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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