{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:46:27Z","timestamp":1760237187926,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,3,5]],"date-time":"2020-03-05T00:00:00Z","timestamp":1583366400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Social Science Foundation","award":["19BYY076"],"award-info":[{"award-number":["19BYY076"]}]},{"DOI":"10.13039\/501100019048","name":"Science Foundation of Ministry of Education of China","doi-asserted-by":"publisher","award":["14YJC860042"],"award-info":[{"award-number":["14YJC860042"]}],"id":[{"id":"10.13039\/501100019048","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shandong Provincial Social Science Planning Project","award":["19BJCJ51, 18CXWJ01, 18BJYJ04"],"award-info":[{"award-number":["19BJCJ51, 18CXWJ01, 18BJYJ04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Knowledge base question answering (KBQA) aims to analyze the semantics of natural language questions and return accurate answers from the knowledge base (KB). More and more studies have applied knowledge bases to question answering systems, and when using a KB to answer a natural language question, there are some words that imply the tense (e.g., original and previous) and play a limiting role in questions. However, most existing methods for KBQA cannot model a question with implicit temporal constraints. In this work, we propose a model based on a bidirectional attentive memory network, which obtains the temporal information in the question through attention mechanisms and external knowledge. Specifically, we encode the external knowledge as vectors, and use additive attention between the question and external knowledge to obtain the temporal information, then further enhance the question vector to increase the accuracy. On the WebQuestions benchmark, our method not only performs better with the overall data, but also has excellent performance regarding questions with implicit temporal constraints, which are separate from the overall data. As we use attention mechanisms, our method also offers better interpretability.<\/jats:p>","DOI":"10.3390\/fi12030045","type":"journal-article","created":{"date-parts":[[2020,3,6]],"date-time":"2020-03-06T09:26:41Z","timestamp":1583486801000},"page":"45","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Introducing External Knowledge to Answer Questions with Implicit Temporal Constraints over Knowledge Base"],"prefix":"10.3390","volume":"12","author":[{"given":"Wenqing","family":"Wu","sequence":"first","affiliation":[{"name":"School of Information Science and Electrical Engineering, Shandong Jiao tong University, Jinan 250357, China"}]},{"given":"Zhenfang","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Information Science and Electrical Engineering, Shandong Jiao tong University, Jinan 250357, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2868-1891","authenticated-orcid":false,"given":"Qiang","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Information Science and Electrical Engineering, Shandong Jiao tong University, Jinan 250357, China"}]},{"given":"Dianyuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Electrical Engineering, Shandong Jiao tong University, Jinan 250357, China"}]},{"given":"Qiangqiang","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Information Science and Electrical Engineering, Shandong Jiao tong University, Jinan 250357, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., and Ives, Z. (2007, January 11\u201315). DBpedia: A Nucleus for a Web of Open Data. The Semantic Web, 6th International Semantic Web Conference, 2nd Asian Semantic Web Conference, Busan, Korea.","DOI":"10.1007\/978-3-540-76298-0_52"},{"key":"ref_2","unstructured":"Google (2019, December 20). Freebase Data Dumps. Available online: https:\/\/developers.google.com\/freebase."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Sintek, M., and Decker, S. (2002, January 9\u201312). TRIPLE\u2014A Query, Inference, and Transformation Language for the Semantic Web. Proceedings of the International Semantic Web Conference, Sardinia, Italy.","DOI":"10.1007\/3-540-48005-6_28"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Xu, K., Reddy, S., Feng, Y., Huang, S., Zhao, D., Erk, K., and Smith, N.A. (2016, January 7\u201312). Question Answering on Freebase via Relation Extraction and Textual Evidence. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Berlin, Germany.","DOI":"10.18653\/v1\/P16-1220"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bast, H., Haussmann, E., Bailey, J., Moffat, A., Aggarwal, C.C., De Rijke, M., Kumar, R., Murdock, V., Sellis, T., and Yu, J.X. (2015, January 19\u201323). More Accurate Question Answering on Freebase. Proceedings of the 24th ACM International on Conference on Information and Knowledge Management-CIKM \u201915, Melbourne, VIC, Australia.","DOI":"10.1145\/2806416.2806472"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Abujabal, A., Yahya, M., Riedewald, M., and Weikum, G. (2017, January 3\u20137). Automated Template Generation for Question Answering over Knowledge Graphs. Proceedings of the 26th International Conference on World Wide Web Companion-WWW \u201917 Companion, Perth, Australia.","DOI":"10.1145\/3038912.3052583"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Berant, J., and Liang, P. (2014, January 22\u201327). Semantic Parsing via Paraphrasing. