Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/8579
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dc.contributor.authorChatzis, Sotirios P.-
dc.contributor.otherΧατζής, Σωτήριος Π.-
dc.date.accessioned2016-07-01T09:48:38Z-
dc.date.available2016-07-01T09:48:38Z-
dc.date.issued2015-10-
dc.identifier.citationProceedings of the 24th ACM International on Conference on Information and Knowledge Management, 2015, Melbourne, VIC, Australia, pp. 1311-1320en_US
dc.identifier.isbn978-1-4503-3794-6-
dc.identifier.urihttp://ktisis.cut.ac.cy/handle/10488/8579-
dc.description.abstractIn this paper, we focus on the problem of extending a given knowledge base by accurately predicting additional true facts based on the facts included in it. This is an essential problem of knowledge representation systems, since knowledge bases typically suffer from incompleteness and lack of ability to reason over their discrete entities and relationships. To achieve our goals, in our work we introduce an inducing space nonparametric Bayesian large-margin inference model, capable of reasoning over relationships between pairs of entities. Previous works addressing the entity relationship inference problem model each entity based on atomic entity vector representations. In contrast, our method exploits word feature vectors to directly obtain high-dimensional nonlinear inducing space representations for entity pairs. This way, we allow for extracting salient latent characteristics and interaction dynamics within entity pairs that can be useful for inferring their relationships. On this basis, our model performs the relations inference task by postulating a set of binary Dirichlet process mixture large-margin classifiers, presented with the derived inducing space representations of the considered entity pairs. Bayesian inference for this inducing space model is performed under the mean-field inference paradigm. This is made possible by leveraging a recently proposed latent variable formulation of regularized large-margin classifiers that facilitates mean-field parameter estimation. We exhibit the superiority of our approach over the state-of-the-art by considering the problem of predicting additional true relations between entities given subsets of the WordNet and FreeBase knowledge bases.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.subjectComputing methodologiesen_US
dc.subjectMathematics of computingen_US
dc.subjectMachine learningen_US
dc.subjectProbability and statisticsen_US
dc.subjectMachine learning approachesen_US
dc.subjectNonparametric statisticsen_US
dc.subjectLogical and relational learningen_US
dc.subjectInductive logic learningen_US
dc.titleInducing-Space Dirichlet Process Mixture Large-Margin Entity Relationship Inference in Knowledge Basesen_US
dc.typeConference Papersen_US
dc.affiliationCyprus University of Technology-
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.relation.conferenceACM International on Conference on Information and Knowledge Managementen_US
dc.identifier.doi10.1145/2806416.2806499en_US
dc.dept.handle123456789/134en
cut.common.academicyear2015-2016en_US
item.languageiso639-1other-
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-4956-4013-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers
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