Corpus-level Fine-grained Entity Typing Using Contextual Information
This work addresses knowledge base completion by improving entity typing accuracy, though it appears incremental as it builds on existing embedding and aggregation techniques.
The paper tackles corpus-level entity typing for knowledge base completion by proposing FIGMENT, an embedding-based method combining global and context models, which strongly outperforms an open information extraction approach in evaluation.
This paper addresses the problem of corpus-level entity typing, i.e., inferring from a large corpus that an entity is a member of a class such as "food" or "artist". The application of entity typing we are interested in is knowledge base completion, specifically, to learn which classes an entity is a member of. We propose FIGMENT to tackle this problem. FIGMENT is embedding-based and combines (i) a global model that scores based on aggregated contextual information of an entity and (ii) a context model that first scores the individual occurrences of an entity and then aggregates the scores. In our evaluation, FIGMENT strongly outperforms an approach to entity typing that relies on relations obtained by an open information extraction system.