Description Logics with Concrete Domains and Functional Dependencies

Description Logics (DLs) with concrete domains are a useful tool in many applications. To further enhance the expressive power of such DLs, it has been proposed to add database-style key constraints. Up to now, however, only uniqueness constraints have been considered in this context, thus neglecting the second fundamental family of key constraints: functional dependencies. In this paper, we consider the basic DL with concrete domains ALC(D), extend it with functional dependencies, and analyze the impact of this extension on the decidability and complexity of reasoning. Though intuitively the expressivity of functional dependencies seems weaker than that of uniqueness constraints, we are able to show that the former have a similarly severe impact on the computational properties: reasoning is undecidable in the general case, and NExpTime-complete in some slightly restricted variants of our logic

Subsumption Algorithms for Some Attributive Concept Description Languages

This paper investigates subsumption algorithms for logic-based knowledge representation languages of the KL-ONE family. We amalgamate the attributive concept description language ALC, that contains value restrictions, intersections, unions and complements with number restrictions, role hierarchies (to model the KL-ONE’s roleset differentiation), and Feature Logic, respectively. We show that deciding consistency and subsumption of ALC extended with number restrictions and ALC extended with role hierarchies is PSPACE-complete. Furthermore, for all these languages we give subsumption algorithms

Reasoning with Very Expressive Fuzzy Description Logics

It is widely recognized today that the management of imprecision and vagueness will yield more intelligent and realistic knowledge-based applications. Description Logics (DLs) are a family of knowledge representation languages that have gained considerable attention the last decade, mainly due to their decidability and the existence of empirically high performance of reasoning algorithms. In this paper, we extend the well known fuzzy ALC DL to the fuzzy SHIN DL, which extends the fuzzy ALC DL with transitive role axioms (S), inverse roles (I), role hierarchies (H) and number restrictions (N). We illustrate why transitive role axioms are difficult to handle in the presence of fuzzy interpretations and how to handle them properly. Then we extend these results by adding role hierarchies and finally number restrictions. The main contributions of the paper are the decidability proof of the fuzzy DL languages fuzzy-SI and fuzzy-SHIN, as well as decision procedures for the knowledge base satisfiability problem of the fuzzy-SI and fuzzy-SHIN

Evaluation von Wissensrepräsentationssystemen

Ziel dieses Berichtes ist eine Evaluation von aktuellen Wissensrepräsentationssystemen, insbesondere terminologischen Logiken. Nach Aufstellung der relevanten Evaluationskriterien erfolgt zunächst eine allgemeine Behandlung von KL-ONE - der Urmutter der terminologischen Logiken -, wobei schon einige inhärente kritische Punkte der zu behandelnden Systeme aufgezeigt werden. Anschließend werden Syntax- und Semantikdefinitionen von KL-ONE-Derivaten vorgestellt, um deren Sprachumfang zu vergleichen. Neben den gängigen KL-ONE-Derivaten wird auch die in LILOG verwendete Repräsentationssprache vorgestellt. Abschließend erfolgt ein zusammenfassender Vergleich der Systeme. Hierbei stellt sich heraus, dass insbesondere die Systeme LOOM, CLASSIC, KRIS und BACK bezüglich des verwendeten Sprachumfangs und der Effizienz der Inferenzen gut abschneiden. Die Systeme BACK und KRIS sind dabei für Verbmobil besonders relevant, da sie relativ leicht verfügbar sind. Außerdem zeichnet sich BACK durch ein gut strukturiertes Handbuch aus und eine schnelle neue Implementierung In C. Kritisch bei allen vorgestellten Systemen ist die Darstellung zeitlicher Zusammenhänge (Ereignisse, Aktionen); hierzu liegen jedoch schon Forschungsergebnisse für die Erweiterung der terminologischen Sprachen vor

Fuzzy Description Logics with General Concept Inclusions

Description logics (DLs) are used to represent knowledge of an application domain and provide standard reasoning services to infer consequences of this knowledge. However, classical DLs are not suited to represent vagueness in the description of the knowledge. We consider a combination of DLs and Fuzzy Logics to address this task. In particular, we consider the t-norm-based semantics for fuzzy DLs introduced by Hájek in 2005. Since then, many tableau algorithms have been developed for reasoning in fuzzy DLs. Another popular approach is to reduce fuzzy ontologies to classical ones and use existing highly optimized classical reasoners to deal with them. However, a systematic study of the computational complexity of the different reasoning problems is so far missing from the literature on fuzzy DLs. Recently, some of the developed tableau algorithms have been shown to be incorrect in the presence of general concept inclusion axioms (GCIs). In some fuzzy DLs, reasoning with GCIs has even turned out to be undecidable. This work provides a rigorous analysis of the boundary between decidable and undecidable reasoning problems in t-norm-based fuzzy DLs, in particular for GCIs. Existing undecidability proofs are extended to cover large classes of fuzzy DLs, and decidability is shown for most of the remaining logics considered here. Additionally, the computational complexity of reasoning in fuzzy DLs with semantics based on finite lattices is analyzed. For most decidability results, tight complexity bounds can be derived

Basic Description Logics

This chapter provides an introduction to Description Logics as a formal language for representing knowledge and reasoning about it. It first gives a short overview of the ideas underlying Description Logics. Then it introduces syntax and semantics, covering the basic constructors that are used in systems or have been introduced in the literature, and the way these constructors can be used to build knowledge bases. Finally, it defines the typical inference problems, shows how they are interrelated, and describes different approaches for effectively solving these problems. Some of the topics that are only briefly mentioned in this chapter will be treated in more detail in subsequent chapters

The Data Complexity of Ontology-Mediated Queries with Closed Predicates

In the context of ontology-mediated querying with description logics (DLs), we study the data complexity of queries in which selected predicates can be closed (OMQCs). We provide a non-uniform analysis, aiming at a classification of the complexity into tractable and non-tractable for ontologies in the lightweight DLs DL-Lite and EL, and the expressive DL ALCHI. At the level of ontologies, we prove a dichotomy between FO-rewritable and coNP-complete for DL-Lite and between PTime and coNP-complete for EL. The meta problem of deciding tractability is proved to be in PTime. At the level of OMQCs, we show that there is no dichotomy (unless NP equals PTime) if both concept and role names can be closed. If only concept names can be closed, we tightly link the complexity of query evaluation to the complexity of surjective CSPs. We also identify a class of OMQCs based on ontologies formulated in DL-Lite that are guaranteed to be tractable and even FO-rewritable