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

• URL: http://arxiv.org/abs/2306.16296v2
• Date: Wed, 16 Aug 2023 09:28:17 GMT
• Title: Relevant Entity Selection: Knowledge Graph Bootstrapping via Zero-Shot Analogical Pruning
• Authors: Lucas Jarnac, Miguel Couceiro, Pierre Monnin
• Abstract summary: 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.
• Score: 4.281723404774889
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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