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Baltimore, MD, USA.","DOI":"10.3115\/v1\/P14-1133"},{"key":"ref_8","unstructured":"Kwiatkowski, T., Zettlemoyer, L., Goldwater, S., and Steedman, M. (2011, January 27\u201331). Lexical generalization in ccg grammar induction for semantic parsing. Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, Edinburgh, Scotland, UK."},{"key":"ref_9","unstructured":"Wong, Y.W., and Mooney, R. (2007, January 23\u201330). Learning synchronous grammars for semantic parsing with lambda calculus. Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, Prague, Czech Republic."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hu, S., Zou, L., Yu, J.X., Wang, H., and Zhao, D. (2018, January 16\u201320). Answering Natural Language Questions by Subgraph Matching over Knowledge Graphs (Extended Abstract). Proceedings of the 2018 IEEE 34th International Conference on Data Engineering (ICDE), Paris, France.","DOI":"10.1109\/ICDE.2018.00265"},{"key":"ref_11","unstructured":"Cai, Q., and Yates, A. (2013, January 4\u20139). Large-scale semantic parsing via schema matching and lexicon extension. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, Sofia, Bulgaria."},{"key":"ref_12","unstructured":"Jayant Krishnamurthy, J., and Mitchell, M.T. (2012, January 12\u201314). Weakly supervised training of semantic parsers. Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Jeju Island, Korea."},{"key":"ref_13","first-page":"165","article-title":"Open Question Answering with Weakly Supervised Embedding Models","volume":"Volume 8724","author":"Bordes","year":"2014","journal-title":"Formal Aspects of Component Software"},{"key":"ref_14","unstructured":"Hao, Y., Zhang, Y., Liu, K., He, S., Liu, Z., Wu, H., Zhao, J., Barzilay, R., and Kan, M.-Y. (August, January 30). An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vancouver, Canada."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wu, L., and Zaki, M.J. (2019). Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases. arXiv, Available online: https:\/\/arxiv.org\/abs\/1903.02188.","DOI":"10.18653\/v1\/N19-1299"},{"key":"ref_16","unstructured":"Liang, P., Jordan, M., and Klein, D. (2011, January 19\u201324). Learning dependency- based compositional semantics. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, OR, USA."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yih, W.-T., Chang, M.-W., He, X., and Gao, J. (2015, January 26\u201331). Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Beijing, China.","DOI":"10.3115\/v1\/P15-1128"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Dong, L., Lapata, M., Erk, K., and Smith, N.A. (2016, January 7\u201312). Language to Logical Form with Neural Attention. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers); Association for Computational Linguistics (ACL), Berlin, Germany.","DOI":"10.18653\/v1\/P16-1004"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jia, R., Liang, P., Erk, K., and Smith, N.A. (2016, January 7\u201312). Data Recombination for Neural Semantic Parsing. Proceedings of the Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Berlin, Germany.","DOI":"10.18653\/v1\/P16-1002"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yavuz, S., Gur, I., Su, Y., Srivatsa, M., and Yan, X. (2016, January 1\u20135). Improving Semantic Parsing via Answer Type Inference. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, TX, USA.","DOI":"10.18653\/v1\/D16-1015"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1162\/tacl_a_00157","article-title":"Imitation Learning of Agenda-based Semantic Parsers","volume":"3","author":"Berant","year":"2015","journal-title":"Trans. Assoc. Comput. Linguistics"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xu, K., Wu, L., Wang, Z., Yu, M., Chen, L., and Sheinin, V. (2018). Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model. arXiv, Available online: https:\/\/arxiv.org\/pdf\/1808.07624.pdf.","DOI":"10.18653\/v1\/D18-1110"},{"key":"ref_23","unstructured":"Berant, J., Chou, A., Frostig, R., and Percy Liang, P. (2013, January 18\u201321). Semantic parsing on freebase from question-answer pairs. Proceedings of the EMNLP; Conference on Empirical Methods in Natural Language Processing, Chicago, IL, USA."},{"key":"ref_24","first-page":"1815","article-title":"Answering Natural Language Questions by Subgraph Matching over Knowledge Graphs (Extended Abstract)","volume":"30","author":"Hu","year":"2018","journal-title":"TKDE"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liang, C., Berant, J., Le, Q., Forbus, K.D., Lao, N., Barzilay, R., and Kan, M.-Y. (2016). Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision. arXiv.","DOI":"10.18653\/v1\/P17-1003"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1007\/BF00992696","article-title":"Simple statistical gradient following algorithms for connectionist reinforcement learning","volume":"8","author":"Williams","year":"1992","journal-title":"Mach. Learn."