Name
Abstract | ||
|---|---|---|
The human mind is a powerful multifunctional knowledge storage and management system that performs generalization, type inference, anomaly detection, stereotyping, and other tasks. A dynamic KR system that appropriately profiles over sparse inputs to provide complete expectations for unknown facets can help with all these tasks. In this paper, we introduce the task of profiling, inspired by theories and findings in social psychology about the potential of profiles for reasoning and information processing. We describe two generic state-of-the-art neural architectures that can be easily instantiated as profiling machines to generate expectations and applied to any kind of knowledge to fill gaps. We evaluate these methods against Wikidata and crowd expectations, and compare the results to gain insight in the nature of knowledge captured by various profiling methods. We make all code and data available to facilitate future research. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Artificial Intelligence | Anomaly detection,Information processing,Profiling (computer programming),Computer science,Type inference,Artificial intelligence,Management system,Machine learning |
DocType | Volume | Citations |
Journal | abs/1810.00782 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Filip Ilievski | 1 | 12 | 7.63 |
| Eduard H. Hovy | 2 | 7450 | 663.27 |
| Qizhe Xie | 3 | 2 | 1.04 |
| Piek Vossen | 4 | 387 | 61.59 |