armando-vieira-jointly-embedding-knowledge.json

{
  "description": "Targeting knowledge graphs completion is a recent paradigm that allow\nextraction of new relations (facts) from existing knowledge graphs like\nFreebase or GeneOntology. Word embeddings represents each entity into a\nlow dimensional space and the relationships as vectorial transformations\nwhich has the advantage of making the search space continuous. This\nallows to encode the entities and transformations with global\ninformation from the entire graph. On the other hand, word embedding\napproaches, like word2vec, extracted from unlabeled text allows\nrepresentations of words as vectors, although it doesn't allow to\nextract relationships . By careful alignment of entities from free text\nwith a knowledge graph it is possible to combine both approaches and\njointly extract new knowledge through relationships between entities and\nwords / phrases. We will show results from applying this technology to\nbiomedical data.\n",
  "duration": 2031,
  "language": "eng",
  "recorded": "2015-06-21",
  "speakers": [
    "Armando Vieira"
  ],
  "summary": "Recent advances in combining structured graph data with textual data\nusing embedding word representations from a large corpus of\nunlabelled data. This allows to expand the knowledge base graph and\nextract complex semantic relationships.",
  "thumbnail_url": "https://i.ytimg.com/vi/UAMGMMqjHuY/hqdefault.jpg",
  "title": "Jointly Embedding knowledge from large graph databases with textual data using deep learning",
  "videos": [
    {
      "type": "youtube",
      "url": "https://www.youtube.com/watch?v=UAMGMMqjHuY"
    }
  ]
}