Knowledge Graph Completion With Pattern-Based Methods
Knowledge graphs (KGs) are popularly used to develop several intelligent applications. Revealing valuable knowledge hidden in these graphs opened up a branch of research, known as KG reasoning, aiming at predicting the missing links. Some methods take advantage of external information such as entity...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10832468/ |
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| author | Maryam Sabet Mohammadreza Pajoohan Mohammad Reza Moosavi |
| author_facet | Maryam Sabet Mohammadreza Pajoohan Mohammad Reza Moosavi |
| author_sort | Maryam Sabet |
| collection | DOAJ |
| description | Knowledge graphs (KGs) are popularly used to develop several intelligent applications. Revealing valuable knowledge hidden in these graphs opened up a branch of research, known as KG reasoning, aiming at predicting the missing links. Some methods take advantage of external information such as entity description but at the cost of more computational complexity. Besides, most of the current techniques focus solely on local information in the KG. However, the learning process can utilise valuable global information in the entire graph. In this paper, we propose a Pattern-based Knowledge Graph Completion (PKGC) method that consists of three phases. The first phase utilizes multi-source information and expands the KG using entity description as external information with efficient Natural Language Processing (NLP) techniques. In the second phase, we mine frequent patterns from the expanded KG, extract connections between them and assign entities to the patterns that construct the abstraction layer. Based on the extracted patterns, connections, and entity assignments, a flow network is constructed on the abstraction layer in the third phase. We use global internal information, namely patterns, by adapting the minimum-cost circulation problem to the flow network. This way the links in a larger neighborhood are involved in the inference. We conducted experiments on the link prediction task and evaluated the training time on two benchmark datasets, WordNet and Freebase. Experiments have demonstrated that the proposed method is superior to the state-of-the-art methods and that pattern extraction is effective for knowledge graph completion tasks. |
| format | Article |
| id | doaj-art-12cf1b57d1924e69909883affa7d0ebd |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-12cf1b57d1924e69909883affa7d0ebd2025-01-14T00:02:42ZengIEEEIEEE Access2169-35362025-01-01135815583110.1109/ACCESS.2025.352558610832468Knowledge Graph Completion With Pattern-Based MethodsMaryam Sabet0https://orcid.org/0009-0009-3646-7896Mohammadreza Pajoohan1Mohammad Reza Moosavi2https://orcid.org/0000-0002-9296-9382Computer Engineering Department, Yazd University, Yazd, IranComputer Engineering Department, Yazd University, Yazd, IranDepartment of Computer Science and Engineering and IT, School of Electrical and Computer Engineering, Shiraz University, Shiraz, IranKnowledge graphs (KGs) are popularly used to develop several intelligent applications. Revealing valuable knowledge hidden in these graphs opened up a branch of research, known as KG reasoning, aiming at predicting the missing links. Some methods take advantage of external information such as entity description but at the cost of more computational complexity. Besides, most of the current techniques focus solely on local information in the KG. However, the learning process can utilise valuable global information in the entire graph. In this paper, we propose a Pattern-based Knowledge Graph Completion (PKGC) method that consists of three phases. The first phase utilizes multi-source information and expands the KG using entity description as external information with efficient Natural Language Processing (NLP) techniques. In the second phase, we mine frequent patterns from the expanded KG, extract connections between them and assign entities to the patterns that construct the abstraction layer. Based on the extracted patterns, connections, and entity assignments, a flow network is constructed on the abstraction layer in the third phase. We use global internal information, namely patterns, by adapting the minimum-cost circulation problem to the flow network. This way the links in a larger neighborhood are involved in the inference. We conducted experiments on the link prediction task and evaluated the training time on two benchmark datasets, WordNet and Freebase. Experiments have demonstrated that the proposed method is superior to the state-of-the-art methods and that pattern extraction is effective for knowledge graph completion tasks.https://ieeexplore.ieee.org/document/10832468/Frequent pattern miningflow networkknowledge graph completionminimum-cost circulation problem |
| spellingShingle | Maryam Sabet Mohammadreza Pajoohan Mohammad Reza Moosavi Knowledge Graph Completion With Pattern-Based Methods IEEE Access Frequent pattern mining flow network knowledge graph completion minimum-cost circulation problem |
| title | Knowledge Graph Completion With Pattern-Based Methods |
| title_full | Knowledge Graph Completion With Pattern-Based Methods |
| title_fullStr | Knowledge Graph Completion With Pattern-Based Methods |
| title_full_unstemmed | Knowledge Graph Completion With Pattern-Based Methods |
| title_short | Knowledge Graph Completion With Pattern-Based Methods |
| title_sort | knowledge graph completion with pattern based methods |
| topic | Frequent pattern mining flow network knowledge graph completion minimum-cost circulation problem |
| url | https://ieeexplore.ieee.org/document/10832468/ |
| work_keys_str_mv | AT maryamsabet knowledgegraphcompletionwithpatternbasedmethods AT mohammadrezapajoohan knowledgegraphcompletionwithpatternbasedmethods AT mohammadrezamoosavi knowledgegraphcompletionwithpatternbasedmethods |