Intelligent SPARQL Query Generation for Natural Language Processing Systems
Developing question answering (QA) systems that process natural language is a popular research topic. Conventionally, when QA systems receive a natural language question, they choose useful words or phrases based on their parts-of-speech (POS) tags. In general, words tagged as nouns are mapped to cl...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9627128/ |
| _version_ | 1818574161291771904 |
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| author | Yi-Hui Chen Eric Jui-Lin Lu Ting-An Ou |
| author_facet | Yi-Hui Chen Eric Jui-Lin Lu Ting-An Ou |
| author_sort | Yi-Hui Chen |
| collection | DOAJ |
| description | Developing question answering (QA) systems that process natural language is a popular research topic. Conventionally, when QA systems receive a natural language question, they choose useful words or phrases based on their parts-of-speech (POS) tags. In general, words tagged as nouns are mapped to class entities, words tagged as verbs are mapped to property entities, and words tagged as proper nouns are mapped to named entities, although the accuracy of entity type identification remains low. Afterward, the relationship between entity types as RDF types determines the first element to be a pivot word to generate the SPARQL (acronym for SPARQL protocol and RDF query language) query on the basis of the sequences by a specific graph or tree structure, such as dependence tree or directed acyclic graph (DAG). However, the generated SPARQL query is difficult to adapt to the given query request in that the sequences are decided by a fixed structure. Unlike in previous research, SPARQL generation occurs automatically according to the entity type identification and RDF type identification results. This study attempts to design a method that leverages machine learning to learn human experiences in entity type identification as well as RDF-type identification. We approach the problem as a multiclass classification problem and propose a two-stage maximum-entropy Markov model (MEMM). The first stage identifies the entity type and the second identifies the RDF type for the purpose of generating appropriate SPARQL queries to meet the query request. Along with the templates designed for the two-stage MEMM model, we develop an automatic question answering prototype system called QAWizard. The experimental results show that QAWizard outperforms all other systems in question answering when evaluated on Linked Data version 8 (QALD-8) metrics. |
| first_indexed | 2024-12-15T00:22:52Z |
| format | Article |
| id | doaj.art-5054be509f7c442798becddf6ff82144 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-15T00:22:52Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5054be509f7c442798becddf6ff821442022-12-21T22:42:15ZengIEEEIEEE Access2169-35362021-01-01915863815865010.1109/ACCESS.2021.31306679627128Intelligent SPARQL Query Generation for Natural Language Processing SystemsYi-Hui Chen0https://orcid.org/0000-0002-9932-0594Eric Jui-Lin Lu1https://orcid.org/0000-0001-7953-5486Ting-An Ou2Department of Information Management, Chang Gung University, Taoyuan, TaiwanDepartment of Management Information Systems, National Chung Hsing University, Taichung, TaiwanDepartment of Management Information Systems, National Chung Hsing University, Taichung, TaiwanDeveloping question answering (QA) systems that process natural language is a popular research topic. Conventionally, when QA systems receive a natural language question, they choose useful words or phrases based on their parts-of-speech (POS) tags. In general, words tagged as nouns are mapped to class entities, words tagged as verbs are mapped to property entities, and words tagged as proper nouns are mapped to named entities, although the accuracy of entity type identification remains low. Afterward, the relationship between entity types as RDF types determines the first element to be a pivot word to generate the SPARQL (acronym for SPARQL protocol and RDF query language) query on the basis of the sequences by a specific graph or tree structure, such as dependence tree or directed acyclic graph (DAG). However, the generated SPARQL query is difficult to adapt to the given query request in that the sequences are decided by a fixed structure. Unlike in previous research, SPARQL generation occurs automatically according to the entity type identification and RDF type identification results. This study attempts to design a method that leverages machine learning to learn human experiences in entity type identification as well as RDF-type identification. We approach the problem as a multiclass classification problem and propose a two-stage maximum-entropy Markov model (MEMM). The first stage identifies the entity type and the second identifies the RDF type for the purpose of generating appropriate SPARQL queries to meet the query request. Along with the templates designed for the two-stage MEMM model, we develop an automatic question answering prototype system called QAWizard. The experimental results show that QAWizard outperforms all other systems in question answering when evaluated on Linked Data version 8 (QALD-8) metrics.https://ieeexplore.ieee.org/document/9627128/Question answering system (QA)parts-of-speech (POS)SPARQL querymaximum-entropy Markov model |
| spellingShingle | Yi-Hui Chen Eric Jui-Lin Lu Ting-An Ou Intelligent SPARQL Query Generation for Natural Language Processing Systems IEEE Access Question answering system (QA) parts-of-speech (POS) SPARQL query maximum-entropy Markov model |
| title | Intelligent SPARQL Query Generation for Natural Language Processing Systems |
| title_full | Intelligent SPARQL Query Generation for Natural Language Processing Systems |
| title_fullStr | Intelligent SPARQL Query Generation for Natural Language Processing Systems |
| title_full_unstemmed | Intelligent SPARQL Query Generation for Natural Language Processing Systems |
| title_short | Intelligent SPARQL Query Generation for Natural Language Processing Systems |
| title_sort | intelligent sparql query generation for natural language processing systems |
| topic | Question answering system (QA) parts-of-speech (POS) SPARQL query maximum-entropy Markov model |
| url | https://ieeexplore.ieee.org/document/9627128/ |
| work_keys_str_mv | AT yihuichen intelligentsparqlquerygenerationfornaturallanguageprocessingsystems AT ericjuilinlu intelligentsparqlquerygenerationfornaturallanguageprocessingsystems AT tinganou intelligentsparqlquerygenerationfornaturallanguageprocessingsystems |