ADD: Academic Disciplines Detector Based on Wikipedia
The academic disciplines and their interrelationships represent a backbone that organizes the enormous amount of documented human knowledge available today. Having an up-to-date overview of the established disciplines, the emerging ones, and their mutual interactions is essential to the academic ins...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8948031/ |
| _version_ | 1818875909974786048 |
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| author | Ana Gjorgjevikj Kostadin Mishev Dimitar Trajanov |
| author_facet | Ana Gjorgjevikj Kostadin Mishev Dimitar Trajanov |
| author_sort | Ana Gjorgjevikj |
| collection | DOAJ |
| description | The academic disciplines and their interrelationships represent a backbone that organizes the enormous amount of documented human knowledge available today. Having an up-to-date overview of the established disciplines, the emerging ones, and their mutual interactions is essential to the academic institutions, publishers, and many other actors involved in today's knowledge-based society, even in a situation of nonexistence of a precise definition of the term “academic discipline” itself. The discipline classification schemes represent crucial resources for the purpose, and in circumstances where the knowledge production rate demands discovering changes in their structure very frequently, the data-driven methodologies which facilitate their revision processes become essential. Analyzing the world-wide community's opinion on what represents a discipline, available through Wikipedia, can be very informative for the purpose, considering Wikipedia's comprehensiveness, continuous updates, and historical exports availability. This paper proposes a data-driven methodology for identification of the concepts which the world-wide community defines as disciplines at a particular moment by analyzing the information available in Wikipedia at that same moment. At the same time, it discusses Wikipedia's strengths and challenges on the task while also comparing a variety of Machine Learning and Natural Language Processing methodologies. High accuracy of the trained models is achieved on datasets created for this task specifically, and low changes in the model accuracy are observed on four Wikipedia exports from 2015 to 2018. |
| first_indexed | 2024-12-19T13:34:00Z |
| format | Article |
| id | doaj.art-465586320f404b3282be393e52af4b5c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T13:34:00Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-465586320f404b3282be393e52af4b5c2022-12-21T20:19:16ZengIEEEIEEE Access2169-35362020-01-0187005701910.1109/ACCESS.2019.29636748948031ADD: Academic Disciplines Detector Based on WikipediaAna Gjorgjevikj0https://orcid.org/0000-0002-5135-7718Kostadin Mishev1https://orcid.org/0000-0003-3982-3330Dimitar Trajanov2https://orcid.org/0000-0002-3105-6010Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Skopje, MacedoniaFaculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Skopje, MacedoniaFaculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Skopje, MacedoniaThe academic disciplines and their interrelationships represent a backbone that organizes the enormous amount of documented human knowledge available today. Having an up-to-date overview of the established disciplines, the emerging ones, and their mutual interactions is essential to the academic institutions, publishers, and many other actors involved in today's knowledge-based society, even in a situation of nonexistence of a precise definition of the term “academic discipline” itself. The discipline classification schemes represent crucial resources for the purpose, and in circumstances where the knowledge production rate demands discovering changes in their structure very frequently, the data-driven methodologies which facilitate their revision processes become essential. Analyzing the world-wide community's opinion on what represents a discipline, available through Wikipedia, can be very informative for the purpose, considering Wikipedia's comprehensiveness, continuous updates, and historical exports availability. This paper proposes a data-driven methodology for identification of the concepts which the world-wide community defines as disciplines at a particular moment by analyzing the information available in Wikipedia at that same moment. At the same time, it discusses Wikipedia's strengths and challenges on the task while also comparing a variety of Machine Learning and Natural Language Processing methodologies. High accuracy of the trained models is achieved on datasets created for this task specifically, and low changes in the model accuracy are observed on four Wikipedia exports from 2015 to 2018.https://ieeexplore.ieee.org/document/8948031/Machine learning algorithmsnatural language processingacademic disciplinetext analysisWikipedia |
| spellingShingle | Ana Gjorgjevikj Kostadin Mishev Dimitar Trajanov ADD: Academic Disciplines Detector Based on Wikipedia IEEE Access Machine learning algorithms natural language processing academic discipline text analysis Wikipedia |
| title | ADD: Academic Disciplines Detector Based on Wikipedia |
| title_full | ADD: Academic Disciplines Detector Based on Wikipedia |
| title_fullStr | ADD: Academic Disciplines Detector Based on Wikipedia |
| title_full_unstemmed | ADD: Academic Disciplines Detector Based on Wikipedia |
| title_short | ADD: Academic Disciplines Detector Based on Wikipedia |
| title_sort | add academic disciplines detector based on wikipedia |
| topic | Machine learning algorithms natural language processing academic discipline text analysis Wikipedia |
| url | https://ieeexplore.ieee.org/document/8948031/ |
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