Enriching Documents with Compact, Representative, Relevant Knowledge Graphs
This work addresses document enrichment for applications needing more expressive knowledge representation, though it is incremental as it builds on existing entity mention methods.
The paper tackled the problem of document enrichment by computing entity relation subgraphs (ERGs) that capture indirect relations among mentioned entities, achieving promising performance in experiments and user studies.
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.