Name
Title | ||
|---|---|---|
Characterising Semantic Relatedness using Interpretable Directions in Conceptual Spaces. |
Abstract | ||
|---|---|---|
Various applications, such as critique-based recommendation systems and analogical classifiers, rely on knowledge of how different entities relate. In this paper, we present a methodology for identifying such semantic relationships, by interpreting them as qualitative spatial relations in a conceptual space. In particular, we use multi-dimensional scaling to induce a conceptual space from a relevant text corpus and then identify directions that correspond to relative properties such as "more violent than" in an entirely unsupervised way. We also show how a variant of FOIL is able to learn natural categories from such qualitative representations, by simulating a fortiori inference, an important pattern of commonsense reasoning. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.3233/978-1-61499-419-0-243 | Frontiers in Artificial Intelligence and Applications |
Field | DocType | Volume |
Recommender system,Spatial relation,Semantic similarity,Inference,Computer science,Commonsense reasoning,Text corpus,Conceptual space,Natural language processing,Artificial intelligence,Machine learning | Conference | 263 |
ISSN | Citations | PageRank |
0922-6389 | 6 | 0.49 |
References | Authors | |
15 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joaquín Derrac | 1 | 2552 | 64.42 |
| Steven Schockaert | 2 | 583 | 57.95 |