. (July 2003), pp. 954-961.</dc:source>
    <dc:date>2006-09-14T13:57:06-00:00</dc:date>
    <prism:publicationYear>2003</prism:publicationYear>
    <prism:publicationName>ACM Transactions on Graphics</prism:publicationName>
    <prism:volume>22</prism:volume>
    <prism:number>3</prism:number>
    <prism:startingPage>954</prism:startingPage>
    <prism:endingPage>961</prism:endingPage>
    <prism:publisher>ACM Press</prism:publisher>
    <prism:category>control-skeleton</prism:category>
    <prism:category>decomposition</prism:category>
    <prism:category>extraction</prism:category>
    <prism:category>mesh</prism:category>
    <prism:category>segmentation</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/seabay/article/348285">
    <title>Monadic datalog and the expressive power of languages for Web information extraction</title>
    <link>http://www.citeulike.org/user/seabay/article/348285</link>
    <description>&lt;i&gt;J. ACM, Vol. 51, No. 1. (January 2004), pp. 74-113.&lt;/i&gt;</description>
    <dc:title>Monadic datalog and the expressive power of languages for Web information extraction</dc:title>

    <dc:creator>Georg Gottlob</dc:creator>
    <dc:creator>Christoph Koch</dc:creator>
    <dc:identifier>doi:10.1145/962446.962450</dc:identifier>
    <dc:source>J. ACM, Vol. 51, No. 1. (January 2004), pp. 74-113.</dc:source>
    <dc:date>2005-10-11T19:34:21-00:00</dc:date>
    <prism:publicationYear>2004</prism:publicationYear>
    <prism:publicationName>J. ACM</prism:publicationName>
    <prism:issn>0004-5411</prism:issn>
    <prism:volume>51</prism:volume>
    <prism:number>1</prism:number>
    <prism:startingPage>74</prism:startingPage>
    <prism:endingPage>113</prism:endingPage>
    <prism:publisher>ACM Press</prism:publisher>
    <prism:category>datalog</prism:category>
    <prism:category>extraction</prism:category>
    <prism:category>mso</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/Scis0000002/article/940795">
    <title>Is &#8220;the theory of everything&#8221; merely the ultimate ensemble theory?</title>
    <link>http://www.citeulike.org/user/Scis0000002/article/940795</link>
    <description>&lt;i&gt;(1 Dec 1998)&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;We discuss some physical consequences of what might be called &#8220;the ultimate ensemble theory&#8221;, where not only worlds corresponding to say different sets of initial data or different physical constants are considered equally real, but also worlds ruled by altogether different equations. The only postulate in this theory is that all structures that exist mathematically exist also physically, by which we mean that in those complex enough to contain self-aware substructures (SASs), these SASs will subjectively perceive themselves as existing in a physically &#8220;real&#8221; world. We find that it is far from clear that this simple theory, which has no free parameters whatsoever, is observationally ruled out. The predictions of the theory take the form of probability distributions for the outcome of experiments, which makes it testable. In addition, it may be possible to rule it out by comparing its a priori predictions for the observable attributes of nature (the particle masses, the dimensionality of spacetime, etc) with what is observed.</description>
    <dc:title>Is &#8220;the theory of everything&#8221; merely the ultimate ensemble theory?</dc:title>

    <dc:creator>Max Tegmark</dc:creator>
    <dc:source>(1 Dec 1998)</dc:source>
    <dc:date>2006-11-12T17:22:35-00:00</dc:date>
    <prism:publicationYear>1998</prism:publicationYear>
    <prism:category>accessibility</prism:category>
    <prism:category>all-categorical-structures</prism:category>
    <prism:category>all-mathematical-structures</prism:category>
    <prism:category>all-possible-worlds</prism:category>
    <prism:category>complexity</prism:category>
    <prism:category>computability</prism:category>
    <prism:category>computability-theory</prism:category>
    <prism:category>constructivization</prism:category>
    <prism:category>control</prism:category>
    <prism:category>controlability</prism:category>
    <prism:category>decision-procedure</prism:category>
    <prism:category>exploitation</prism:category>
    <prism:category>exploiting</prism:category>
    <prism:category>exploration</prism:category>
    <prism:category>exploring</prism:category>
    <prism:category>extendability</prism:category>
    <prism:category>extraction</prism:category>
    <prism:category>formal-interoperability</prism:category>
    <prism:category>formalization-hypothesis</prism:category>
    <prism:category>formal-problem-solving</prism:category>
    <prism:category>formal-science</prism:category>
    <prism:category>formal-theories</prism:category>
    <prism:category>goedel-machine</prism:category>
    <prism:category>hierarchy-of-machines</prism:category>
    <prism:category>higher-dimensional-category-theory</prism:category>
    <prism:category>hypercomputability</prism:category>
    <prism:category>hypercomputing</prism:category>
    <prism:category>hyperefficiency</prism:category>
    <prism:category>intelligence-order-relations</prism:category>
    <prism:category>interconnectivity</prism:category>
    <prism:category>interoperability</prism:category>
    <prism:category>mathematical-structures</prism:category>
    <prism:category>metaevolution</prism:category>
    <prism:category>metalogical-frameworks</prism:category>
    <prism:category>metalogics</prism:category>
    <prism:category>metamathematical-frameworks</prism:category>
    <prism:category>metamathematics</prism:category>
    <prism:category>metaphilosophy</prism:category>
    <prism:category>metascience</prism:category>
    <prism:category>metaselection</prism:category>
    <prism:category>philosophy-of-science</prism:category>
    <prism:category>physical-structures</prism:category>
    <prism:category>power</prism:category>
    <prism:category>presentation</prism:category>
    <prism:category>quantum-computability</prism:category>
    <prism:category>regularities</prism:category>
    <prism:category>representation</prism:category>
    <prism:category>resource-allocation</prism:category>
    <prism:category>selection</prism:category>
    <prism:category>specification</prism:category>
    <prism:category>survivability</prism:category>
    <prism:category>topos-theory</prism:category>
    <prism:category>transformation</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/Scis0000002/article/126700">
    <title>Mining the peanut gallery: opinion extraction and semantic classification of product reviews</title>
    <link>http://www.citeulike.org/user/Scis0000002/article/126700</link>
    <description>&lt;i&gt;(2003), pp. 519-528.&lt;/i&gt;</description>
    <dc:title>Mining the peanut gallery: opinion extraction and semantic classification of product reviews</dc:title>

