Semantic Document Networks to Support Concept Retrieval
There are many unstructured documents created in many disciplines which need to be (pre-) processed in one way or another for further integration and use in IT systems. The predominance of the Internet and large corporate databases implies that there are large volumes of documents that need to be an...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Book Chapter |
| Published: |
IGI Global
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/26103 |
| _version_ | 1848751889585274880 |
|---|---|
| author | Boese, S. Reiners, Torsten Wood, Lincoln |
| author2 | John Wang |
| author_facet | John Wang Boese, S. Reiners, Torsten Wood, Lincoln |
| author_sort | Boese, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | There are many unstructured documents created in many disciplines which need to be (pre-) processed in one way or another for further integration and use in IT systems. The predominance of the Internet and large corporate databases implies that there are large volumes of documents that need to be analysed and searched to retrieve information; particularly within the fields of machine translation, text analysis, semantic mining, information extraction and retrieval. We explicate a framework based on concept-based indexing that supports the analysis, storage, and retrieval of documents. Natural-language reduction is used to calculate semantic cores for concept-based indexing of stored concepts found within documents. The processed documents are stored within a semantic network enabling effective analysis of core concepts within documents and rapid retrieval of specific ideas from multiple documents based on provided concepts. |
| first_indexed | 2025-11-14T07:59:54Z |
| format | Book Chapter |
| id | curtin-20.500.11937-26103 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:59:54Z |
| publishDate | 2014 |
| publisher | IGI Global |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-261032017-09-13T15:22:41Z Semantic Document Networks to Support Concept Retrieval Boese, S. Reiners, Torsten Wood, Lincoln John Wang There are many unstructured documents created in many disciplines which need to be (pre-) processed in one way or another for further integration and use in IT systems. The predominance of the Internet and large corporate databases implies that there are large volumes of documents that need to be analysed and searched to retrieve information; particularly within the fields of machine translation, text analysis, semantic mining, information extraction and retrieval. We explicate a framework based on concept-based indexing that supports the analysis, storage, and retrieval of documents. Natural-language reduction is used to calculate semantic cores for concept-based indexing of stored concepts found within documents. The processed documents are stored within a semantic network enabling effective analysis of core concepts within documents and rapid retrieval of specific ideas from multiple documents based on provided concepts. 2014 Book Chapter http://hdl.handle.net/20.500.11937/26103 10.4018/978-1-4666-5202-6.ch192 IGI Global fulltext |
| spellingShingle | Boese, S. Reiners, Torsten Wood, Lincoln Semantic Document Networks to Support Concept Retrieval |
| title | Semantic Document Networks to Support Concept Retrieval |
| title_full | Semantic Document Networks to Support Concept Retrieval |
| title_fullStr | Semantic Document Networks to Support Concept Retrieval |
| title_full_unstemmed | Semantic Document Networks to Support Concept Retrieval |
| title_short | Semantic Document Networks to Support Concept Retrieval |
| title_sort | semantic document networks to support concept retrieval |
| url | http://hdl.handle.net/20.500.11937/26103 |