Dear Collegues,

Currently, we all live in an information society in which knowledge plays an unprecedented role in our professional but also personal lives. Organizations have become conscious that their corporate assets are the boundaries that set them apart from their competitors. In the past, assets were usually coupled with product lines, frameworks, etc., but this is no longer true. Assets, as relevant and valuable units of an organization, not only include reusable code snippets or executable components, product lines or frameworks. All information units relevant for an organization’s actual, tactical or strategical views are considered assets: specifically, knowledge assets ready to be reused. This includes business process descriptions, business models, software designs, requirements, personnel structures and classifications, technology failures, support problems, client experiences, competitor analyses, business intelligence, images, lessons learned, human resources experiences, etc. Day by day, there is an increased interest in maximizing the value of an organization’s knowledge, and knowledge has become one of the most (if not the most) valuable assets of the modern organization.

In this context, knowledge reuse has become important for industries because it increases the benefits of productivity. These benefits can be enjoyed by capitalizing on previous experiences and avoiding duplicated solutions. Currently, a significant problem companies face is the variety of relevant information available. Therefore, it is a challenge for them to transform information into knowledge, to represent any kind of knowledge within a common repository, and finally, to offer reuse methods to users: thus, a retrieval approach is needed to support the diverse assets of the companies and the knowledge reuse methods.

This Special Issue intends to focus on several aspects around knowledge retrieval and reuse (KR & R), including:

• Clarifying the difference between information and knowledge;
• Modern algorithms for information and knowledge retrieval;
• Artificial intelligence and KR & R: how machine learning and user feedback affect the retrieval and reuse of all sorts of knowledge artifacts;
• Applications of KR & R;
• Interactive retrieval and reuse: chat boxes/virtual assistants;
• Beyond the format and domain limit: specific search tools for models, blueprints, molecules, graphs, etc.;
• Systems engineering retrieval and reuse (solutions for archiving, model-based systems engineering, traceability discovery, pattern recognition and matching algorithms, etc.);
• Software engineering retrieval and reuse (requirements reuse, code retrieval and organization, software reuse);
• Information science retrieval and reuse (thesaurus generation, automatic classification of documentation, information quality, etc.);
• Ontology and semantic domain (knowledge reuse through ontology reasoning, generation of ontologies using artificial intelligence and machine learning, etc.).

Prof. Dr. Eugenio Parra
Prof. Dr. Juan Llorens
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• knowledge reuse
• retrieval
• systems engineering
• software engineering
• artificial intelligence