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Communication Dans Un Congrès Année : 2023

Relevant Entity Selection: Knowledge Graph Bootstrapping via Zero-Shot Analogical Pruning

Miguel Couceiro
Pierre Monnin

Résumé

Knowledge Graph Construction (KGC) can be seen as an iterative process starting from a high quality nucleus that is refined by knowledge extraction approaches in a virtuous loop. Such a nucleus can be obtained from knowledge existing in an open KG like Wikidata. However, due to the size of such generic KGs, integrating them as a whole may entail irrelevant content and scalability issues. We propose an analogy-based approach that starts from seed entities of interest in a generic KG, and keeps or prunes their neighboring entities. We evaluate our approach on Wikidata through two manually labeled datasets that contain either domain-homogeneous or -heterogeneous seed entities. We empirically show that our analogy-based approach outperforms LSTM, Random Forest, SVM, and MLP, with a drastically lower number of parameters. We also evaluate its generalization potential in a transfer learning setting. These results advocate for the further integration of analogy-based inference in tasks related to the KG lifecycle.
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Dates et versions

hal-04254979 , version 1 (23-10-2023)

Identifiants

Citer

Lucas Jarnac, Miguel Couceiro, Pierre Monnin. Relevant Entity Selection: Knowledge Graph Bootstrapping via Zero-Shot Analogical Pruning. CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management, Oct 2023, Birmingham (UK), United Kingdom. pp.934-944, ⟨10.1145/3583780.3615030⟩. ⟨hal-04254979⟩
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