An Ontology-Driven Recommender System for Engineering Projects

Knowledge and information resources in enterprises are rapidly growing. The International Data Corporation (IDC) forecasts that significant yearly growth of data will result that the so-called global datasphere will have grown to 163 zettabytes (ZB) by 2025, which is 10 times of the 16.1 ZB of data generated in 2016. This happens while IT staff to manage it will grow less than 1.5 times (Reinsel, Gantz, & Rydning, 2017). A substantial number of these resources are documents that are potentially valuable for intentional reuse. Knowledge workers and engineers in particular, require specific knowledge and information embedded in different types of knowledge objects stored in internal or external resources (Hertzum & Pejtersen, 2000). However, identifying relevant knowledge from a large number of unstructured enterprise resources is challenging for users. There is a strong need for an approach that identifies users’ required information and automatically explores their preferred documents. This PhD project focuses on improving knowledge access, sharing, and reuse challenges that people, engineers, are faced with in their daily (knowledge-based) work tasks. The proposed solution is a recommender system in professional settings to provide relevant documents for users in specific work contexts based on domain-specific ontologies. A prototype has been developed and validated on a multidisciplinary engineering use case and its performance has been evaluated. The results show that the developed system is a useful tool for improving information access in traditional engineering Projects compared to the currently applied solutions. The main contributions of this thesis are: C1: In-depth analysis of the context of users and the document corpus in an engineering setting by applying information retrieval tools and semantic annotation. C2. Proposing a framework for a knowledge access system combining recommendation approaches, ontologies, and information retrieval and extraction tools. C3. Construction of a contextual ontology as knowledge domain, derived from users’ work contexts and evaluating its retrievability and coverage against existing documents as resources of knowledge and information. C4. Validation of the concept of the recommender system for improving knowledge and information Access in engineering context by developing a system that uses the proposed ontology-based profiling approach and evaluating the performance of the developed system on a case-study