Comprehensive structured knowledge base system construction with natural language presentation
| Autor: | Yi-Ping Phoebe Chen, Fei Liu, Shirin Akther Khanam |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Machine readable
General Computer Science Knowledge representation and reasoning Computer science Process (engineering) WordNet 02 engineering and technology Ontology (information science) computer.software_genre lcsh:QA75.5-76.95 0202 electrical engineering electronic engineering information engineering lcsh:Information theory Information retrieval business.industry Knowledge base system Concept 020207 software engineering lcsh:Q350-390 Information extraction Knowledge base 020201 artificial intelligence & image processing lcsh:Electronic computers. Computer science business computer Word (computer architecture) Natural language Ontology construction |
| Zdroj: | Human-Centric Computing and Information Sciences, Vol 9, Iss 1, Pp 1-32 (2019) |
| ISSN: | 2192-1962 |
| DOI: | 10.1186/s13673-019-0184-7 |
| Popis: | Constructing an ontology-based machine-readable knowledge base system from different sources with minimum human intervention, also known as ontology-based machine-readable knowledge base construction (OMRKBC), has been a long-term outstanding problem. One of the issues is how to build a large-scale OMRKBC process with appropriate structural information. To address this issue, we propose Natural Language Independent Knowledge Representation (NLIKR), a method which regards each word as a concept which should be defined by its relations with other concepts. Using NLIKR, we propose a framework for the OMRKBC process to automatically develop a comprehensive ontology-based machine-readable knowledge base system (OMRKBS) using well-built structural information. Firstly, as part of this framework, we propose formulas to discover concepts and their relations in the OMRKBS. Secondly, the challenges in obtaining rich structured information are resolved through the development of algorithms and rules. Finally, rich structured information is built in the OMRKBS. OMRKBC allows the efficient search of words and supports word queries with a specific attribute. We conduct experiments and analyze the results of relational information extraction, with the results showing that OMRKBS had an accuracy of 84% which was higher than the other knowledge base systems, namely ConceptNet, DBpedia and WordNet. |
| Databáze: | OpenAIRE |
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