Measuring inconsistency in knowledgebases Export

@article{citeulike:1029861, abstract = {It is well-known that knowledgebases may contain inconsistencies. We provide a measure to quantify the inconsistency of a knowledgebase, thereby allowing for the comparison of the inconsistency of various knowledgebases, represented as first-order logic formulas. We use quasi-classical (QC) logic for this purpose. QC logic is a formalism for reasoning and analysing inconsistent information. It has been used as the basis of a framework for measuring inconsistency in propositional theories. Here we extend this framework, by using a first-order logic version of QC logic for measuring inconsistency in first-order theories. We motivate the QC logic approach by considering some formulae as database or knowledgebase integrity constraints. We then define a measure of extrinsic inconsistency that can be used to compare the inconsistency of different knowledgebases. This measure takes into account both the language used and the underlying domain. We show why this definition also captures the intrinsic inconsistency of a knowledgebase. We also provide a formalization of paraconsistent equality, called quasi-equality, and we use this in an extended example of an application for measuring inconsistency between heterogeneous sources of information and integrity constraints prior to merging.}, address = {Hingham, MA, USA}, author = {Grant, John and Hunter, Anthony}, citeulike-article-id = {1029861}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1164503}, citeulike-linkout-1 = {http://dx.doi.org/10.1007/s10844-006-2974-4}, citeulike-linkout-2 = {http://www.ingentaconnect.com/content/klu/jiis/2006/00000027/00000002/00002974}, doi = {10.1007/s10844-006-2974-4}, issn = {0925-9902}, journal = {J. Intell. Inf. Syst.}, keywords = {inconsistency, measuring}, month = {September}, number = {2}, pages = {159--184}, posted-at = {2009-11-11 14:54:33}, priority = {2}, publisher = {Kluwer Academic Publishers}, title = {Measuring inconsistency in knowledgebases}, url = {http://dx.doi.org/10.1007/s10844-006-2974-4}, volume = {27}, year = {2006} }

TY - JOUR ID - citeulike:1029861 L3 - citeulike-article-id:1029861 N2 - It is well-known that knowledgebases may contain inconsistencies. We provide a measure to quantify the inconsistency of a knowledgebase, thereby allowing for the comparison of the inconsistency of various knowledgebases, represented as first-order logic formulas. We use quasi-classical (QC) logic for this purpose. QC logic is a formalism for reasoning and analysing inconsistent information. It has been used as the basis of a framework for measuring inconsistency in propositional theories. Here we extend this framework, by using a first-order logic version of QC logic for measuring inconsistency in first-order theories. We motivate the QC logic approach by considering some formulae as database or knowledgebase integrity constraints. We then define a measure of extrinsic inconsistency that can be used to compare the inconsistency of different knowledgebases. This measure takes into account both the language used and the underlying domain. We show why this definition also captures the intrinsic inconsistency of a knowledgebase. We also provide a formalization of paraconsistent equality, called quasi-equality, and we use this in an extended example of an application for measuring inconsistency between heterogeneous sources of information and integrity constraints prior to merging. IS - 2 JF - J. Intell. Inf. Syst. SN - 0925-9902 AD - Hingham, MA, USA EP - 184 TI - Measuring inconsistency in knowledgebases PB - Kluwer Academic Publishers VL - 27 SP - 159 KW - inconsistency KW - measuring AU - Grant, John AU - Hunter, Anthony PY - 2006/09// UR - http://dx.doi.org/10.1007/s10844-006-2974-4 ER -