Ontology-Aware Partitioning for Knowledge Graph Identification Jay - PowerPoint PPT Presentation

Ontology-Aware Partitioning for Knowledge Graph Identification Jay Pujara, Hui Miao, Lise Getoor, William Cohen Workshop on Automatic Knowledge Base Construction 10/27/2013

Knowledge Graph Ingredients: Entities Baltimore Orioles Giants Giants New York San Francisco Football Baseball Maryland New York California

Knowledge Graph Ingredients: Labels SportsTeam Baltimore Orioles City Giants Giants New York San Francisco Location Football Baseball Maryland State Sport New York California

Knowledge Graph Ingredients: Relations SportsTeam Baltimore Orioles City Giants Giants New York San Francisco Location Football Baseball Maryland State Sport New York California

Knowledge Graph Nuisance: Noise! SportsTeam Baltimore Orioles City Giants Giants New York San Francisco Location Football Baseball Maryland State Sport New York California

Knowledge Graph Ingredients: Ontology SportsTeam Baltimore Orioles City Giants Giants New York San Francisco Location Football Baseball Maryland State Sport New York California

Ontological rules for Knowledge Graphs Inverse: ˜ w O : Inv ( R, S ) ∧ Rel ( E 1 , E 2 , R ) ⇒ Rel ( E 2 , E 1 , S ) Selectional Preference: ˜ w O : Dom ( R, L ) ∧ Rel ( E 1 , E 2 , R ) ⇒ Lbl ( E 1 , L ) ˜ w O : Rng ( R, L ) ∧ Rel ( E 1 , E 2 , R ) ⇒ Lbl ( E 2 , L ) Subsumption: ˜ w O : Sub ( L, P ) ∧ Lbl ( E, L ) ⇒ Lbl ( E, P ) ˜ w O : RSub ( R, S ) ∧ Rel ( E 1 , E 2 , R ) ⇒ Rel ( E 1 , E 2 , S ) Mutual Exclusion: ˜ w O : Mut ( L 1 , L 2 ) ∧ Lbl ( E, L 1 ) ⇒ ˜ ¬ Lbl ( E, L 2 ) ˜ w O : RMut ( R, S ) ∧ Rel ( E 1 , E 2 , R ) ⇒ ˜ ¬ Rel ( E 1 , E 2 , S ) Adapted from Jiang et al., ICDM 2012

Knowledge Graph Identification (KGI) • Joint inference over possible knowledge graphs • Resolves co-referent entities • Removes spurious labels and relations • Infers missing labels and relations • Uses many uncertain sources • Enforces ontological constraints Pujara, Miao, Getoor, Cohen, "Knowledge Graph Identification" International Semantic Web Conference, 2013

KGI: Under the Hood • Define a probability distribution over knowledge graphs: P ( G | D ) = 1 $ & ∑ Z exp − w r ϕ r ( G ) % ' r ∈ R weighted potential Grounding of logical rule takes the form of a in model hinge-loss • Hinge-Loss Markov Random Fields • Templated: easy to define with probabilistic soft logic • Continuous: atoms have continuous truth values in [0,1] range • Efficient: inference via convex optimization in O(|R|) • Superior speed and quality for KGI (Pujara, ISWC13) Pujara, Miao, Getoor, Cohen, "Knowledge Graph Identification" International Semantic Web Conference, 2013