@inproceedings{sudhahar-etal-2019-reasoning,
    title = "Reasoning Over Paths via Knowledge Base Completion",
    author = "Sudhahar, Saatviga  and
      Pierleoni, Andrea  and
      Roberts, Ian",
    editor = "Ustalov, Dmitry  and
      Somasundaran, Swapna  and
      Jansen, Peter  and
      Glava{\v{s}}, Goran  and
      Riedl, Martin  and
      Surdeanu, Mihai  and
      Vazirgiannis, Michalis",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/D19-5320/",
    doi = "10.18653/v1/D19-5320",
    pages = "164--171",
    abstract = "Reasoning over paths in large scale knowledge graphs is an important problem for many applications. In this paper we discuss a simple approach to automatically build and rank paths between a source and target entity pair with learned embeddings using a knowledge base completion model (KBC). We assembled a knowledge graph by mining the available biomedical scientific literature and extracted a set of high frequency paths to use for validation. We demonstrate that our method is able to effectively rank a list of known paths between a pair of entities and also come up with plausible paths that are not present in the knowledge graph. For a given entity pair we are able to reconstruct the highest ranking path 60{\%} of the time within the top 10 ranked paths and achieve 49{\%} mean average precision. Our approach is compositional since any KBC model that can produce vector representations of entities can be used."
}