Rule extraction from scientific texts: Evaluation in the specialty of gynecology
Due to the considerable increase in freely available data (especially on the Web), extracting relevant information from textual content is a critical challenge. Most of the available data is embedded in unstructured texts and is not linked to formalized knowledge structures such as ontologies or rul...
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| Format: | Article |
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Springer
2022-04-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157820303736 |
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| author | Amina Boufrida Zizette Boufaida |
| author_facet | Amina Boufrida Zizette Boufaida |
| author_sort | Amina Boufrida |
| collection | DOAJ |
| description | Due to the considerable increase in freely available data (especially on the Web), extracting relevant information from textual content is a critical challenge. Most of the available data is embedded in unstructured texts and is not linked to formalized knowledge structures such as ontologies or rules. A potential solution to this problem is to acquire such knowledge through natural language processing (NLP) tools and text mining techniques. Prior work has focused on the automatic extraction of ontologies from texts, but the acquired knowledge is generally limited to simple hierarchies of terms. This paper presents a polyvalent framework for acquiring complex relationships from texts and coding these in the form of rules. Our approach begins with existing domain knowledge represented as an OWL ontology, and applies NLP tools and text matching techniques to deduce different atoms, such as classes, properties and literals, to capture deductive knowledge in the form of new rules. For the reason, to enrich the existing domain ontology by these rules, in order to obtain higher relational expressiveness, make reasoning and produce new facts. The approach was tested using medical reports, specifically, in the specialty of gynecology. It reports an F-measure of 95.83% on test our corpus. |
| format | Article |
| id | doaj-art-06d7183179b24a248a4e1a62f00a7d80 |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2022-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-06d7183179b24a248a4e1a62f00a7d802025-08-20T03:49:08ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782022-04-013441150116010.1016/j.jksuci.2020.05.008Rule extraction from scientific texts: Evaluation in the specialty of gynecologyAmina Boufrida0Zizette Boufaida1Corresponding author.; Lire Laboratory, University of Abdelhamid MEHRI Constantine II, Constantine, AlgeriaLire Laboratory, University of Abdelhamid MEHRI Constantine II, Constantine, AlgeriaDue to the considerable increase in freely available data (especially on the Web), extracting relevant information from textual content is a critical challenge. Most of the available data is embedded in unstructured texts and is not linked to formalized knowledge structures such as ontologies or rules. A potential solution to this problem is to acquire such knowledge through natural language processing (NLP) tools and text mining techniques. Prior work has focused on the automatic extraction of ontologies from texts, but the acquired knowledge is generally limited to simple hierarchies of terms. This paper presents a polyvalent framework for acquiring complex relationships from texts and coding these in the form of rules. Our approach begins with existing domain knowledge represented as an OWL ontology, and applies NLP tools and text matching techniques to deduce different atoms, such as classes, properties and literals, to capture deductive knowledge in the form of new rules. For the reason, to enrich the existing domain ontology by these rules, in order to obtain higher relational expressiveness, make reasoning and produce new facts. The approach was tested using medical reports, specifically, in the specialty of gynecology. It reports an F-measure of 95.83% on test our corpus.http://www.sciencedirect.com/science/article/pii/S1319157820303736Text miningNatural language processingKnowledge extractionRule acquisitionOntology Web Language (OWL) ontologySemantic Web Rule Language (SWRL) rules |
| spellingShingle | Amina Boufrida Zizette Boufaida Rule extraction from scientific texts: Evaluation in the specialty of gynecology Journal of King Saud University: Computer and Information Sciences Text mining Natural language processing Knowledge extraction Rule acquisition Ontology Web Language (OWL) ontology Semantic Web Rule Language (SWRL) rules |
| title | Rule extraction from scientific texts: Evaluation in the specialty of gynecology |
| title_full | Rule extraction from scientific texts: Evaluation in the specialty of gynecology |
| title_fullStr | Rule extraction from scientific texts: Evaluation in the specialty of gynecology |
| title_full_unstemmed | Rule extraction from scientific texts: Evaluation in the specialty of gynecology |
| title_short | Rule extraction from scientific texts: Evaluation in the specialty of gynecology |
| title_sort | rule extraction from scientific texts evaluation in the specialty of gynecology |
| topic | Text mining Natural language processing Knowledge extraction Rule acquisition Ontology Web Language (OWL) ontology Semantic Web Rule Language (SWRL) rules |
| url | http://www.sciencedirect.com/science/article/pii/S1319157820303736 |
| work_keys_str_mv | AT aminaboufrida ruleextractionfromscientifictextsevaluationinthespecialtyofgynecology AT zizetteboufaida ruleextractionfromscientifictextsevaluationinthespecialtyofgynecology |