Explaining completions produced by embeddings of knowledge graphs

Andrey Ruschel, Arthur Colombini Gusmão, Gustavo Padilha Polleti, Fabio Gagliardi Cozman

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferencia

Resumen

© Springer Nature Switzerland AG 2019. Advanced question answering typically employs large-scale knowledge bases such as DBpedia or Freebase, and are often based on mappings from entities to real-valued vectors. These mappings, called embeddings, are accurate but very hard to explain to a human subject. Although interpretability has become a central concern in machine learning, the literature so far has focused on non-relational classifiers (such as deep neural networks); embeddings, however, require a whole range of different approaches. In this paper, we describe a combination of symbolic and quantitative processes that explain, using sequences of predicates, completions generated by embeddings.
Idioma originalInglés estadounidense
Título de la publicación alojadaExplaining completions produced by embeddings of knowledge graphs
Páginas324-335
Número de páginas12
ISBN (versión digital)9783030297640
DOI
EstadoPublicada - 1 ene 2019
Publicado de forma externa
EventoLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duración: 1 ene 2019 → …

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen11726 LNAI
ISSN (versión impresa)0302-9743

Conferencia

ConferenciaLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Período1/01/19 → …

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    Ruschel, A., Gusmão, A. C., Polleti, G. P., & Cozman, F. G. (2019). Explaining completions produced by embeddings of knowledge graphs. En Explaining completions produced by embeddings of knowledge graphs (pp. 324-335). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11726 LNAI). https://doi.org/10.1007/978-3-030-29765-7_27