4 research outputs found
Evaluation of Analogical Inferences Formed from Automatically Generated Representations of Scientific Publications
Humans regularly exploit analogical reasoning to generate potentially novel and useful inferences. We outline the Dr Inventor model that identifies analogies between research publications, describing recent work to evaluate the inferences that are generated by the system. Its inferences, in the form of subjectverb-object triples, can involve arbitrary combinations of source and target information. We evaluate three approaches to assess the quality of inferences. Firstly, we explore an n-gram based approach (derived from the Dr Inventor corpus). Secondly, we use ConceptNet as a basis for evaluating inferences. Finally, we explore the use of Watson Concept Insights (WCI) to support our inference evaluation process. Dealing with novel inferences arising from an ever growing corpus is a central concern throughout
Evaluation of Analogical Inferences Formed from Automatically Generated Representations of Scientific Publications
Humans regularly exploit analogical reasoning to generate potentially novel and useful inferences. We outline the Dr Inventor model that identifies analogies between research publications, describing recent work to evaluate the inferences that are generated by the system. Its inferences, in the form of subjectverb-object triples, can involve arbitrary combinations of source and target information. We evaluate three approaches to assess the quality of inferences. Firstly, we explore an n-gram based approach (derived from the Dr Inventor corpus). Secondly, we use ConceptNet as a basis for evaluating inferences. Finally, we explore the use of Watson Concept Insights (WCI) to support our inference evaluation process. Dealing with novel inferences arising from an ever growing corpus is a central concern throughout
Characteristics of Pro-c Analogies and Blends between Research Publications
Dr Inventor is a tool that aims to enhance the professional (Pro-c) creativity of researchers by suggesting novel hypotheses, arising from analogies between publications. Dr Inventor processes original research documents using a combination of lexical analysis and cognitive computation to identify novel comparisons that suggest new research hypotheses, with the objective of supporting a novel research publication. Research on analogical reasoning strongly suggests that the value of analogy-based comparisons depends primarily on the strength of the mapping (or counterpart projection) between the two analogs. An evaluation study of a number of computer generated comparisons attracted creativity ratings from a group of practising researchers. This paper explores a variety of theoretically motivated metrics operating on different conceptual spaces, identifying some weak associations with user's creativity ratings. Surprisingly, our results show that metrics focused on the mapping appear to have less relevance to creativity than metrics assessing the inferences (blended space). This paper includes a brief description of a research project currently exploring the best research hypothesis generated during this evaluation. Finally, we explore PCA as a means of specifying a combined multiple metrics from several blending spaces as a basis for detecting comparisons to enhance researchers’ creativity
Neural Analogical Matching
Analogy is core to human cognition. It allows us to solve problems based on
prior experience, it governs the way we conceptualize new information, and it
even influences our visual perception. The importance of analogy to humans has
made it an active area of research in the broader field of artificial
intelligence, resulting in data-efficient models that learn and reason in
human-like ways. While cognitive perspectives of analogy and deep learning have
generally been studied independently of one another, the integration of the two
lines of research is a promising step towards more robust and efficient
learning techniques. As part of a growing body of research on such an
integration, we introduce the Analogical Matching Network: a neural
architecture that learns to produce analogies between structured, symbolic
representations that are largely consistent with the principles of
Structure-Mapping Theory.Comment: AAAI versio