{
  "id": "1807.01763",
  "version": "v1",
  "published": "2018-07-04T20:13:31.000Z",
  "updated": "2018-07-04T20:13:31.000Z",
  "title": "Seq2RDF: An end-to-end application for deriving Triples from Natural Language Text",
  "authors": [
    "Yue Liu",
    "Tongtao Zhang",
    "Zhicheng Liang",
    "Heng Ji",
    "Deborah L. McGuinness"
  ],
  "comment": "Proceedings of the ISWC 2018 Posters & Demonstrations",
  "categories": [
    "cs.CL",
    "cs.AI"
  ],
  "abstract": "We present an end-to-end approach that takes unstructured textual input and generates structured output compliant with a given vocabulary. Inspired by recent successes in neural machine translation, we treat the triples within a given knowledge graph as an independent graph language and propose an encoder-decoder framework with an attention mechanism that leverages knowledge graph embeddings. Our model learns the mapping from natural language text to triple representation in the form of subject-predicate-object using the selected knowledge graph vocabulary. Experiments on three different data sets show that we achieve competitive F1-Measures over the baselines using our simple yet effective approach. A demo video is included.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-07-04T20:13:31.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "natural language text",
      "end-to-end application",
      "deriving triples",
      "leverages knowledge graph embeddings",
      "independent graph language"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}