Corpus ID: 135464432

Building Knowledge Base through Deep Learning Relation Extraction and Wikidata

  title={Building Knowledge Base through Deep Learning Relation Extraction and Wikidata},
  author={P. Subasic and Hongfeng Yin and X. Lin},
  booktitle={AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering},
  • P. Subasic, Hongfeng Yin, X. Lin
  • Published in
    AAAI Spring Symposium…
  • Computer Science
  • Many AI agent tasks require domain specific knowledge graph (KG) that is compact and complete. We present a methodology to build domain specific KG by merging output from deep learning-based relation extraction from free text and existing knowledge database such as Wikidata. We first form a static KG by traversing knowledge database constrained by domain keywords. Very large high-quality training data set is then generated automatically by matching Common Crawl data with relation keywords… CONTINUE READING

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    DBpedia - A large-scale, multilingual knowledge base extracted from Wikipedia
    • 1,723
    • Open Access
    Freebase: a collaboratively created graph database for structuring human knowledge
    • 2,830
    • Open Access
    Modeling Relations and Their Mentions without Labeled Text
    • 745
    • Open Access
    CYC: a large-scale investment in knowledge infrastructure
    • 2,097
    • Open Access
    Relation Classification via Convolutional Deep Neural Network
    • 919
    • Open Access
    Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification
    • Peng Zhou, Wei Shi, +4 authors Bo Xu
    • Computer Science
    • 2016
    • 604
    • Open Access
    Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
    • 504
    • Open Access