.png){kind=icon}
Return
Contextual Path Retrieval: A Contextual Entity Relation Embedding-based Approach
.png){kind=icon}
OA
AI
DOI：10.1145/3502720.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Knowledge base
reasoning
information retrieval
embedding learning
Journal
IF:
9.1
Papers:
1.2K
Citations:
4.7K
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveShareSave no more
no more