Ontology-Based Semantic Similarity Approach for Biomedical Dataset Retrieval. Wang, X., Huang, Z., & van Harmelen, F. In Huang, Z., Siuly, S., Wang, H., Zhou, R., & Zhang, Y., editors, Health Information Science, of Lecture Notes in Computer Science, pages 49–60. Springer International Publishing. doi abstract bibtex Ontology-based semantic similarity approaches play an important role in text-similarity task, thanks to its ability of explanation. Ontology-based semantic similarity approaches can explain how two terms are similar with help of rich knowledge in ontology. Information retrieval aims to find relevant information for given user query. As a subareas of information retrieval, dataset retrieval is an activity to find dataset which are relevant to an information need, by using full-text indexing approach or content-based indexing approach. Ontology-based semantic similarity approaches can not only do some information retrieval tasks, such as full-text mapping, but also finding deeper similar information with the help of knowledge-richness in ontology. Because of the advantage of ontology-based similarity approaches, we are looking forwards to find the possibility to using ontology-based similarity for datasets retrieval. In this paper, we provide an ontology-based similarity approach for dataset retrieval. We run our novel approach on the bioCADDIE 2016 Dataset Retrieval Challenge. After ruining experiments, we evaluate our results with several information retrieval evaluation measures. The evaluation results show that our approach could perform well.
@inproceedings{wang_ontology-based_2020,
location = {Cham},
title = {Ontology-Based Semantic Similarity Approach for Biomedical Dataset Retrieval},
isbn = {978-3-030-61951-0},
doi = {10.1007/978-3-030-61951-0_5},
series = {Lecture Notes in Computer Science},
abstract = {Ontology-based semantic similarity approaches play an important role in text-similarity task, thanks to its ability of explanation. Ontology-based semantic similarity approaches can explain how two terms are similar with help of rich knowledge in ontology. Information retrieval aims to find relevant information for given user query. As a subareas of information retrieval, dataset retrieval is an activity to find dataset which are relevant to an information need, by using full-text indexing approach or content-based indexing approach. Ontology-based semantic similarity approaches can not only do some information retrieval tasks, such as full-text mapping, but also finding deeper similar information with the help of knowledge-richness in ontology. Because of the advantage of ontology-based similarity approaches, we are looking forwards to find the possibility to using ontology-based similarity for datasets retrieval. In this paper, we provide an ontology-based similarity approach for dataset retrieval. We run our novel approach on the {bioCADDIE} 2016 Dataset Retrieval Challenge. After ruining experiments, we evaluate our results with several information retrieval evaluation measures. The evaluation results show that our approach could perform well.},
pages = {49--60},
booktitle = {Health Information Science},
publisher = {Springer International Publishing},
author = {Wang, Xu and Huang, Zhisheng and van Harmelen, Frank},
editor = {Huang, Zhisheng and Siuly, Siuly and Wang, Hua and Zhou, Rui and Zhang, Yanchun},
date = {2020},
langid = {english},
keywords = {Biomedical dataset, Dataset retrieval, Semantic similarity},
}
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