Detecting and correcting typing errors in open-domain knowledge graphs using semantic representation of entities

• Author / Creator

  Caminhas, Daniel D.

• Large and accurate Knowledge Graphs (KGs) are often used as a source of structured knowledge in many natural language processing (NLP) tasks, including question-answering systems, conversational agents, information integration, named entity recognition, document ranking, among others.

  Various approaches for creating and updating KGs exist, each with its own advantages and disadvantages. Manually curated KGs can be very accurate, but require too much effort and tend to be small and domain-specific. Larger cross-domain KGs can be created automatically from unstructured or semi-structured data, but even the best methods today are error-prone. Given this trade-off between completeness and correctness, researchers have been attempting to refine KGs after they have been constructed, by adding missing knowledge or finding and correcting erroneous information.

  This thesis proposes a fully automatic method for detecting and correcting type assignments in an open-domain KG, provided that the entities in the KG are mentioned in a text corpus. Our approach consists of creating semantic representations (embeddings) of the entities in the KG that take into account how they are mentioned in the corpus and their properties in the KG itself, and using these embeddings as features for machine learning classifiers which are trained to distinguish entities of each type.

  To test our solution, we use DBpedia as the KG and Wikipedia as the text corpus, and we perform an extensive retrospective evaluation in which almost 15,000 entity-type pairs were verified by humans. Our results reveal several problems in the DBpedia ontology and led us to the conclusion that our method significantly outperforms alternative solutions for finding erroneous type assignment in knowledge graphs.

• Subjects / Keywords

  • Semantic web
  • DBpedia
  • Graph database
  • Knowledge graph
  • Information extraction
  • Error detection

• Graduation date

  Fall 2019

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-1qtb-st35

• License

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