Hypergraph-of-entity A unified representation model for the retrieval of text and knowledge

• Type: Article in International Scientific Journal
• Date: 2019

Authors

José Devezas (Author) Other; Sérgio Nunes (Author) FEUP

Journal

Title: Open Computer Science

Vol. 9

Pages: 103-127

Publisher: Walter De Gruyter

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 4 Citations

Other information

Authenticus ID: P-00Q-SGY

DOI: 10.1515/comp-2019-0006

Abstract (EN): Modern search is heavily powered by knowledge bases, but users still query using keywords or natural language. As search becomes increasingly dependent on the integration of text and knowledge, novel approaches for a unified representation of combined data present the opportunity to unlock new ranking strategies. We have previously proposed the graph-of-entity as a purely graph-based representation and retrieval model, however this model would scale poorly. We tackle the scalability issue by adapting the model so that it can be represented as a hypergraph. This enables a significant reduction of the number of (hyper)edges, in regard to the number of nodes, while nearly capturing the same amount of information. Moreover, such a higher-order data structure, presents the ability to capture richer types of relations, including nary connections such as synonymy, or subsumption. We present the hypergraph-of-entity as the next step in the graph-of-entity model, where we explore a ranking approach based on biased random walks. We evaluate the approaches using a subset of the INEX 2009 Wikipedia Collection. While performance is still below the state of the art, we were, in part, able to achieve a MAP score similar to TF-IDF and greatly improve indexing efficiency over the graph-of-entity.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 25