Automating the extraction of information from a historical text and building a linked data model for the domain of ecology and conservation science
Data heterogeneity is a pressing issue and is further compounded if we have to deal with data from textual documents. The unstructured nature of such documents implies that collating, comparing and analysing the information contained therein can be a challenging task. Automating these processes can...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-10-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844022019983 |
| _version_ | 1811192557974061056 |
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| author | Vatsala Nundloll Robert Smail Carly Stevens Gordon Blair |
| author_facet | Vatsala Nundloll Robert Smail Carly Stevens Gordon Blair |
| author_sort | Vatsala Nundloll |
| collection | DOAJ |
| description | Data heterogeneity is a pressing issue and is further compounded if we have to deal with data from textual documents. The unstructured nature of such documents implies that collating, comparing and analysing the information contained therein can be a challenging task. Automating these processes can help to unleash insightful knowledge that otherwise remains buried in them. Moreover, integrating the extracted information from the documents with other related information can help to make more information-rich queries. In this context, the paper presents a comprehensive review of text extraction and data integration techniques to enable this automation process in an ecological context. The paper investigates into extracting valuable floristic information from a historical Botany journal. The purpose behind this extraction is to bring to light relevant pieces of information contained within the document. In addition, the paper also explores the need to integrate the extracted information together with other related information from disparate sources. All the information is then rendered into a query-able form in order to make unified queries. Hence, the paper makes use of a combination of Machine Learning, Natural Language Processing and Semantic Web techniques to achieve this. The proposed approach is demonstrated through the information extracted from the journal and the information-rich queries made through the integration process. The paper shows that the approach has a merit in extracting relevant information from the journal, discusses how the machine learning models have been designed to classify complex information and also gives a measure of their performance. The paper also shows that the approach has a merit in query time in regard to querying floristic information from a multi-source linked data model. |
| first_indexed | 2024-04-11T23:54:36Z |
| format | Article |
| id | doaj.art-eb03e28d3d2f4d65a598068412287365 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-11T23:54:36Z |
| publishDate | 2022-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-eb03e28d3d2f4d65a5980684122873652022-12-22T03:56:24ZengElsevierHeliyon2405-84402022-10-01810e10710Automating the extraction of information from a historical text and building a linked data model for the domain of ecology and conservation scienceVatsala Nundloll0Robert Smail1Carly Stevens2Gordon Blair3School of Computing and Communications, Lancaster University, Lancaster, UK; Corresponding author.Lancaster Environment Centre, Lancaster University, UK11 Robert Smail worked at this organisation.Lancaster Environment Centre, Lancaster University, UK11 Robert Smail worked at this organisation.School of Computing and Communications, Lancaster University, Lancaster, UKData heterogeneity is a pressing issue and is further compounded if we have to deal with data from textual documents. The unstructured nature of such documents implies that collating, comparing and analysing the information contained therein can be a challenging task. Automating these processes can help to unleash insightful knowledge that otherwise remains buried in them. Moreover, integrating the extracted information from the documents with other related information can help to make more information-rich queries. In this context, the paper presents a comprehensive review of text extraction and data integration techniques to enable this automation process in an ecological context. The paper investigates into extracting valuable floristic information from a historical Botany journal. The purpose behind this extraction is to bring to light relevant pieces of information contained within the document. In addition, the paper also explores the need to integrate the extracted information together with other related information from disparate sources. All the information is then rendered into a query-able form in order to make unified queries. Hence, the paper makes use of a combination of Machine Learning, Natural Language Processing and Semantic Web techniques to achieve this. The proposed approach is demonstrated through the information extracted from the journal and the information-rich queries made through the integration process. The paper shows that the approach has a merit in extracting relevant information from the journal, discusses how the machine learning models have been designed to classify complex information and also gives a measure of their performance. The paper also shows that the approach has a merit in query time in regard to querying floristic information from a multi-source linked data model.http://www.sciencedirect.com/science/article/pii/S2405844022019983Data extractionUnstructured dataSemantic integrationNatural language processingMachine learningOntologies |
| spellingShingle | Vatsala Nundloll Robert Smail Carly Stevens Gordon Blair Automating the extraction of information from a historical text and building a linked data model for the domain of ecology and conservation science Heliyon Data extraction Unstructured data Semantic integration Natural language processing Machine learning Ontologies |
| title | Automating the extraction of information from a historical text and building a linked data model for the domain of ecology and conservation science |
| title_full | Automating the extraction of information from a historical text and building a linked data model for the domain of ecology and conservation science |
| title_fullStr | Automating the extraction of information from a historical text and building a linked data model for the domain of ecology and conservation science |
| title_full_unstemmed | Automating the extraction of information from a historical text and building a linked data model for the domain of ecology and conservation science |
| title_short | Automating the extraction of information from a historical text and building a linked data model for the domain of ecology and conservation science |
| title_sort | automating the extraction of information from a historical text and building a linked data model for the domain of ecology and conservation science |
| topic | Data extraction Unstructured data Semantic integration Natural language processing Machine learning Ontologies |
| url | http://www.sciencedirect.com/science/article/pii/S2405844022019983 |
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