Abstract
Semantic text mining is a challenging research topic in recent years. Many types of research focus on measuring the similarity of two documents with ontologies such as Medical Subject Headings (Mesh) and Gene Ontology (GO). However, most of the researches considered the single relationship in an ontology. To represent the document comprehensively, a semantic document similarity calculation method is proposed, based on utilizing Average Maximum Match algorithm with double-relations in GO. In the experiment, the results show that the double-relations based similarity calculation method is better than traditional semantic similarity measurements.
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This study was supported by the National Natural Science Foundation of China (61702324).
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Hu, J., Li, M., Zhang, Z., Li, K. (2020). An Efficient Semantic Document Similarity Calculation Method Based on Double-Relations in Gene Ontology. In: Pan, JS., Li, J., Tsai, PW., Jain, L. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 157. Springer, Singapore. https://doi.org/10.1007/978-981-13-9710-3_19
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DOI: https://doi.org/10.1007/978-981-13-9710-3_19
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