Knowledge-based question answering

被引:0
|
作者
Rinaldi, F [1 ]
Dowdall, J
Hess, M
Mollá, D
Schwitter, R
Kaljurand, K
机构
[1] Univ Zurich, Inst Computat Linguist, Zurich, Switzerland
[2] Macquarie Univ, Ctr Language Technol, Sydney, NSW 2109, Australia
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Large amounts of technical documentation axe available in machine readable form, however there is a lack of effective ways to access them. In this paper we propose an approach based on linguistic techniques, geared towards the creation of a domain-specific Knowledge Base, starting from the available technical documentation. We then discuss an effective way to access the information encoded in the Knowledge Base. Given a user question phrased in natural language the system is capable of retrieving the encoded semantic information that most closely matches the user input, and present it by highlighting the textual elements that were used to deduct it.
引用
收藏
页码:785 / 792
页数:8
相关论文
共 50 条
  • [41] ViQuAE, a Dataset for Knowledge-based Visual Question Answering about Named Entities
    Lerner, Paul
    Ferret, Olivier
    Guinaudeau, Camille
    Le Borgne, Herve
    Besancon, Romaric
    Moreno, Jose G.
    Melgarejo, Jesus Lovon
    PROCEEDINGS OF THE 45TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '22), 2022, : 3108 - 3120
  • [42] MKEAH: Multimodal knowledge extraction and accumulation based on hyperplane embedding for knowledge-based visual question answering
    Heng ZHANG
    Zhihua WEI
    Guanming LIU
    Rui WANG
    Ruibin MU
    Chuanbao LIU
    Aiquan YUAN
    Guodong CAO
    Ning HU
    虚拟现实与智能硬件(中英文), 2024, 6 (04) : 280 - 291
  • [43] FacGPT: An Effective and Efficient Method for Evaluating Knowledge-Based Visual Question Answering
    Cheng, Sirui
    Zhang, Siyu
    Wu, Jiayi
    Lan, Muchen
    Sun, Yaoru
    NATURAL LANGUAGE PROCESSING AND CHINESE COMPUTING, PT I, NLPCC 2024, 2025, 15359 : 201 - 214
  • [44] VIG: Visual Information-Guided Knowledge-Based Visual Question Answering
    Liu, Heng
    Wang, Boyue
    Sun, Yanfeng
    Li, Xiaoyan
    Hu, Yongli
    Yin, Baocai
    PROCEEDINGS OF THE 2024 27 TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN, CSCWD 2024, 2024, : 1086 - 1091
  • [45] Prompting Large Language Models with Knowledge-Injection for Knowledge-Based Visual Question Answering
    Hu, Zhongjian
    Yang, Peng
    Liu, Fengyuan
    Meng, Yuan
    Liu, Xingyu
    BIG DATA MINING AND ANALYTICS, 2024, 7 (03): : 843 - 857
  • [46] Improving and Diagnosing Knowledge-Based Visual Question Answering via Entity Enhanced Knowledge Injection
    Garcia-Olano, Diego
    Onoe, Yasumasa
    Ghosh, Joydeep
    COMPANION PROCEEDINGS OF THE WEB CONFERENCE 2022, WWW 2022 COMPANION, 2022, : 705 - 715
  • [47] Automatic Chinese knowledge-based question answering by the MGBA-LSTM-CNN model
    Liu, Wenyuan
    Fan, Mingliang
    Feng, Kai
    Guo, Dingding
    AI COMMUNICATIONS, 2023, 36 (02) : 93 - 110
  • [48] A data-centric way to improve entity linking in knowledge-based question answering
    Liu, Shuo
    Zhou, Gang
    Xia, Yi
    Wu, Hao
    Li, Zhufeng
    PEERJ COMPUTER SCIENCE, 2023, 9 : 1 - 20
  • [49] Inner Knowledge-based Img2Doc Scheme for Visual Question Answering
    Li, Qun
    Xiao, Fu
    Bhanu, Bir
    Sheng, Biyun
    Hong, Richang
    ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS, 2022, 18 (03)
  • [50] Cross-modality Multiple Relations Learning for Knowledge-based Visual Question Answering
    Wang, Yan
    Li, Peize
    Si, Qingyi
    Zhang, Hanwen
    Zang, Wenyu
    Lin, Zheng
    Fu, Peng
    ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS, 2024, 20 (03)