Enriching Documents with Compact, Representative, Relevant Knowledge Graphs

• URL: http://arxiv.org/abs/2005.00153v2
• Date: Sun, 10 May 2020 08:29:28 GMT
• Title: Enriching Documents with Compact, Representative, Relevant Knowledge Graphs
• Authors: Shuxin Li, Zixian Huang, Gong Cheng, Evgeny Kharlamov, Kalpa Gunaratna
• Abstract summary: A prominent application of knowledge graph (KG) is document enrichment. Existing methods identify mentions of entities in a background KG and enrich documents with entity types and direct relations. We compute an entity relation subgraph (ERG) that can more expressively represent indirect relations among a set of mentioned entities.
• Score: 17.884816859418013
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A prominent application of knowledge graph (KG) is document enrichment. Existing methods identify mentions of entities in a background KG and enrich documents with entity types and direct relations. We compute an entity relation subgraph (ERG) that can more expressively represent indirect relations among a set of mentioned entities. To find compact, representative, and relevant ERGs for effective enrichment, we propose an efficient best-first search algorithm to solve a new combinatorial optimization problem that achieves a trade-off between representativeness and compactness, and then we exploit ontological knowledge to rank ERGs by entity-based document-KG and intra-KG relevance. Extensive experiments and user studies show the promising performance of our approach.

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