Link prediction in relational data Export

@misc{taskar03linkprediction, abstract = {Many real-world domains are relational in nature, consisting of a set of objects related to each other in complex ways. This paper focuses on predicting the existence and the type of links between entities in such domains. We apply the relational Markov network framework of Taskar et al. to define a joint probabilistic model over the entire link graph --- entity attributes and links. The application of the RMN algorithm to this task requires the definition of probabilistic patterns over...}, author = {Taskar, B. and Wong, M. and Abbeel, P. and Koller, D.}, citeulike-article-id = {975425}, citeulike-linkout-0 = {http://citeseer.ist.psu.edu/630066.html}, keywords = {graph, link-prediction}, posted-at = {2006-12-05 16:15:27}, priority = {2}, title = {Link prediction in relational data}, url = {http://citeseer.ist.psu.edu/630066.html}, year = {2004} }

TY - GEN ID - taskar03linkprediction L3 - citeulike-article-id:975425 N2 - Many real-world domains are relational in nature, consisting of a set of objects related to each other in complex ways. This paper focuses on predicting the existence and the type of links between entities in such domains. We apply the relational Markov network framework of Taskar et al. to define a joint probabilistic model over the entire link graph --- entity attributes and links. The application of the RMN algorithm to this task requires the definition of probabilistic patterns over... TI - Link prediction in relational data KW - graph KW - link-prediction AU - Taskar, B AU - Wong, M AU - Abbeel, P AU - Koller, D PY - 2004/// UR - http://citeseer.ist.psu.edu/630066.html ER -