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Although acknowledged as a principal dimension in the context of text mining, time has yet to be formally incorporated into the process of visually representing the relationships between keywords in a knowledge domain. This paper aims to develop and validate the feasibility of adding temporal knowledge to a concept map via pair-wise temporal analysis (PTA).

The paper presents a temporal trend detection algorithm – vector space model – designed to use objective quantitative pair-wise temporal operators to automatically detect co-occurring hot concepts. This PTA approach is demonstrated and validated without loss of generality for a spectrum of information technologies.

The rigorous validation study shows that the resulting temporal assessments are highly correlated with subjective assessments of experts (n = 136), exhibiting substantial reliability-of-agreement measures and average predictive validity above 85 per cent.

Using massive amounts of textual documents available on the Web to first generate a concept map and then add temporal knowledge, the contribution of this work is emphasized and magnified against the current growing attention to big data analytics.

This paper proposes a novel knowledge discovery method to improve a text-based concept map (i.e. semantic graph) via detection and representation of temporal relationships. The originality and value of the proposed method is highlighted in comparison to other knowledge discovery methods.

Citizens Advice Bureau (SHIL in Hebrew) is an information and referral service dedicated to serving the needs of citizens by providing easy access to information about citizenship rights and obligations. Many people turn to the offices of SHIL either for help or to volunteer as advisors. This study seeks to examine the information seeking behavior of SHIL volunteers supplying information services to citizens.

The theoretical foundations of the study are based on two existing models of information searching related to everyday life problems, Foster's non‐linear model of information seeking behavior and Bates's berry‐picking approach. This research employs a qualitative method. A total of 35 advisors in different SHIL branches were interviewed and the content of the interviews was analyzed, mapped and organized into categories by using concepts and terms revealed in the data.

Findings show that volunteers at SHIL search information in a way that integrates the two models mentioned above, the berry‐picking model and the non‐linear model. In addition, findings point to difficulties that the advisors face in solving problems of the clients. These difficulties are connected with the different aspects relating to the flow of information both within and outside the organization and with organizational and administrative aspects at SHIL.

The information seeking behavior of volunteers acting as providers of information services has yet to be investigated at length and the understanding of their information behavior can be of value, since volunteering carries great importance in a democracy.

This chapter uses the poliheuristic theory of decision-making to analyze the decisions of Israeli prime minister Benjamin Netanyahu. The study examines a series of Netanyahu’s decisions regarding the peace process during 1996–2014. Using Applied Decision Analysis (ADA), this study demonstrates that Netanyahu ruled out alternatives that failed to satisfy alternatives on the non-compensatory decision dimension – his political survivability. The prime minister’s final choices were made from the remaining options according to their ability to maximize net benefits with respect to Netanyahu’s ideological concerns.