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This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase.

In particular, this study extracts new predicates from four types of data sources, namely, Web texts, Document Object Model (DOM) trees, existing KBs and query stream to augment the ontology of the existing KB (i.e. Freebase). In addition, a graph-based approach to conduct better truth discovery for multi-valued predicates is also proposed.

Empirical studies demonstrate the effectiveness of the approaches presented in this study and the potential of GrandBase. The future research directions regarding GrandBase construction and extension has also been discussed.

To revolutionize our modern society by using the wisdom of Big Data, considerable KBs have been constructed to feed the massive knowledge-driven applications with Resource Description Framework triples. The important challenges for KB construction include extracting information from large-scale, possibly conflicting and different-structured data sources (i.e. the knowledge extraction problem) and reconciling the conflicts that reside in the sources (i.e. the truth discovery problem). Tremendous research efforts have been contributed on both problems. However, the existing KBs are far from being comprehensive and accurate: first, existing knowledge extraction systems retrieve data from limited types of Web sources; second, existing truth discovery approaches commonly assume each predicate has only one true value. In this paper, the focus is on the problem of generating actionable knowledge from Big Data. A system is proposed, which consists of two phases, namely, knowledge extraction and truth discovery, to construct a broader KB, called GrandBase.

This article analyses the effect of bank concentration on women's political empowerment in 80 developing countries over the period 2004–2020.

Banking concentration (BC) is measured by the assets held by the three largest commercial banks as a percentage of total commercial bank assets in a country. We use several indices to measure political empowerment, namely: the political empowerment index, composed of three indices (i.e. the women's civil liberties index, the women's participation in civil society index and the women's political participation index). The empirical evidence is based on the Ordinary Least Squares (OLS) and Fixed Effects (FE) techniques.

The following findings are established. Banking concentration reduces women's political empowerment. Furthermore, information sharing offices (i.e. public credit registries and private credit bureaus) mitigate the negative effect of bank concentration on women’s political empowerment. Information sharing thresholds that are needed to completely dampen the negative effect of bank concentration on women’s political empowerment are provided. Policy implications are discussed, notably: (1) that governments in developing countries increase competition by easing barriers to entry for potential banks, to facilitate the transition from confiscatory concentration to distributive concentration favorable to all stakeholders; and (2) information sharing offices should be consolidated beyond the established thresholds in order to completely crowd-out the unfavorable effect of bank concentration of women’s political empowerment.

The paper provides new empirical evidence that helps to advance the debate on the effects of banking concentration and information sharing in the banking sector on women's political empowerment in developing countries.