SFCA: A Scalable Formal Concepts Driven Architecture for Multi-Field Knowledge Graph Completion
With the proliferation of Knowledge Graphs (KGs), knowledge graph completion (KGC) has attracted much attention. Previous KGC methods focus on extracting shallow structural information from KGs or in combination with external knowledge, especially in commonsense concepts (generally, commonsense conc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/11/6851 |
| _version_ | 1827739645308305408 |
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| author | Xiaochun Sun Chenmou Wu Shuqun Yang |
| author_facet | Xiaochun Sun Chenmou Wu Shuqun Yang |
| author_sort | Xiaochun Sun |
| collection | DOAJ |
| description | With the proliferation of Knowledge Graphs (KGs), knowledge graph completion (KGC) has attracted much attention. Previous KGC methods focus on extracting shallow structural information from KGs or in combination with external knowledge, especially in commonsense concepts (generally, commonsense concepts refer to the basic concepts in related fields that are required for various tasks and academic research, for example, in the general domain, “Country” can be considered as a commonsense concept owned by “China”), to predict missing links. However, the technology of extracting commonsense concepts from the limited database is immature, and the scarce commonsense database is also bound to specific verticals (commonsense concepts vary greatly across verticals, verticals refer to a small field subdivided vertically under a large field). Furthermore, most existing KGC models refine performance on public KGs, leading to inapplicability to actual KGs. To address these limitations, we proposed a novel Scalable Formal Concept-driven Architecture (SFCA) to automatically encode factual triples into formal concepts as a superior structural feature, to support rich information to KGE. Specifically, we generate dense formal concepts first, then yield a handful of entity-related formal concepts by sampling and delimiting the appropriate candidate entity range via the filtered formal concepts to improve the inference of KGC. Compared with commonsense concepts, KGC benefits from more valuable information from the formal concepts, and our self-supervision extraction method can be applied to any KGs. Comprehensive experiments on five public datasets demonstrate the effectiveness and scalability of SFCA. Besides, the proposed architecture also achieves the SOTA performance on the industry dataset. This method provides a new idea in the promotion and application of knowledge graphs in AI downstream tasks in general and industrial fields. |
| first_indexed | 2024-03-11T03:10:09Z |
| format | Article |
| id | doaj.art-0291f94bbe9f447997cb10fbb045c6c7 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T03:10:09Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0291f94bbe9f447997cb10fbb045c6c72023-11-18T07:37:33ZengMDPI AGApplied Sciences2076-34172023-06-011311685110.3390/app13116851SFCA: A Scalable Formal Concepts Driven Architecture for Multi-Field Knowledge Graph CompletionXiaochun Sun0Chenmou Wu1Shuqun Yang2School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaDepartment of Computer Science & Engineering, Jeonbuk National University, Jeonju 54896, Republic of KoreaSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaWith the proliferation of Knowledge Graphs (KGs), knowledge graph completion (KGC) has attracted much attention. Previous KGC methods focus on extracting shallow structural information from KGs or in combination with external knowledge, especially in commonsense concepts (generally, commonsense concepts refer to the basic concepts in related fields that are required for various tasks and academic research, for example, in the general domain, “Country” can be considered as a commonsense concept owned by “China”), to predict missing links. However, the technology of extracting commonsense concepts from the limited database is immature, and the scarce commonsense database is also bound to specific verticals (commonsense concepts vary greatly across verticals, verticals refer to a small field subdivided vertically under a large field). Furthermore, most existing KGC models refine performance on public KGs, leading to inapplicability to actual KGs. To address these limitations, we proposed a novel Scalable Formal Concept-driven Architecture (SFCA) to automatically encode factual triples into formal concepts as a superior structural feature, to support rich information to KGE. Specifically, we generate dense formal concepts first, then yield a handful of entity-related formal concepts by sampling and delimiting the appropriate candidate entity range via the filtered formal concepts to improve the inference of KGC. Compared with commonsense concepts, KGC benefits from more valuable information from the formal concepts, and our self-supervision extraction method can be applied to any KGs. Comprehensive experiments on five public datasets demonstrate the effectiveness and scalability of SFCA. Besides, the proposed architecture also achieves the SOTA performance on the industry dataset. This method provides a new idea in the promotion and application of knowledge graphs in AI downstream tasks in general and industrial fields.https://www.mdpi.com/2076-3417/13/11/6851formal conceptknowledge graph embeddingmachine learningknowledge graph completion |
| spellingShingle | Xiaochun Sun Chenmou Wu Shuqun Yang SFCA: A Scalable Formal Concepts Driven Architecture for Multi-Field Knowledge Graph Completion Applied Sciences formal concept knowledge graph embedding machine learning knowledge graph completion |
| title | SFCA: A Scalable Formal Concepts Driven Architecture for Multi-Field Knowledge Graph Completion |
| title_full | SFCA: A Scalable Formal Concepts Driven Architecture for Multi-Field Knowledge Graph Completion |
| title_fullStr | SFCA: A Scalable Formal Concepts Driven Architecture for Multi-Field Knowledge Graph Completion |
| title_full_unstemmed | SFCA: A Scalable Formal Concepts Driven Architecture for Multi-Field Knowledge Graph Completion |
| title_short | SFCA: A Scalable Formal Concepts Driven Architecture for Multi-Field Knowledge Graph Completion |
| title_sort | sfca a scalable formal concepts driven architecture for multi field knowledge graph completion |
| topic | formal concept knowledge graph embedding machine learning knowledge graph completion |
| url | https://www.mdpi.com/2076-3417/13/11/6851 |
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