    <dc:creator>Kushal Dave</dc:creator>
    <dc:creator>Steve Lawrence</dc:creator>
    <dc:creator>David Pennock</dc:creator>
    <dc:identifier>doi:10.1145/775152.775226</dc:identifier>
    <dc:source>(2003), pp. 519-528.</dc:source>
    <dc:date>2005-03-14T16:47:56-00:00</dc:date>
    <prism:publicationYear>2003</prism:publicationYear>
    <prism:startingPage>519</prism:startingPage>
    <prism:endingPage>528</prism:endingPage>
    <prism:publisher>ACM Press</prism:publisher>
    <prism:category>extraction</prism:category>
    <prism:category>opinions</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/Scis0000002/article/1376317">
    <title>Automatic semantics extraction in law documents</title>
    <link>http://www.citeulike.org/user/Scis0000002/article/1376317</link>
    <description>&lt;i&gt;(2005), pp. 133-140.&lt;/i&gt;</description>
    <dc:title>Automatic semantics extraction in law documents</dc:title>

    <dc:creator>C Biagioli</dc:creator>
    <dc:creator>E Francesconi</dc:creator>
    <dc:creator>A Passerini</dc:creator>
    <dc:creator>S Montemagni</dc:creator>
    <dc:creator>C Soria</dc:creator>
    <dc:identifier>doi:10.1145/1165485.1165506</dc:identifier>
    <dc:source>(2005), pp. 133-140.</dc:source>
    <dc:date>2007-06-10T15:55:29-00:00</dc:date>
    <prism:publicationYear>2005</prism:publicationYear>
    <prism:startingPage>133</prism:startingPage>
    <prism:endingPage>140</prism:endingPage>
    <prism:publisher>ACM Press</prism:publisher>
    <prism:category>extraction</prism:category>
    <prism:category>laws</prism:category>
    <prism:category>rules</prism:category>
    <prism:category>semantics</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/Scis0000002/article/1577672">
    <title>Learning taxonomic relations from heterogeneous sources</title>
    <link>http://www.citeulike.org/user/Scis0000002/article/1577672</link>
    <description>&lt;i&gt;(2004)&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;We present a novel approach to the automatic acquisition of taxonomic relations. The main difference to earlier approaches is that we do not only consider one single source of evidence, i.e. a specific algorithm or approach, but examine the possibility of learning taxonomic relations by considering various and heterogeneous forms of evidence. In particular, we derive these different evidences by using well-known NLP techniques and resources and combine them via two simple strategies. Our...</description>
    <dc:title>Learning taxonomic relations from heterogeneous sources</dc:title>

    <dc:creator>P Cimiano</dc:creator>
    <dc:creator>A Pivk</dc:creator>
    <dc:creator>Schmidt Thieme</dc:creator>
    <dc:creator>S Staab</dc:creator>
    <dc:source>(2004)</dc:source>
    <dc:date>2007-08-20T23:35:01-00:00</dc:date>
    <prism:publicationYear>2004</prism:publicationYear>
    <prism:category>extraction</prism:category>
    <prism:category>relations</prism:category>
    <prism:category>taxonomic</prism:category>
    <prism:category>taxonomy</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/scis0000001/article/832747">
    <title>ExSel++: A General Framework to Extract Parametric Models</title>
    <link>http://www.citeulike.org/user/scis0000001/article/832747</link>
    <description>&lt;i&gt;(1995), pp. 90-97.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;We present a framework for accurate and robust extraction of parametric models of different types. It includes a mechanism that lets each model type determine its domain of applicability. The framework is general in the sense that it can be described and implemented without specifying the following components: a domain of application, a particular type of data, a set of admissible model types, and a specific fitting technique. It is a conceptually clean approach to model extraction, and...</description>
    <dc:title>ExSel++: A General Framework to Extract Parametric Models</dc:title>

