Ontology-based semantic data interestingness using BERT models
The COVID-19 pandemic has generated massive data in the healthcare sector in recent years, encouraging researchers and scientists to uncover the underlying facts. Mining interesting patterns in the large COVID-19 corpora is very important and useful for the decision makers. This paper presents a nov...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2023.2190499 |
| _version_ | 1797684003624452096 |
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| author | Abhilash CB Kavi Mahesh Nihar Sanda |
| author_facet | Abhilash CB Kavi Mahesh Nihar Sanda |
| author_sort | Abhilash CB |
| collection | DOAJ |
| description | The COVID-19 pandemic has generated massive data in the healthcare sector in recent years, encouraging researchers and scientists to uncover the underlying facts. Mining interesting patterns in the large COVID-19 corpora is very important and useful for the decision makers. This paper presents a novel approach for uncovering interesting insights in large datasets using ontologies and BERT models. The research proposes a framework for extracting semantically rich facts from data by incorporating domain knowledge into the data mining process through the use of ontologies. An improved Apriori algorithm is employed for mining semantic association rules, while the interestingness of the rules is evaluated using BERT models for semantic richness. The results of the proposed framework are compared with state-of-the-art methods and evaluated using a combination of domain expert evaluation and statistical significance testing. The study offers a promising solution for finding meaningful relationships and facts in large datasets, particularly in the healthcare sector. |
| first_indexed | 2024-03-12T00:24:07Z |
| format | Article |
| id | doaj.art-755378fdd4c040a795e2885c3eaf8dab |
| institution | Directory Open Access Journal |
| issn | 0954-0091 1360-0494 |
| language | English |
| last_indexed | 2024-03-12T00:24:07Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj.art-755378fdd4c040a795e2885c3eaf8dab2023-09-15T10:48:01ZengTaylor & Francis GroupConnection Science0954-00911360-04942023-12-0135110.1080/09540091.2023.21904992190499Ontology-based semantic data interestingness using BERT modelsAbhilash CB0Kavi Mahesh1Nihar Sanda2Indian Institute of Information Technology Dharwad, IIIT DharwadIndian Institute of Information Technology Dharwad, IIIT DharwadIndian Institute of Information Technology Dharwad, IIIT DharwadThe COVID-19 pandemic has generated massive data in the healthcare sector in recent years, encouraging researchers and scientists to uncover the underlying facts. Mining interesting patterns in the large COVID-19 corpora is very important and useful for the decision makers. This paper presents a novel approach for uncovering interesting insights in large datasets using ontologies and BERT models. The research proposes a framework for extracting semantically rich facts from data by incorporating domain knowledge into the data mining process through the use of ontologies. An improved Apriori algorithm is employed for mining semantic association rules, while the interestingness of the rules is evaluated using BERT models for semantic richness. The results of the proposed framework are compared with state-of-the-art methods and evaluated using a combination of domain expert evaluation and statistical significance testing. The study offers a promising solution for finding meaningful relationships and facts in large datasets, particularly in the healthcare sector.http://dx.doi.org/10.1080/09540091.2023.2190499semantic data miningsemantic interestingnessassociation rule miningontology-methodsbert models |
| spellingShingle | Abhilash CB Kavi Mahesh Nihar Sanda Ontology-based semantic data interestingness using BERT models Connection Science semantic data mining semantic interestingness association rule mining ontology-methods bert models |
| title | Ontology-based semantic data interestingness using BERT models |
| title_full | Ontology-based semantic data interestingness using BERT models |
| title_fullStr | Ontology-based semantic data interestingness using BERT models |
| title_full_unstemmed | Ontology-based semantic data interestingness using BERT models |
| title_short | Ontology-based semantic data interestingness using BERT models |
| title_sort | ontology based semantic data interestingness using bert models |
| topic | semantic data mining semantic interestingness association rule mining ontology-methods bert models |
| url | http://dx.doi.org/10.1080/09540091.2023.2190499 |
| work_keys_str_mv | AT abhilashcb ontologybasedsemanticdatainterestingnessusingbertmodels AT kavimahesh ontologybasedsemanticdatainterestingnessusingbertmodels AT niharsanda ontologybasedsemanticdatainterestingnessusingbertmodels |