Abstract
During the last decade, the need for reliable and massive Knowledge Graphs (KG) increased. KGs can be created in several ways: manually with forms or automatically with Information Extraction (IE), a natural language processing task for extracting knowledge from text. Relation Extraction is the part of IE that focuses on identifying relations between named entities in texts, which amounts to find new edges in a KG. Most recent approaches rely on deep learning, achieving state-of-the-art performances. However, those performances are still too low to fully automatize the construction of reliable KGs, and human interaction remains necessary. This is made difficult by the statistical nature of deep learning methods that makes their predictions hardly interpretable. In this paper, we present a new symbolic and interpretable approach for Relation Extraction in texts. It is based on a modeling of the lexical and syntactic structure of text as a knowledge graph, and it exploits Concepts of Neighbours, a method based on Graph-FCA for computing similarities in knowledge graphs. An evaluation has been performed on a subset of TACRED (a relation extraction benchmark), showing promising results.
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We use the CoreNLP tool [12] but other tools could be used.
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We use the 58 POS tags of English Penn Treebank [16].
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We use the dependency grammar proposed by Treebank Universal Dependencies.
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Code available at https://gitlab.inria.fr/hayats/conceptualknn-relex.
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Ayats, H., Cellier, P., Ferré, S. (2021). Extracting Relations in Texts with Concepts of Neighbours. In: Braud, A., Buzmakov, A., Hanika, T., Le Ber, F. (eds) Formal Concept Analysis. ICFCA 2021. Lecture Notes in Computer Science(), vol 12733. Springer, Cham. https://doi.org/10.1007/978-3-030-77867-5_10
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