A Versatile Hypergraph Model for Document Collections

Efficiently and effectively representing large collections of text is of central importance to information retrieval tasks such as summarization and search. Since models for these tasks frequently rely on an implicit graph structure of the documents or their contents, graph-based document representations are naturally appealing. For tasks that consider the joint occurrence of words or entities, however, existing document representations often fall short in capturing cooccurrences of higher order, higher multiplicity, or at varying proximity levels. Furthermore, while numerous applications benefit from structured knowledge sources, external data sources are rarely considered as integral parts of existing document models. To address these shortcomings, we introduce heterogeneous hypergraphs as a versatile model for representing annotated document collections. We integrate external metadata, document content, entity and term annotations, and document segmentation at different granularity levels in a joint model that bridges the gap between structured and unstructured data. We discuss selection and transformation operations on the set of hyperedges, which can be chained to support a wide range of query scenarios. To ensure compatibility with established information retrieval methods, we discuss projection operations that transform hyperedges to traditional dyadic cooccurrence graph representations. Using PostgreSQL and Neo4j, we investigate the suitability of existing database systems for implementing the hypergraph document model, and explore the impact of utilizing implicit and materialized hyperedge representations on storage space requirements and query performance.