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Exploring Summary-Expanded Entity Embeddings for Entity Retrieval

Entity retrieval is an important part of any modern retrieval system and often satisfies user information needs directly. Word and entity embeddings are a promising opportunity for new improvements in retrieval, especially in the presence of vocabulary mismatch problems. We present an approach to entity embedding that leverages the summary of entity articles from Wikipedia in order to form a richer representation of entities. We present a brief evaluation using the DBPedia-Entity-v2 dataset. Our evaluation shows that our new, summary-inspired representation provides improvements over both standard retrieval and pseudo-relevance feedback baselines as well as over a straightforward word-embedding model. We observe that this representation is particularly helpful for the verbose queries in the INEX-LD and QALD-2 subsets of our test collection.