ABSTRACT. This presentation will give an overview of our ongoing work in developing knowledge extraction methods for description logic based ontologies. Because the stored knowledge is not only given by the axioms stated in an ontology but also by the knowledge that can be inferred from these axioms, knowledge extraction is a challenging problem. Forgetting creates a compact and faithful representation of the stored knowledge over a user-specified signature by performing inferences on the symbols outside this signature.

After an introduction of the idea of forgetting, an overview of our forgetting tools and some applications we have explored, I will discuss recent collaborative work with SNOMED International to create bespoke knowledge extraction software the medical ontology SNOMED CT. The software creates a self-contained subontology containing definitions of a specified set of focus concepts which minimises the number of supporting symbols used and satisfies SNOMED modelling guidelines. Such subontologies make it easier to reuse and share content, assist with ontology analysis, and querying and classification is faster. The talk will give an overview of this research spanning several years, focussing on key ideas, findings, practical challenges encountered and current applications.

ABSTRACT. Ontology Classification is a central DL reasoning task and supported by several highly-optimised reasoners for OWL ontologies. Different notions of modularity, including the atomic decomposition (AD), have already been exploited by different modular reasoners. In our previous work, we have designed and implemented a new AD-informed and MORe-inspired algorithm that uses Hermit and ELK as delegate reasoners, but avoids any duplicate subsumption tests between these two reasoners. In this paper, we push the algorithm further with easyfication and parallelization. We empirically evaluate our algorithm with a set of Snomed CT extensions ontologies and a corpus of BioPortal ontologies. We also design, implement and empirically evaluate a new modular reasoner, called Crane, which works with ``coarsened'' AD.

ABSTRACT. Classical instance queries over an ontology only consider explicitly named individuals. Concept referring expressions (CREs) also allow for returning answers in the form of concepts that describe implicitly given individuals in terms of their relation to an explicitly named one. Existing approaches, e.g., based on tree automata, can neither be integrated into state-of-the-art OWL reasoners nor are they directly amenable for an efficient implementation. To address this, we devise a novel algorithm that uses highly optimized OWL reasoners as a black box. In addition to the standard criteria of singularity and certainty for CREs, we devise and consider the criterion of uniqueness of CREs for Horn ALC ontologies. The evaluation of our prototypical implementation shows that computing CREs for the most general concept (⊤) can be done in less than one minute for ontologies with thousands of individuals and concepts.

ABSTRACT. We show how JSON documents can be abstracted as concept descriptions in an appropriate description logic. This representation allows the use of additional background knowledge in a form of a TBox and an assignment of referring expression types (RETs) to certain primitive concepts to detect situations in which subdocuments, perhaps multiple subdocuments located in various parts of the original documents, capture information about a particular conceptual entity. Detecting such situations allows for normalizing the JSON document into several separate documents that capture all information about such conceptual entities in separate documents. This transformation preserves all the original information present in the input documents. The RET assignment contributes a set of possible concept descriptions that enable more refined and normalized capture of documents, and to more crafted answers to queries that adhere to user expectations expressed as RETs. We also show how RETs allow checking for a document admissibility condition that ensures that the documents describe a single conceptual entity.

ABSTRACT. Data preparation is a key step for process mining. In this paper, we present how to leverage the Virtual Knowledge Graph approach for extracting event logs from data in relational databases. This approach is implemented in the OnProm system, and supports both the IEEE Standard for eXtensible Event Stream (XES), and the recently proposed standard Object-Centric Event Logs (OCEL).Process mining is a family of techniques that supports the analysis of operational processes based on event logs. Among the existing event log formats, the IEEE standard eXtensible Event Stream (XES) is the most widely adopted. In XES, each event must be related to a single case object, which may lead to convergence and divergence problems. To solve such issues, object-centric approaches become promising, where objects are the central notion, and one event may refer to multiple objects. In particular, the Object-Centric Event Logs (OCEL) standard has been proposed recently. However, the crucial problem of extracting OCEL logs from external sources is still largely unexplored. In this paper, we try to fill this gap by leveraging the Virtual Knowledge Graph (VKG) approach to access data in relational databases. We have implemented this approach in the OnProm system, extending it from XES to OCEL support.

ABSTRACT. We present in this paper a reformulation of the usual set-theoretical semantics of the description logic ALC with general TBoxes by using categorical language. In this setting, ALC concepts are represented as objects, concept subsumptions as arrows, and memberships as logical quantifiers over objects and arrows of categories. Such a category-based semantics provides a more modular representation of the semantics of ALC. This feature allows us to define a sublogic of ALC by dropping the interaction between existential and universal restrictions, which would be responsible for an exponential complexity in space. Such a sublogic is undefinable in the usual set-theoretical semantics, We show that this sublogic is PSPACE by proposing a deterministic algorithm for checking concept satisfiability which runs in polynomial space.