The reconciled knowledge graphs are typically used for multidocument summarization, or to detect knowledge evolution across document series. This paper focuses on reconciling knowledge graphs generated from two text documents about similar events described differently. Our approach employs and extends MERGILO, a tool for reconciling knowledge graphs extracted from text, using word similarity and graph alignment. Complete semantic representation of events are generated using FRED, a semantic web machine reader, jointly with Framester, a linguistic linked data hub represented using a novel formal semantics for frames. Event-reconciliation is mainly performed via similarities based on the graph structure of frames using RDF2Vec graph embeddings, and the subsumption hierarchy of semantic roles as defined in Framester. Our approach is evaluated over a coreference resolution task.
Reconciling event-based knowledge through RDF2Vec
Mongiovi Misael;
2017-01-01
Abstract
The reconciled knowledge graphs are typically used for multidocument summarization, or to detect knowledge evolution across document series. This paper focuses on reconciling knowledge graphs generated from two text documents about similar events described differently. Our approach employs and extends MERGILO, a tool for reconciling knowledge graphs extracted from text, using word similarity and graph alignment. Complete semantic representation of events are generated using FRED, a semantic web machine reader, jointly with Framester, a linguistic linked data hub represented using a novel formal semantics for frames. Event-reconciliation is mainly performed via similarities based on the graph structure of frames using RDF2Vec graph embeddings, and the subsumption hierarchy of semantic roles as defined in Framester. Our approach is evaluated over a coreference resolution task.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
