Increasingly, it has been hard to ignore that standalone KOSs are becoming functionally
ineffective components for knowledge-based AI systems, given their inability to capture the …

The development of domain ontological models, though being a mature research arena
backed by well-established methodologies, still suffer from two key shortcomings. Firstly, the …

Abstract Knowledge Representation (KR) and facet-analytical Knowledge Organization (KO)
have been the two most prominent methodologies of data and knowledge modelling in the …

Measuring ontology matching is a critical issue in knowledge engineering and supports
knowledge sharing and knowledge evolution. Recently, linguistic scientists have defined …

We propose a general methodology and an infrastructure which allows to achieve
interoperability within the same university and across universities. The former goal is …

Semantic Heterogeneity is conventionally understood as the existence of variance in the
representation of a target reality when modelled, by independent parties, in different …

More and more, with the growing focus on large scale analytics, we are confronted with the
need of integrating data from multiple sources. The problem is that these data are …

The premise for AI systems like personal assistants to provide guidance and suggestions to
an end-user is to understand, at any moment in time, the personal context that the user is in …

Ontological knowledge modelling of epic texts, though being an established research arena
backed by concrete multilingual and multicultural works, still suffers from two key …

Knowledge Organization (KO) and Knowledge Representation (KR) have been the two
mainstream methodologies of knowledge modelling in the Information Science community …