Abstract
Literature Knowledge Graphs play a critical role in helping domain experts carry out query resolution for finding relevant articles in published literature. Such knowledge graphs are usually in the form of Curated Document Databases (CDDs). Domain Experts and researchers typically query such literature knowledge graphs using some form of query-resolution mechanism. Machine learning techniques can be used to automate query-resolution. This paper presents a document query-resolution mechanism, given a query and set of documents in a knowledge graph, based on a hybrid word embedding that combines knowledge graph embeddings with “traditional” embeddings. A query-document data set extracted from a clinical trials CDD (the ORRCA CDD) was used. Three “traditional” word embeddings were considered: CBOW, BERT and SciBERT. The evaluation demonstrated that hybrid embeddings produced better results than when the embedding models were used in isolation. A best Mean Average Precision of 0.486 was obtained when using a CBOW and random walk knowledge graph hybrid embedding.
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Muhammad, I., Coenen, F., Gamble, C., Kearney, A., Williamson, P. (2022). Query Resolution of Literature Knowledge Graphs Using Hybrid Document Embeddings. In: Bramer, M., Stahl, F. (eds) Artificial Intelligence XXXIX. SGAI-AI 2022. Lecture Notes in Computer Science(), vol 13652. Springer, Cham. https://doi.org/10.1007/978-3-031-21441-7_7
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