Contextual Path Retrieval: A Contextual Entity Relation Embedding-based Approach

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 threecomponent 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.

Automated summary

Contextual path retrieval (CPR) is to find contextual path(s) between a pair of entities in a knowledge graph, explaining the connection between them in a given context. We propose Embedding-based Contextual Path Retrieval (ECPR) framework, which includes a context encoder, a path encoder, and a path ranker. Our experiments on synthetic and real datasets demonstrate superior performance of ECPR-based methods over baselines, particularly with our proposed context encoders.