    <dc:creator>Markus Stricker</dc:creator>
    <dc:creator>Ales Leonardis</dc:creator>
    <dc:source>(1995), pp. 90-97.</dc:source>
    <dc:date>2006-09-06T18:08:43-00:00</dc:date>
    <prism:publicationYear>1995</prism:publicationYear>
    <prism:startingPage>90</prism:startingPage>
    <prism:endingPage>97</prism:endingPage>
    <prism:category>extraction</prism:category>
    <prism:category>modeling</prism:category>
    <prism:category>parametric-model</prism:category>
    <prism:category>reconstruction</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/scis0000001/article/998719">
    <title>Searching for Services on the Semantic Web using Process Ontologies</title>
    <link>http://www.citeulike.org/user/scis0000001/article/998719</link>
    <description>&lt;i&gt;(2001)&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;. The ability to rapidly locate useful on-line services (e.g. software applications, software components, process models, or service organizations), as opposed to simply useful documents, is becoming increasingly critical in many domains. As the sheer number of such services increases it will become increasingly more important to provide tools that allow people (and software) to quickly find the services they need, while minimizing the burden for those who wish to list their services with ...</description>
    <dc:title>Searching for Services on the Semantic Web using Process Ontologies</dc:title>

    <dc:creator>M Klein</dc:creator>
    <dc:creator>A Bernstein</dc:creator>
    <dc:source>(2001)</dc:source>
    <dc:date>2006-12-17T16:57:04-00:00</dc:date>
    <prism:publicationYear>2001</prism:publicationYear>
    <prism:category>extraction</prism:category>
    <prism:category>functionality</prism:category>
    <prism:category>searching</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/scis0000001/article/933535">
    <title>Towards Large-Scale, Open-Domain and Ontology-Based Named Entity Classification</title>
    <link>http://www.citeulike.org/user/scis0000001/article/933535</link>
    <description>&lt;i&gt;&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;Named entity recognition and classification research has so far mainly focused on supervised techniques and has typically considered only small sets of classes with regard to which to classify the recognized entities.</description>
    <dc:title>Towards Large-Scale, Open-Domain and Ontology-Based Named Entity Classification</dc:title>

    <dc:creator>Philipp Cimiano</dc:creator>
    <dc:creator>Johanna Völker</dc:creator>
    <dc:date>2006-11-06T15:12:31-00:00</dc:date>
    <prism:category>extraction</prism:category>
    <prism:category>named-entity</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/scis0000001/article/1117482">
    <title>WordSieve: A Method for Real-Time Context Extraction</title>
    <link>http://www.citeulike.org/user/scis0000001/article/1117482</link>
    <description>&lt;i&gt;Lecture Notes in Computer Science, Vol. 2116 (2001), pp. 30-??.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;In order to be useful, intelligent information retrieval agents must provide their users with context-relevant information. This paper presents WordSieve, an algorithm for automatically extracting information about the context in which documents are consulted during web browsing. Using information extracted from the stream of documents consulted by the user, WordSieve automatically builds context profiles which differentiate sets of documents that users tend to access in groups. These...</description>
    <dc:title>WordSieve: A Method for Real-Time Context Extraction</dc:title>

    <dc:creator>Travis Bauer</dc:creator>
    <dc:creator>David Leake</dc:creator>
    <dc:source>Lecture Notes in Computer Science, Vol. 2116 (2001), pp. 30-??.</dc:source>
    <dc:date>2007-02-22T09:44:46-00:00</dc:date>
    <prism:publicationYear>2001</prism:publicationYear>
    <prism:publicationName>Lecture Notes in Computer Science</prism:publicationName>
    <prism:volume>2116</prism:volume>
    <prism:startingPage>30</prism:startingPage>
    <prism:endingPage>??</prism:endingPage>
    <prism:category>context</prism:category>
    <prism:category>extraction</prism:category>
    <prism:category>real-time</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/scis0000001/article/988006">
    <title>Kernel methods for relation extraction</title>
    <link>http://www.citeulike.org/user/scis0000001/article/988006</link>
    <description>&lt;i&gt;(2003)&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;We present an application of kernel methods to extracting relations from unstructured natural language sources.</description>
    <dc:title>Kernel methods for relation extraction</dc:title>

    <dc:creator>D Zelenko</dc:creator>
    <dc:creator>C Aone</dc:creator>
    <dc:creator>A Richardella</dc:creator>
    <dc:source>(2003)</dc:source>
    <dc:date>2006-12-10T14:40:47-00:00</dc:date>
    <prism:publicationYear>2003</prism:publicationYear>
    <prism:category>extraction</prism:category>
    <prism:category>kernel</prism:category>
    <prism:category>relations</prism:category>
</item>



</rdf:RDF>

