Publication Type

Journal Article

Version

publishedVersion

Publication Date

1-2023

Abstract

Contextual path retrieval (CPR) refers to the task of finding contextual path(s) between a pair of entities in a knowledge graph that explains the connection between them in a given context. For this novel retrieval task, we propose the Embedding-based Contextual Path Retrieval (ECPR) framework. ECPR is based on a three-component structure that includes a context encoder and path encoder that encode query context and path, respectively, and a path ranker that assigns a ranking score to each candidate path to determine the one that should be the contextual path. For context encoding, we propose two novel context encoding methods, i.e., context-fused entity embeddings and contextualized embeddings. For path encoding, we propose PathVAE, an inductive embedding approach to generate path representations. Finally, we explore two path-ranking approaches. In our evaluation, we construct a synthetic dataset from Wikipedia and two real datasets of Wikinews articles constructed through crowdsourcing. Our experiments show that methods based on ECPR framework outperform baseline methods, and that our two proposed context encoders yield significantly better performance than baselines. We also analyze a few case studies to show the distinct features of ECPR-based methods.

Keywords

Knowledge base, embedding learning, information retrieval, reasoning

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

ACM Transactions on Information Systems

Volume

41

Issue

1

First Page

1

Last Page

38

ISSN

1046-8188

Identifier

10.1145/3502720

Publisher

Association for Computing Machinery (ACM)

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3502720

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