4 research outputs found
Towards Ontology Reshaping for KG Generation with User-in-the-Loop: Applied to Bosch Welding
Knowledge graphs (KG) are used in a wide range of applications. The
automation of KG generation is very desired due to the data volume and variety
in industries. One important approach of KG generation is to map the raw data
to a given KG schema, namely a domain ontology, and construct the entities and
properties according to the ontology. However, the automatic generation of such
ontology is demanding and existing solutions are often not satisfactory. An
important challenge is a trade-off between two principles of ontology
engineering: knowledge-orientation and data-orientation. The former one
prescribes that an ontology should model the general knowledge of a domain,
while the latter one emphasises on reflecting the data specificities to ensure
good usability. We address this challenge by our method of ontology reshaping,
which automates the process of converting a given domain ontology to a smaller
ontology that serves as the KG schema. The domain ontology can be designed to
be knowledge-oriented and the KG schema covers the data specificities. In
addition, our approach allows the option of including user preferences in the
loop. We demonstrate our on-going research on ontology reshaping and present an
evaluation using real industrial data, with promising results
Trust-sensitive evolution of DL-lite knowledge bases
Evolution of Knowledge Bases (KBs) consists of incorporating new information in an existing KB. Previous studies assume that the new information should be fully trusted and thus completely incorporated in the old knowledge. We suggest a setting where the new knowledge can be partially trusted and develop model-based approaches (MBAs) to KB evolution that rely on this assumption. Under MBAs the result of evolution is a set of interpretations and thus two core problems for MBAs are closure, i.e., whether evolution result can be axiomatised with a KB, and approximation, i.e., whether it can be (maximally) approximated with a KB. We show that DL-Lite is not closed under a wide range of trustsensitive MBAs. We introduce a notion of s-approximation that improves the previously proposed approximations and show how to compute it for various trust-sensitive MBAs
Trust-sensitive evolution of DL-lite knowledge bases
Evolution of Knowledge Bases (KBs) consists of incorporating new information in an existing KB. Previous studies assume that the new information should be fully trusted and thus completely incorporated in the old knowledge. We suggest a setting where the new knowledge can be partially trusted and develop model-based approaches (MBAs) to KB evolution that rely on this assumption. Under MBAs the result of evolution is a set of interpretations and thus two core problems for MBAs are closure, i.e., whether evolution result can be axiomatised with a KB, and approximation, i.e., whether it can be (maximally) approximated with a KB. We show that DL-Lite is not closed under a wide range of trustsensitive MBAs. We introduce a notion of s-approximation that improves the previously proposed approximations and show how to compute it for various trust-sensitive MBAs