Author:
Hao Huibin,Sun Xiang-e,Wei Jian
Abstract
AbstractIn Open-domain Chinese Knowledge Base Question Answering (ODCKBQA), most common simple questions can be answered by a single relational fact in the knowledge base (KB). The abbreviations, aliases, and nesting of entities in Chinese question sentences, and the gap between them and the structured semantics in the knowledge base, make it difficult for the system to accurately return answers. This study proposes a semantic union model (SUM), which concatenates candidate entities and candidate relationships, using a contrastive learning algorithm to learn the semantic vector representation of question and candidate entity-relation pairs, and perform cosine similarity calculations to simultaneously complete entity disambiguation and relation matching tasks. It can provide information for entity disambiguation through the relationships between entities, avoid error propagation, and improve the system performance. The experimental results show that the system achieves a good average F1 of 85.94% on the dataset provided by the NLPCC-ICCPOL 2016 KBQA task.
Publisher
Springer Science and Business Media LLC
Reference25 articles.
1. Fader, A., Zettlemoyer, L., Etzioni, O. Paraphrase-driven learning for open question answering. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1608–1618 (2013).
2. Bordes, A., Chopra, S., Weston, J. Question answering with subgraph embeddings. In: Empirical Methods in Natural Language Processing. Association for Computational Linguistics. (2014).
3. Do, P. & Phan, T. H. Developing a bert based triple classification model using knowledge graph embedding for question answering system. Appl. Intell. 52(1), 636–651 (2022).
4. SU, J.L. CoSENT(1): A more efficient sentence vector scheme than Sentence-BERT. https://spaces.ac.cn/archives/8847 (2022).
5. Zhou, G. & Huang, J. X. Modeling and learning distributed word representation with metadata for question retrieval. IEEE Trans. Knowl. Data Eng. 29(6), 1226–1239 (2017).