Abstract
The hypergraph-of-entity is a joint representation model for terms, entities and their relations, used as an indexing approach in entity-oriented search. In this work, we characterize the structure of the hypergraph, from a microscopic and macroscopic scale, as well as over time with an increasing number of documents. We use a random walk based approach to estimate shortest distances and node sampling to estimate clustering coefficients. We also propose the calculation of a general mixed hypergraph density measure based on the corresponding bipartite mixed graph. We analyze these statistics for the hypergraph-of-entity, finding that hyperedge-based node degrees are distributed as a power law, while node-based node degrees and hyperedge cardinalities are log-normally distributed. We also find that most statistics tend to converge after an initial period of accentuated growth in the number of documents. We then repeat the analysis over three extensions—materialized through synonym, context, and tf_bin hyperedges—in order to assess their structural impact in the hypergraph. Finally, we focus on the application-specific aspects of the hypergraph-of-entity, in the domain of information retrieval. We analyze the correlation between the retrieval effectiveness and the structural features of the representation model, proposing ranking and anomaly indicators, as useful guides for modifying or extending the hypergraph-of-entity.
Mario Gionfriddo and mixed hypergraph coloring
