Abstract The problem of concept equivalence is often addressed within ontology alignment. A similar problem is however encountered in ontology design: the decision whether to express multiple semantically close informal concepts as one or more formal classes, for which we coin the term concept quasi-equivalence trade-off. We outline its formal framework as well as an initial set of decision-making criteria. We also tried to collect traces of the trade-off from two sources: the LOV vocabulary catalog and ontology design experts addressed through a questionnaire. Finally, we discuss possible modalities of a software support.

Support for PhD students and their advisors in decision-making before and along their PhD journeys requires providing them with a deep understanding and knowledge of the life-cycle of a PhD. This means giving them access to a thorough understanding of causal relations between events, decisions, and the possible outcome. This knowledge can be attained primarily from insider stories, study reports, communications threads with advisors and colleagues, interviews, and scholarly databases. However, it is unclear how to give this knowledge a reasonable structure (due to the heterogeneity of concepts and data sources) so that we can use it for decision-making during the PhD journey. In this paper, we explore how to analyze and model PhD stories to uncover and extract causal relationships found within each story to get insights into the co-occurrences and causalities. We analyze these stories with thematic analysis to understand their main points and we use concept maps to create semi-formal graphs of connected events and objects where the relationships are being emphasized from the perspective of cause and effect. Our results at this point are a collection of PhD stories in the form of concept maps, thematic codes, a proposed approach for goal-directed PhD story modeling which we describe in this paper.

The concerns of efficient data management and logical inference on the Semantic Web often lead to disconnection between the surface structure of RDF/OWL data/ontologies and the background state of affairs. The PURO ontology background model language allows to explicitly capture the mapping between the foreground and background modeling layers. The background modeling primitives are intentionally kept analogous to those of RDF/OWL, namely, derived from the particular-universal and relationship-object distinctions. We project the PURO framework onto the W3C CPV family of structural design patterns, thus providing additional insights into them and possibly facilitating their selection and reuse.

Extraction ontologies allow to swiftly proceed from initial domain modelling to running a functional prototype of a web information extraction application. We investigate the possibility of semi-automatically deriving extraction ontologies from third-party domain ontologies.

We describe an application of information extraction from company Web sites focusing on product offers. A statistical approach to text analysis is used in conjunction with different ways of image classification. Ontological knowledge is used to group the extracted items into structured objects. The results are stored in an RDF repository and made available for structured search.