Literaturnachweis - Detailanzeige
| Autor/inn/en | Sheng Bi; Zeyi Miao; Qizhi Min |
|---|---|
| Titel | LEMON: A Knowledge-Enhanced, Type-Constrained, and Grammar-Guided Model for Question Generation over Knowledge Graphs |
| Quelle | In: IEEE Transactions on Learning Technologies, 18 (2025), S. 256-272Infoseite zur Zeitschrift
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| Zusatzinformation | ORCID (Sheng Bi) ORCID (Zeyi Miao) ORCID (Qizhi Min) |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| DOI | 10.1109/TLT.2025.3544454 |
| Schlagwörter | Forschungsbericht; Grammar; Models; Questioning Techniques; Graphs; Artificial Intelligence; Accuracy; Heuristics; Guidelines; Syntax; Semantics; Reinforcement; Rewards; Benchmarking; Competition |
| Abstract | The objective of question generation from knowledge graphs (KGQG) is to create coherent and answerable questions from a given subgraph and a specified answer entity. KGQG has garnered significant attention due to its pivotal role in enhancing online education. Encoder-decoder architectures have advanced traditional KGQG approaches. However, these approaches encounter challenges in achieving question diversity and grammatical accuracy. They often suffer from a disconnect between the phrasing of the question and the type of the answer entity, a phenomenon known as semantic drift. To address these challenges, we introduce LEMON, a knowledge-enhanced, type-constrained, and grammar-guided model for KGQG. LEMON enhances the input by integrating entity-related knowledge using heuristic rules, which fosters diversity in question generation. It employs a hierarchical global relation embedding with translation loss to align questions with entity types. In addition, it utilizes a graph-based module to aggregate type information from neighboring nodes. The LEMON model incorporates a type-constrained decoder to generate diverse expressions and improves grammatical accuracy through a syntactic and semantic reward function via reinforcement learning. Evaluations on benchmark datasets demonstrate LEMON's strong competitiveness. The study also examines the impact of question generation quality on question-answering systems, providing guidance for future research endeavors in this domain. (As Provided). |
| Anmerkungen | Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076 |
| Begutachtung | Peer reviewed |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2025/3/08 |
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