A better entity detection of question for knowledge graph question answering through extracting position-based patterns
Abstract Entity detection task on knowledge graph question answering systems has been studied well on simple questions. However, the task is still challenging on complex questions. It is due to a complex question is composed of more than one fact or triple. This paper proposes a method to detect ent...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2022-06-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-022-00631-1 |
| _version_ | 1818239018710597632 |
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| author | Mohammad Yani Adila Alfa Krisnadhi Indra Budi |
| author_facet | Mohammad Yani Adila Alfa Krisnadhi Indra Budi |
| author_sort | Mohammad Yani |
| collection | DOAJ |
| description | Abstract Entity detection task on knowledge graph question answering systems has been studied well on simple questions. However, the task is still challenging on complex questions. It is due to a complex question is composed of more than one fact or triple. This paper proposes a method to detect entities and their position on triples mentioned in a question. Unlike existing approaches that only focus on detecting the entity name, our method can determine in which triple an entity is located. Furthermore, our approach can also define if an entity is a head or a tail of a triple mentioned in a question. We tested our approach to SimpleQuestions, LC-QuAD 2.0, and QALD series benchmarks. The experiment result demonstrates that our model outperforms the previous works on SimpleQuestions and QALD series datasets. 99.15% accuracy and 96.15% accuracy on average, respectively. Our model can also improve entity detection performance on LC-QuAD 2.0 with a merged dataset, namely, 97.4% accuracy. This paper also presents Wikidata QALD series version that is helpful for researchers to assess the knowledge graph question answering system they develop. |
| first_indexed | 2024-12-12T12:50:53Z |
| format | Article |
| id | doaj.art-c17bc4fa2a9b4f02a259f0b7d14e0b82 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-12-12T12:50:53Z |
| publishDate | 2022-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-c17bc4fa2a9b4f02a259f0b7d14e0b822022-12-22T00:24:00ZengSpringerOpenJournal of Big Data2196-11152022-06-019112610.1186/s40537-022-00631-1A better entity detection of question for knowledge graph question answering through extracting position-based patternsMohammad Yani0Adila Alfa Krisnadhi1Indra Budi2Faculty of Computer Science, Universitas IndonesiaFaculty of Computer Science, Universitas IndonesiaFaculty of Computer Science, Universitas IndonesiaAbstract Entity detection task on knowledge graph question answering systems has been studied well on simple questions. However, the task is still challenging on complex questions. It is due to a complex question is composed of more than one fact or triple. This paper proposes a method to detect entities and their position on triples mentioned in a question. Unlike existing approaches that only focus on detecting the entity name, our method can determine in which triple an entity is located. Furthermore, our approach can also define if an entity is a head or a tail of a triple mentioned in a question. We tested our approach to SimpleQuestions, LC-QuAD 2.0, and QALD series benchmarks. The experiment result demonstrates that our model outperforms the previous works on SimpleQuestions and QALD series datasets. 99.15% accuracy and 96.15% accuracy on average, respectively. Our model can also improve entity detection performance on LC-QuAD 2.0 with a merged dataset, namely, 97.4% accuracy. This paper also presents Wikidata QALD series version that is helpful for researchers to assess the knowledge graph question answering system they develop.https://doi.org/10.1186/s40537-022-00631-1Entity detectionEntity recognitionKnowledge graph question answeringComplex questionQuestion pattern |
| spellingShingle | Mohammad Yani Adila Alfa Krisnadhi Indra Budi A better entity detection of question for knowledge graph question answering through extracting position-based patterns Journal of Big Data Entity detection Entity recognition Knowledge graph question answering Complex question Question pattern |
| title | A better entity detection of question for knowledge graph question answering through extracting position-based patterns |
| title_full | A better entity detection of question for knowledge graph question answering through extracting position-based patterns |
| title_fullStr | A better entity detection of question for knowledge graph question answering through extracting position-based patterns |
| title_full_unstemmed | A better entity detection of question for knowledge graph question answering through extracting position-based patterns |
| title_short | A better entity detection of question for knowledge graph question answering through extracting position-based patterns |
| title_sort | better entity detection of question for knowledge graph question answering through extracting position based patterns |
| topic | Entity detection Entity recognition Knowledge graph question answering Complex question Question pattern |
| url | https://doi.org/10.1186/s40537-022-00631-1 |
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