{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:29:55Z","timestamp":1772252995886,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T00:00:00Z","timestamp":1639699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The commonly-used large-scale knowledge bases have been facing challenges in open domain question answering tasks which are caused by the loose knowledge association and weak structural logic of triplet-based knowledge. To find a way out of this dilemma, this work proposes a novel metaknowledge enhanced approach for open domain question answering. We design an automatic approach to extract metaknowledge and build a metaknowledge network from Wiki documents. For the purpose of representing the directional weighted graph with hierarchical and semantic features, we present an original graph encoder GE4MK to model the metaknowledge network. Then, a metaknowledge enhanced graph reasoning model MEGr-Net is proposed for question answering, which aggregates both relational and neighboring interactions comparing with R-GCN and GAT. Experiments have proved the improvement of metaknowledge over main-stream triplet-based knowledge. We have found that the graph reasoning models and pre-trained language models also have influences on the metaknowledge enhanced question answering approaches.<\/jats:p>","DOI":"10.3390\/s21248439","type":"journal-article","created":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T02:40:32Z","timestamp":1639968032000},"page":"8439","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Metaknowledge Enhanced Open Domain Question Answering with Wiki Documents"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7087-1069","authenticated-orcid":false,"given":"Shukan","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing 211189, China"},{"name":"School of Electronic Engineering, PLA Naval University of Engineering, Wuhan 430033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7468-8635","authenticated-orcid":false,"given":"Ruilin","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, PLA Naval University of Engineering, Wuhan 430033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, PLA Naval University of Engineering, Wuhan 430033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingjie","family":"Li","sequence":"additional","affiliation":[{"name":"Ship Comprehensive Test and Training Base, PLA Naval University of Engineering, Wuhan 430033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiming","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, PLA Naval University of Engineering, Wuhan 430033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., and Ives, Z. 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