Learning Co-reference Relations for FOAF Instances Jennifer Sleeman and Tim Finin, University of Maryland, Baltimore County Motivation 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 Smushing issues: Outdated information Conflicting information Other alignment-based issues owl:sameAs dangers Co-Referent Predicate : coref a owl:TransitiveProperty.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}. Methodology Results Future Work After co-reference is established among pairs we cluster our pairs and use these clusters for future co-reference evaluations. We use an ensemble approach with both the rules and a classifier to evaluate pairs. Predicting accurately co-referent/non-co-referent pairs Enhanced clustering algorithm Application to RDF documents non-FOAF specific 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. E2 # of PairsRuleRule Conclusion differentFromUndetermined 47184Inverse functionalUndetermined 2402Inverse functionalCo-referent Knows graphUndetermined sameAs Undetermined Knows graphNot Co-referent  1 st experiment resulting in 50,000 triples/500 entity mentions/600 training  2 nd experiment with 250,000 triples/3500 entity mentions/1800 training classes  10-fold validation with results shown in Table 2 Figure 2 & Figure 3 : Clustering Approaches Table 1: Rules-based Results ExperimentTP RateFP RatePrecisionRecallF-Measure E E Table 2: 10-Fold Cross Validation Test coreferent coref Two FOAF instances are determined to be co-referent. 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 3 joins cluster 1 and instance 1 and 2 have an explicit coref property that joins each with instance 3. 4 coreferent coref FOAF instance 1 and 2 are determined to be 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. Each instance adds an explicit coref property for each other. 34 coreferent FOAF instance 3 and 4 are determined to be co-referent. 2 coref 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 Figure 1: System Architecture Ingestion Candidate Pair Generation Candidate Pair Generation Rule-based Reasoning Rule-based Reasoning Machine Learning Machine Learning Model Generation Abstract entity generation Potential pairs reduces workload for classifier Deductive Decisions Deductive Decisions Predictions clusters form new abstract entities Co-referent designation and clustering 1. Generate candidate pairs 2. Generate a rules-based model 3. Perform classification using SVMs 4. Designate pairs as co-referent 5. Cluster pairs