Abstract
An open knowledge base (OKB) is a repository of facts, which are typically represented in the form of \(\langle \)subject; relation; object\(\rangle \) triples. The problem of canonicalizing OKB triples is to map different names mentioned in the triples that refer to the same entity into a basic canonical form. We propose the algorithm Multi-Level Canonicalization with Embeddings (MULCE) to perform canonicalization. MULCE executes in two steps. The first step performs word-level canonicalization to coarsely group subject names based on their GloVe vectors into semantically similar clusters. The second step performs sentence-level canonicalization to refine the clusters by employing BERT embedding to model relation and object information. Our experimental results show that MULCE outperforms state-of-the-art methods.
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Notes
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Using BERT-Base Uncased Model: https://github.com/google-research/bert.
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Wu, TH., Kao, B., Wu, Z., Feng, X., Song, Q., Chen, C. (2020). MULCE: Multi-level Canonicalization with Embeddings of Open Knowledge Bases. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2020. WISE 2020. Lecture Notes in Computer Science(), vol 12342. Springer, Cham. https://doi.org/10.1007/978-3-030-62005-9_23
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