Enriching Documents with Compact, Representative, Relevant Knowledge Graphs

TL;DR

This paper introduces a novel method for enriching documents with compact, relevant knowledge graphs by computing entity relation subgraphs using an efficient search algorithm, improving expressiveness and relevance.

Contribution

It proposes a new approach to generate compact, representative, and relevant ERGs for document enrichment, combining best-first search and ontological relevance ranking.

Findings

The method outperforms existing approaches in expressiveness and relevance.

Extensive experiments demonstrate the effectiveness of the proposed ERG enrichment.

User studies confirm improved document understanding with the approach.

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.