Enriching Documents with Compact, Representative, Relevant Knowledge Graphs
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 1748-1754. https://doi.org/10.24963/ijcai.2020/242
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.
Knowledge Representation and Reasoning: Semantic Web
Data Mining: Mining Graphs, Semi Structured Data, Complex Data