A Tableau Algorithm for DLs with Concrete Domains and GCIs

We identify a general property of concrete domains that is sufficient for proving decidability of DLs equipped with them and GCIs. We show that some useful concrete domains, such as temporal one based on the Allen relations and a spatial one based on the RCC-8 relations, have this property. Then, we present a tableau algorithm for reasoning in DLs equipped with such concrete domains

A Tableau Algorithm for DLs with Concrete Domains and GCIs

We identify a general property of concrete domains that is sufficient for proving decidability of DLs equipped with them and GCIs. We show that some useful concrete domains, such as temporal one based on the Allen relations and a spatial one based on the RCC-8 relations, have this property. Then, we present a tableau algorithm for reasoning in DLs equipped with such concrete domains

A Tableau Algorithm for DLs with Concrete Domains and GCIs

We identify a general property of concrete domains that is sufficient for proving decidability of DLs equipped with them and GCIs. We show that some useful concrete domains, such as temporal one based on the Allen relations and a spatial one based on the RCC-8 relations, have this property. Then, we present a tableau algorithm for reasoning in DLs equipped with such concrete domains

A Tableau Algorithm for DLs with Concrete Domains and GCIs

We identify a general property of concrete domains that is sufficient for proving decidability of DLs equipped with them and GCIs. We show that some useful concrete domains, such as a temporal one based on the Allen relations and a spatial one based on the RCC-8 relations, have this property. Then, we present a tableau algorithm for reasoning in DLs equipped with such concrete domains.

Action, Time and Space in Description Logics

Description Logics (DLs) are a family of logic-based knowledge representation (KR) formalisms designed to represent and reason about static conceptual knowledge in a semantically well-understood way. On the other hand, standard action formalisms are KR formalisms based on classical logic designed to model and reason about dynamic systems. The largest part of the present work is dedicated to integrating DLs with action formalisms, with the main goal of obtaining decidable action formalisms with an expressiveness significantly beyond propositional. To this end, we offer DL-tailored solutions to the frame and ramification problem. One of the main technical results is that standard reasoning problems about actions (executability and projection), as well as the plan existence problem are decidable if one restricts the logic for describing action pre- and post-conditions and the state of the world to decidable Description Logics. A smaller part of the work is related to decidable extensions of Description Logics with concrete datatypes, most importantly with those allowing to refer to the notions of space and time

Large Scale Qualitative Spatio-Temporal Reasoning

This thesis considers qualitative spatio-temporal reasoning (QSTR), a branch of artificial intelligence that is concerned with qualitative spatial and temporal relations between entities. Despite QSTR being an active area of research for many years, there has been comparatively little work looking at large scale qualitative spatio-temporal reasoning - reasoning using hundreds of thousands or millions of relations. The big data phenomenon of recent years means there is now a requirement for QSTR implementations that will scale effectively and reason using large scale datasets. However, existing reasoners are limited in their scalability, what is needed are new approaches to QSTR. This thesis considers whether parallel distributed programming techniques can be used to address the challenges of large scale QSTR. Specifically, this thesis presents the first in-depth investigation of adapting QSTR techniques to work in a distributed environment. This has resulted in a large scale qualitative spatial reasoner, ParQR, which has been evaluated by comparing it with existing reasoners and alternative approaches to large scale QSTR. ParQR has been shown to outperform existing solutions, reasoning using far larger datasets than previously possible. The thesis then considers a specific application of large scale QSTR, querying knowledge graphs. This has two parts to it. First, integrating large scale complex spatial datasets to generate an enhanced knowledge graph that can support qualitative spatial reasoning, and secondly, adapting parallel, distributed QSTR techniques to implement a query answering system for spatial knowledge graphs. The query engine that has been developed is able to provide solutions to a variety of spatial queries. It has been evaluated and shown to provide more comprehensive query results in comparison to using quantitative only techniques

Semantische Informationsintegration - Konzeption eines auf Beschreibungslogiken basierenden Integrationssystems für die Produktentwicklung

Aufgrund der Notwendigkeit, unkontrolliert aufkommende Datenfluten zu beherrschen sowie der steigenden Produktkomplexität resultiert der Handlungsbedarf, skalierbare Informationsintegrationslösungen zu finden, die einen effizienten und kontextbezogenen Zugriff auf Wissen unterstützen. Einsatz eines semantischen Integrationskonzepts in der Produktentwicklung erweitert den Wissensbeschaffungsraum des Ingenieurs enorm und ermöglicht die Interoperabilität heterogener Informationssysteme