Explaining completions produced by embeddings of knowledge graphs

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

© 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.
Original languageAmerican English
Title of host publicationExplaining completions produced by embeddings of knowledge graphs
Pages324-335
Number of pages12
ISBN (Electronic)9783030297640
DOIs
StatePublished - 1 Jan 2019
Externally publishedYes
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 1 Jan 2019 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11726 LNAI
ISSN (Print)0302-9743

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period1/01/19 → …

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