Abstract
The semantic similarities among concepts play an important role in many tasks. Ontology represents the semantic relationship among concepts. Traditional methods use the path-length between concepts in the ontology to calculate their semantic similarity. However, this simple method cannot present semantic relationship among concepts well. This study seeks to learn the concept embeddings in ontology, and then use the cosine similarity of two embeddings to inform their sematic similarity. We developed a framework, called concept2vec, to perform the task. The experimental results demonstrate that our work is effective on learning representation of concepts in ontology.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (71571145).
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Qiu, J., Wang, S. (2020). Learning the Concept Embeddings of Ontology. In: Yang, X., Wang, CD., Islam, M.S., Zhang, Z. (eds) Advanced Data Mining and Applications. ADMA 2020. Lecture Notes in Computer Science(), vol 12447. Springer, Cham. https://doi.org/10.1007/978-3-030-65390-3_10
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DOI: https://doi.org/10.1007/978-3-030-65390-3_10
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