{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:39:07Z","timestamp":1778081947284,"version":"3.51.4"},"reference-count":48,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,3,21]],"date-time":"2021-03-21T00:00:00Z","timestamp":1616284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Social Research Project on Economic Development in Liaoning Province","award":["lslybkt-022"],"award-info":[{"award-number":["lslybkt-022"]}]},{"name":"Natural Science Foundation of Liaoning Province, China","award":["20180550921 and 2019-ZD-0175"],"award-info":[{"award-number":["20180550921 and 2019-ZD-0175"]}]},{"name":"Scientific Research Fund Project of the Education Department of Liaoning Province","award":["LJYT201906"],"award-info":[{"award-number":["LJYT201906"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Question-answering systems based on knowledge graphs are extremely challenging tasks in the field of natural language processing. Most of the existing Chinese Knowledge Base Question Answering(KBQA) can only return the knowledge stored in the knowledge base by extractive methods. Nevertheless, this processing does not conform to the reading habits and cannot solve the Out-of-vocabulary(OOV) problem. In this paper, a new generative question answering method based on knowledge graph is proposed, including three parts of knowledge vocabulary construction, data pre-processing, and answer generation. In the word list construction, BiLSTM-CRF is used to identify the entity in the source text, finding the triples contained in the entity, counting the word frequency, and constructing it. In the part of data pre-processing, a pre-trained language model BERT combining word frequency semantic features is adopted to obtain word vectors. In the answer generation part, one combination of a vocabulary constructed by the knowledge graph and a pointer generator network(PGN) is proposed to point to the corresponding entity for generating answer. The experimental results show that the proposed method can achieve superior performance on WebQA datasets than other methods.<\/jats:p>","DOI":"10.3390\/info12030136","type":"journal-article","created":{"date-parts":[[2021,3,21]],"date-time":"2021-03-21T22:00:37Z","timestamp":1616364037000},"page":"136","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Research on Automatic Question Answering of Generative Knowledge Graph Based on Pointer Network"],"prefix":"10.3390","volume":"12","author":[{"given":"Shuang","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Dalian Minzu University, Dalian 116600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nannan","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Dalian Minzu University, Dalian 116600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaqian","family":"Ge","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Dalian Minzu University, Dalian 116600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9517-1157","authenticated-orcid":false,"given":"Niko","family":"Luka\u010d","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science, University of Maribor, SI-2000 Maribor, Slovenia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1093\/mind\/LIX.236.433","article-title":"Computing Machinery and Intelligence","volume":"59","author":"Turing","year":"1950","journal-title":"Mind"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s11023-008-9094-6","article-title":"Turing\u2019s Responses to Two Objections","volume":"18","author":"Abramson","year":"2008","journal-title":"Minds Mach."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Fedushko, S., Ustyianovych, T., and Gregus, M. 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