| Full text | |
| Author(s): |
Ruschel, Andrey
;
Gusmao, Arthur Colombini
;
Polleti, Gustavo Padilha
;
Cozman, Fabio Gagliardi
;
KernIsberner, G
;
Ognjanovic, Z
Total Authors: 6
|
| Document type: | Journal article |
| Source: | SYMBOLIC AND QUANTITATIVE APPROACHES TO REASONING WITH UNCERTAINTY, ECSQARU 2019; v. 11726, p. 12-pg., 2019-01-01. |
| Abstract | |
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. (AU) | |
| FAPESP's process: | 16/18841-0 - Inference and learning algorithms for probabilistic logic programming |
| Grantee: | Fabio Gagliardi Cozman |
| Support Opportunities: | Research Grants - Research Partnership for Technological Innovation - PITE |