Authors:
Wenguan Huang
and
Xudong Luo
Affiliation:
Sun Yat-sen University, China
Keyword(s):
Knowledge Representation, Commonsense Reasoning, ConceptNet, Natural Language Processing.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation and Reasoning
;
Knowledge-Based Systems
;
Methodologies and Methods
;
Natural Language Processing
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
;
Symbolic Systems
Abstract:
One of the biggest drawbacks of nowadays AI reasoning systems is their lack of commonsense. To address the issue, some commonsense knowledge bases and a bunch of reasoning mechanisms with them have been developed to tackle this problem. However, most of them concentrate on the relation between entities (e.g., "cat" and "fish"), but few discuss the relation between predicates (e.g., "angry" and "shout"), which fall into a deeper level of commonsense. To the end, in this paper, we develop a commonsense reasoning framework, which focuses on this type of commonsense knowledge. More specifically, first we give a formal definition of this kind of commonsense. Then we construct a set of knowledge by extending the predicate set of ConceptNet, and apply information extraction technique to capture them from corpus. Finally, to evaluate our framework, we conduct experiments against a part of the Winograd Schema Challenge, which, its author claimed, is an alternative of Turing Test. The res
ult of our experiments confirms the effectiveness of our framework.
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