Abstract
Representation learning of medical knowledge graphs aims to embedding entities and relations in low-dimensional vector spaces, which is beneficial to the application of medical knowledge graphs in intelligent medical systems such as intelligent guidance, disease risk prediction and question answering system of medical field. Recently, some translation-based methods including TransE, TransH and TransR built entity and relation embeddings by regarding a relation as translation from head entity to tail entity. These methods solely use the information of triplets and don’t take text information into consideration. In this paper, we process a novel representation learning method by incorporating the embeddings of entity descriptions with classical translation-based methods. The embeddings of entity descriptions are built by Doc2Vec. It is easily applied for a large-scale domain-specific knowledge graphs because of its simplicity. Besides, we compare our method with classical translation-based methods to demonstrate the effectiveness of our method in medical knowledge graphs representation learning.
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Acknowledgement
This work is supported by the National Key R&D Program of China (2018 YFB1201500), the National Natural Science Foundation of China under (Grant No. 61471055), Beijing Major Science and Technology Special Projects under Grant No. Z181100003118012 and China Railway with project No. BX37.
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Sun, X., Man, Y., Zhao, Y., He, J., Liu, N. (2019). Incorporating Description Embeddings into Medical Knowledge Graphs Representation Learning. In: Tang, Y., Zu, Q., Rodríguez García, J. (eds) Human Centered Computing. HCC 2018. Lecture Notes in Computer Science(), vol 11354. Springer, Cham. https://doi.org/10.1007/978-3-030-15127-0_19
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DOI: https://doi.org/10.1007/978-3-030-15127-0_19
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