Presenting entailments of axioms in a formal ontology is a non-trivial task. This position paper argues that the problem of selecting entailments for presentation to users is not adequately acknowledged or addressed in the literature or in implemented systems. We analyse the problem and consider some di↵erent approaches that can help to address the problem.

Due to the increasing volume of and interconnections between semantic datasets, it becomes a challenging task for novice users to know what are included in a dataset, how they can make use of them, and particularly, what queries should be asked. In this paper we analyse several types of candidate insightful queries and propose a framework to generate such queries and identify their relations. To verify our approach, we implemented our framework and evaluated its performance with benchmark and real world datasets.

So far researchers in the Description Logics / Ontology communities mainly consider ontology reasoning services for static ontologies. The rapid development of the Semantic Web and its emerging data ask for reasoning technologies for dynamic knowledge streams. Existing work on stream reasoning is focused on lightweight languages such as RDF and RDFS. In this paper, we introduce the notion of Ontology Stream Management System (OSMS) and present a stream-reasoning approach based on Truth Maintenance System (TMS). We present optimised EL++ algorithm to reduce memory consumption. Our evaluations show that the optimisation improves TMS-enabled EL++ reasoning to deal with relatively large volumes of data and update efficiently.

Large scale ontology applications require efficient and robust description logic (DL) reasoning services. Expressive DLs usually have very high worst case complexity while tractable DLs are restricted in terms of expressive power. This brings a new challenge: can users use expressive DLs to build their ontologies and still enjoy the efficient services as in tractable languages. In this paper, we present a soundness preserving approximate reasoning framework for TBox reasoning in OWL2-DL. The ontologies are encoded into EL++ with additional data structures. A tractable algorithm is presented to classify such approximation by realizing more and more inference patterns. Preliminary evaluation shows that our approach can classify existing benchmarks in large scale efficiently with a high recall.