Exploring relation types for literature-based discovery

Objective Literature-based discovery (LBD) aims to identify “hidden knowledge” in the medical literature by: (1) analyzing documents to identify pairs of explicitly related concepts (terms), then (2) hypothesizing novel relations between pairs of unrelated concepts that are implicitly related via a shared concept to which both are explicitly related. Many LBD approaches use simple techniques to identify semantically weak relations between concepts, for example, document co-occurrence. These generate huge numbers of hypotheses, difficult for humans to assess. More complex techniques rely on linguistic analysis, for example, shallow parsing, to identify semantically stronger relations. Such approaches generate fewer hypotheses, but may miss hidden knowledge. The authors investigate this trade-off in detail, comparing techniques for identifying related concepts to discover which are most suitable for LBD. Materials and methods A generic LBD system that can utilize a range of relation types was developed. Experiments were carried out comparing a number of techniques for identifying relations. Two approaches were used for evaluation: replication of existing discoveries and the “time slicing” approach.1 Results Previous LBD discoveries could be replicated using relations based either on document co-occurrence or linguistic analysis. Using relations based on linguistic analysis generated many fewer hypotheses, but a significantly greater proportion of them were candidates for hidden knowledge. Discussion and Conclusion The use of linguistic analysis-based relations improves accuracy of LBD without overly damaging coverage. LBD systems often generate huge numbers of hypotheses, which are infeasible to manually review. Improving their accuracy has the potential to make these systems significantly more usabl

Using the Inquiry-based Learning Approach to Enhance Student Innovativeness: A Conceptual Model

Individual innovativeness has become one of the most important employability skills for university graduates. In this paper, we focus on how students could be better prepared to be innovative in the workplace, and we argue that inquiry-based learning (IBL) – a pedagogical approach in which students follow the inquiry-based processes used by scientists to construct knowledge – can be effective for this purpose. Drawing on research which examines the social and cognitive micro-foundations of innovative behaviour, we develop a conceptual model that links IBL and student innovativeness, and introduce three teacher-controlled design elements that can influence the strength of this relationship, namely whether an inquiry is open or closed, discovery-focused or information focused and individual or teambased. We argue that an open, discovery-focused and team-based inquiry offers the greatest potential for enhancing students’ skills in innovation. This paper has several implications for higher education research and practice

Exploring the Evolution of Node Neighborhoods in Dynamic Networks

Dynamic Networks are a popular way of modeling and studying the behavior of evolving systems. However, their analysis constitutes a relatively recent subfield of Network Science, and the number of available tools is consequently much smaller than for static networks. In this work, we propose a method specifically designed to take advantage of the longitudinal nature of dynamic networks. It characterizes each individual node by studying the evolution of its direct neighborhood, based on the assumption that the way this neighborhood changes reflects the role and position of the node in the whole network. For this purpose, we define the concept of \textit{neighborhood event}, which corresponds to the various transformations such groups of nodes can undergo, and describe an algorithm for detecting such events. We demonstrate the interest of our method on three real-world networks: DBLP, LastFM and Enron. We apply frequent pattern mining to extract meaningful information from temporal sequences of neighborhood events. This results in the identification of behavioral trends emerging in the whole network, as well as the individual characterization of specific nodes. We also perform a cluster analysis, which reveals that, in all three networks, one can distinguish two types of nodes exhibiting different behaviors: a very small group of active nodes, whose neighborhood undergo diverse and frequent events, and a very large group of stable nodes

Optimal experience and optimal identity: a multinational study of the associations between flow and social identity

Eudaimonistic identity theory posits a link between activity and identity, where a selfdefining activity promotes the strength of a person's identity. An activity engaged in with high enjoyment, full involvement, and high concentration can facilitate the subjective experience of flow. In the present paper, we hypothesized in accordance with the theory of psychological selection that beyond the promotion of individual development and complexity at the personal level, the relationship between flow and identity at the social level is also positive through participation in self-defining activities. Three different samples (i.e., American, Chinese, and Spanish) filled in measures for flow and social identity, with reference to four previously self-reported activities, characterized by four different combinations of skills (low vs. high) and challenges (low vs. high). Findings indicated that flow was positively associated with social identity across each of the above samples, regardless of participants' gender and age. The results have implications for increasing social identity via participation in self-defining group activities that could facilitate flow

Designing algorithms to aid discovery by chemical robots

Recently, automated robotic systems have become very efficient, thanks to improved coupling between sensor systems and algorithms, of which the latter have been gaining significance thanks to the increase in computing power over the past few decades. However, intelligent automated chemistry platforms for discovery orientated tasks need to be able to cope with the unknown, which is a profoundly hard problem. In this Outlook, we describe how recent advances in the design and application of algorithms, coupled with the increased amount of chemical data available, and automation and control systems may allow more productive chemical research and the development of chemical robots able to target discovery. This is shown through examples of workflow and data processing with automation and control, and through the use of both well-used and cutting-edge algorithms illustrated using recent studies in chemistry. Finally, several algorithms are presented in relation to chemical robots and chemical intelligence for knowledge discovery