NSEEN: Neural Semantic Embedding for Entity Normalization

Much of human knowledge is encoded in the text, such as scientific publications, books, and the web. Given the rapid growth of these resources, we need automated methods to extract such knowledge into formal, machine-processable structures, such as knowledge graphs. An important task in this process is entity normalization (also called entity grounding, or resolution), which consists of mapping entity mentions in text to canonical entities in well-known reference sets. However, entity resolution is a challenging problem, since there often are many textual forms for a canonical entity. The problem is particularly acute in the scientific domain, such as biology. For example, a protein may have many different names and syntactic variations on these names. 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 mapping of new entities to large reference sets, and empirically show the effectiveness of our framework in challenging bio-entity normalization datasets.