Guest-editorial: Special issue title on engineering and management of IDTs for knowledge management systems

An ongoing and main challenge for intelligent decision technologies is the need to support knowledgeintensive tasks that usually are surging in multiple domains of applications such as manufacture [13], finance and insurance [10], and generic knowledge-based service business [12]. Engineering and management systems have relied on knowledge generated from several related areas including Decision Support Systems, Artificial Intelligence and Operations Research. Most recently, in the last decade, a business management perspective realized through Knowledge Management (KM) approach has been incorporated to this research stream driven by a knowledge-based services economy [9,11]. Thus, a new type of IT system called Knowledge Management System (KMS) [2] has emerged to leverage “professional and managerial activities by focusing on creating, gathering, organizing, and disseminating an organization’s “knowledge” as opposed to “information” or “data” (idem, p. 1). While KMS are engineered and managed by using multiple IT, we consider relevant the development of KMS based on intelligent decision technologies and the enhancement of the decision-making process [6]. Through following the seminal directions [7,8] established by the eminent AI scientists Herbert A. Simon (1916–2001) and Alan Newell (1927–1992), and the system’s emergent property established by the Theory of Systems [1,4], we support also the notion of a “distinct computer systems level, lying immediately above the symbol level, which is characterized by knowledge as the medium and the principle of rationality as the law of behavior” (Newell, p. 7) as a core conceptualization for the realization of such KMS. We believe that the five invited and peer-reviewed research papers in this special issue in “Engineering and Management of IDTs for Knowledge Management Systems”, advance our scientific knowledge on the state of the art of intelligent knowledge management systems in a context of decision-making process. One research paper reports an improved algorithm for an automatic joint of knowledge stored via ontologies. Three another research papers analyze deeply the KMS support challenges and the KMS emergent simulation-based design architectures and paradigms. Finally, a fifth paper, reviews the state of the art of KMS focused on the particular problem of improving the utilization of standards and models of process in the context of software and systems engineering. In first paper, titled “Automatic Fusion of knowledge stored in Ontologies”, Dr. Alma-Delia Cueva and Professor Adolfo Guzmán-Arenas (Computer Research Center, Instituto Politecnico Nacional, México), investigate the knowledge fusion problem which is a seamless process in human beings. However, for an automated system, authors report that algorithms of ontologies fusion lack of critical features such as the processing of synonyms, homonyms, redundancies, apparent contradictions, and inconsistencies. Authors, consequently presents a new method for ontology merging (OM), its algorithm and implementation to join two ontologies (obtained from Web documents) in an automatic fashion (without human intervention), producing a third ontology, and taking into account the problematic issues aforementioned, with a delivering result close to a human being performance. This paper contributes to the design of intelligent KMS with such a new method and algorithm. In the second paper, titled “Challenging Computer Software Frontiers and the Human Resistance to