Learning Co-reference Relations for FOAF Instances

1. Learning Co-reference Relations for FOAF Instances Jennifer Sleeman and Tim Finin, University of Maryland, Baltimore County Motivation Methodology Results • 1st experiment resulting in 50,000 triples/500 entity mentions/600 training • 2nd experiment with 250,000 triples/3500 entity mentions/1800 training classes • 10-fold validation with results shown in Table 2 We use an ensemble approach with both the rules and a classifier to evaluate pairs. • Establishing co-reference relations for entities is a common problem. Our goal is to establish co-reference relations among FOAF agents. • FOAF co-referent issues: • No global unique identifiers • Inverse Functional Properties not always reliable • Multiple versions of FOAF files for a single entity When two instances are thought to be co-referent, information can be combined providing a more complete representation of the entity. In Semantic Web this is termed as 'smushing'. Smushing issues: • Outdated information • Conflicting information • Other alignment-based issues • owl:sameAs dangers 1. Generate candidate pairs 2. Generate a rules-based model 3. Perform classification using SVMs 4. Designate pairs as co-referent 5. Cluster pairs Ingestion Abstract entity generation Candidate Pair Generation Table 1: Rules-based Results Potential pairs reduces workload for classifier Model Generation • For experiment one: • 900 pairs designated non-match • majority other rules returned undetermined state For experiment two we show in Table 1: • only inverse functional property rule positive cases • majority resulted in undetermined state • knows rule resulted in non-coreferent state During E2 clustering, first phase resulted in 90% accuracy. Errors occurred in pairs that should have been clustered but were not. A second round of clustering yielded no new relationship pairs among instances but cluster to cluster pairing did occur. Rule-based Reasoning Machine Learning clusters formnew abstract entities Figure 1: System Architecture Deductive Decisions Predictions Co-referent designation and clustering After co-reference is established among pairs we cluster our pairs and use these clusters for future co-reference evaluations. Co-Referent Predicate • The following axioms in N3 are for the coref and notCoref properties. • coref – transitive and symmetric, owl:sameAs as a sub-property • notCoref – symmetric but not transitive, owl:differentFrom as sub-property coreferent coreferent 1 2 3 4 coreferent 1 2 FOAF instance 1 and 2 are determined to be co-referent. FOAF instance 3 and 4 are determined to be co-referent. Two FOAF instances are determined to be co-referent. 1 coreferent 3 3 coreferent coref 1 coref 2 coref 4 2 :coref a owl:TransitiveProperty. :coref a owl:SymmetricProperty. owl:sameAs rdfs:subPropertyOf :coref. :notCoref a owl:SymmetricProperty. owl:differentFrom rdfs:subPropertyOf :notCoref. {?a :notCoref ?b. ?b :coref ?c.} => {?a :notCoref ?c}. {?a foaf:knows ?b.} => {?a :notCoref ?b}. Instance 1 and 2 add an explicit coref property for each other and form cluster 1. It is determined that cluster 1 and FOAF instance 3 are co-referent. Instance 1 and 2 add an explicit coref property for each other and form cluster 1. Instance 3 and 4 add an explicit coref property for each other and form cluster 2. It is determined that cluster 1 and cluster 2 are co-referent. Table 2: 10-Fold Cross Validation Test 1 2 coref 2 coref Future Work coref 3 1 coref coref coref coref 3 coref 4 Instance 3 joins cluster 1 and instance 1 and 2 have an explicit coref property that joins each with instance 3. • Predicting accurately co-referent/non-co-referent pairs • Enhanced clustering algorithm • Application to RDF documents non-FOAF specific coref Each instance adds an explicit coref property for each other. Figure 2 & Figure 3 : Clustering Approaches