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 mapping 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 …

Metadata

publication

Machine Learning and Knowledge Discovery in Databases: European Conference …, 2020

year

2020

publication date

2020

authors

Shobeir Fakhraei, Joel Mathew, José Luis Ambite

link

https://link.springer.com/chapter/10.1007/978-3-030-46147-8_40

resource_link

https://arxiv.org/pdf/1811.07514

conference

Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part II

pages

665-680

publisher

Springer International Publishing