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The purpose of this study was to determine the extent to which teaching staff cooperated with librarians in collection development, specifically in relation to electronic resources, and to identify barriers they encountered while performing collection development activities.

A mixed methods approach was adopted for the study. Quantitative and qualitative techniques of data collection and analysis were used to obtain a comprehensive understanding of the research topic. Data were gathered through a self-administered questionnaire and interviews. A total of 149 faculty members completed the questionnaire, yielding a response rate of 51.2%, while 16 library staff members were interviewed to obtain qualitative data.

The majority of the teaching staff who participated in the study affirmed that they had cooperated with subject librarians in collection development. A high percentage (62.4%) of the faculty members had collaborated with subject librarians in collection development activities. Only 37.6% of the faculty members had not participated in collection development activities with subject librarians to acquire library electronic resources. According to faculty members, some of the main challenges affecting collection development at the University of Namibia were a lack of catalogues for electronic resources and a lack of lists of titles from vendors. Moreover, librarians were not always available to assist faculty members. It is recommended that faculty members be part of the process of selecting materials and that a good relationship be fostered between librarians and faculty members to bring value to collection development activities.

Collection development in respect of electronic resources is a complex process to be undertaken by a single entity and, therefore, requires the collaboration of all stakeholders involved. In the case of institutions of higher learning, these stakeholders include faculty, librarians and vendors. The emergence of a variety of e-resources demands a meticulous strategy on the part of libraries to ensure they can offer a wide range of up-to-date and accurate resources that meet the evolving needs of their users. To the best of the authors’ knowledge, studies that are similar to this one have not been conducted in Namibia before. This case study presents useful findings and lessons on faculty–librarian cooperation for effective collection development, not only at the University of Namibia library but also at other academic libraries in economies with similar characteristics.

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.