},{"key":"ref_27","unstructured":"Ben Veyseh, A.P., Chakraborty, T., Riedl, M., and Vydiswaran, V. (2016, January 17). Cross-Lingual Question Answering Using Common Semantic Space. Proceedings of the TextGraphs-10: the Workshop on Graph-based Methods for Natural Language Processing, San Diego, CA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yao, X., Berant, J., and Van Durme, B. (2014, January 26). Freebase QA: Information Extraction or Semantic Parsing?. Proceedings of the ACL 2014 Workshop on Semantic Parsing, Baltimore, MD, USA.","DOI":"10.3115\/v1\/W14-2416"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bordes, A., Chopra, S., and Weston, J. (2014, January 25\u201329). Question Answering with Subgraph Embeddings. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1067"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Jain, S., Andreas, J., Choi, E., and Lazaridou, A. (2016, January 12\u201317). Question Answering over Knowledge Base using Factual Memory Networks. Proceedings of the NAACL Student Research Workshop, San Diego, CA, USA.","DOI":"10.18653\/v1\/N16-2016"},{"key":"ref_31","unstructured":"Weston, J., Chopra, S., and Antoine Bordes, A. (2014). Memory networks. arXiv, Available online: https:\/\/arxiv.org\/abs\/1410.3916."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Dong, L., Wei, F., Zhou, M., and Xu, K. (2015, January 26\u201331). Question Answering over Freebase with Multi-Column Convolutional Neural Networks. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Beijing, China.","DOI":"10.3115\/v1\/P15-1026"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Das, R., Zaheer, M., Reddy, S., McCallum, A., Barzilay, R., and Kan, M.-Y. (2017). Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks. arXiv, Available online: https:\/\/arxiv.org\/abs\/1704.08384.","DOI":"10.18653\/v1\/P17-2057"},{"key":"ref_34","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., and Gomez, A.N. (, January September). SIG. Proceedings of the conference on empirical methods in natural language processing, Copenhagen, Denmark, September 2017, Copenhagen, Denmark."},{"key":"ref_35","unstructured":"Seo, M., Kembhavi, A., Farhadi, A., and Hajishirzi, H. (2016). Bidirectional attention flow for machine comprehension. arXiv, Available online: https:\/\/arxiv.org\/abs\/1611.01603."},{"key":"ref_36","unstructured":"Xiong, C., Zhong, V., and Socher, S. (2016). Dynamic coattention networks for question answering. arXiv, Available online: https:\/\/arxiv.org\/abs\/1611.01604."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Grefenstette, E., Blunsom, P., de Freitas, N., and Hermann, M.K. (2014, January 26). A Deep Architecture for Semantic Parsing, Baltimore. Proceedings of the ACL 2014 Workshop on Semantic Parsing, chapter A Deep Architecture for Semantic Parsing, Baltimore, MD, USA.","DOI":"10.3115\/v1\/W14-2405"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Iyyer, M., Boyd-Graber, J., Claudino, L., Socher, R., and Hal Daume III, H. (2014, January 25\u201329). A neural \u00b4 network for factoid question answering over paragraphs. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1070"},{"key":"ref_39","unstructured":"Yu, L., Hermann, M.K., Blunsom, P., and Pulman, S. (2014). Deep Learning for Answer Sentence Selection. aXiv, Available online: https:\/\/arxiv.org\/abs\/1412.1632."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yih, W.-T., He, X., and Meek, C. (2014, January 22\u201327). Semantic Parsing for Single-Relation Question Answering. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Baltimore, MD, USA.","DOI":"10.3115\/v1\/P14-2105"},{"key":"ref_41","unstructured":"Fader, A., Soderland, S., and Etzioni, O. (2011, January 30\u201331). Identifying relations for open information extraction. Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP \u201911, Edinburgh, UK."},{"key":"ref_42","unstructured":"Qian, Q., Huang, M., Lei, J., Zhu, X., Barzilay, R., and Kan, M.-Y. (August, January 30). Linguistically Regularized LSTM for Sentiment Classification. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vancouver, Canada."},{"key":"ref_43","first-page":"99","article-title":"Chinese Sentiment Classification Method with Bi-LSTM and Grammar Rules","volume":"3","author":"Lu","year":"2019","journal-title":"Data Anal. Knowl. Discov."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_45","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv, Available online: https:\/\/arxiv.org\/abs\/1412.3555."},{"key":"ref_46","unstructured":"Baudis, P., and Jan Pichl, J. (2019, December 20). Dataset Factoid Webquestions. Available online: https:\/\/github.com\/brmson\/dataset-factoid-webquestions."},{"key":"ref_47","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv, Available online: https:\/\/arxiv.org\/abs\/1412.6980."},{"key":"ref_48","unstructured":"Bao, J., Duan, N., Yan, Z., Zhou, M., and Zhao, T. (2016, January 11\u201317). Constraint-based question answering with knowledge graph. Proceedings of the COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, Japan."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/12\/3\/45\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:04:36Z","timestamp":1760173476000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/12\/3\/45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,5]]},"references-count":48,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["fi12030045"],"URL":"https:\/\/doi.org\/10.3390\/fi12030045","relation":{},"ISSN":["1999-5903"],"issn-type":[{"type":"electronic","value":"1999-5903"}],"subject":[],"published":{"date-parts":[[2020,3,5]]}}}