Abstract
Much of human knowledge is encoded in text, available in scientific publications, books, and the web. Given the rapid growth of these resources, we need automated methods to extract such knowledge into machine-processable structures, such as knowledge graphs. An important task in this process is entity normalization, which consists of map** noisy entity mentions in text to canonical entities in well-known reference sets. However, entity normalization is a challenging problem; there often are many textual forms for a canonical entity that may not be captured in the reference set, and entities mentioned in text may include many syntactic variations, or errors. The problem is particularly acute in scientific domains, such as biology. To address this problem, we have developed a general, scalable solution based on a deep Siamese neural network model to embed the semantic information about the entities, as well as their syntactic variations. We use these embeddings for fast map** of new entities to large reference sets, and empirically show the effectiveness of our framework in challenging bio-entity normalization datasets.
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Notes
- 1.
For brevity of notation we denote \(\delta (v_i,v_j)\) with \(\delta _v\).
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This work was supported in part by DARPA Big Mechanism program under contract number W911NF-14-1-0364.
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Fakhraei, S., Mathew, J., Ambite, J.L. (2020). NSEEN: Neural Semantic Embedding for Entity Normalization. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Lecture Notes in Computer Science(), vol 11907. Springer, Cham. https://doi.org/10.1007/978-3-030-46147-8_40
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