Bijan Parsia

Mohammed Basheikh, Rujiravee Kongdee, Hood Thabit et al.

This study explored healthcare professionals' perspectives on the management of Type 1 Diabetes Mellitus (T1DM) through a two-part questionnaire. The first part examined how clinicians prioritise and apply current clinical guidelines, including the relative importance assigned to different aspects of T1DM management. The second part investigated clinicians' perceptions of patients' ability to interpret data from the glucose monitoring devices and to make appropriate treatment decisions. An online questionnaire was completed by 19 healthcare professionals working in diabetes-related roles in the United Kingdom. The findings revealed that blood glucose management is prioritised within clinical guidance and that advice is frequently tailored to individual patient needs. Additionally, clinicians generally perceive that data presented in glucose monitoring devices is easy for patients to interpret and based on these data, they believe that patients occasionally make correct treatment decisions.

Christian Kindermann, Anne-Marie George, Bijan Parsia et al.

In this paper, we introduce the problem of rewriting finite formal languages using syntactic macros such that the rewriting is minimal in size. We present polynomial-time algorithms to solve variants of this problem and show their correctness. To demonstrate the practical relevance of the proposed problems and the feasibility and effectiveness of our algorithms in practice, we apply these to biomedical ontologies authored in OWL. We find that such rewritings can significantly reduce the size of ontologies by capturing repeated expressions with macros. In addition to offering valuable assistance in enhancing ontology quality and comprehension, the presented approach introduces a systematic way of analysing and evaluating features of rewriting systems (including syntactic macros, templates, or other forms of rewriting rules) in terms of their influence on computational problems.

Chiara Del Vescovo, Pavel Klinov, Bijan Parsia et al.

Extracting a subset of a given OWL ontology that captures all the ontology's knowledge about a specified set of terms is a well-understood task. This task can be based, for instance, on locality-based modules (LBMs). These come in two flavours, syntactic and semantic, and a syntactic LBM is known to contain the corresponding semantic LBM. For syntactic LBMs, polynomial extraction algorithms are known, implemented in the OWL API, and being used. In contrast, extracting semantic LBMs involves reasoning, which is intractable for OWL 2 DL, and these algorithms had not been implemented yet for expressive ontology languages. We present the first implementation of semantic LBMs and report on experiments that compare them with syntactic LBMs extracted from real-life ontologies. Our study reveals whether semantic LBMs are worth the additional extraction effort, compared with syntactic LBMs.