On the Semantic Relationship between Datalog and Description Logics

Krötzsch, Markus; Rudolph, Sebastian; Schmitt, Peter H.

doi:10.1007/978-3-642-15918-3_8

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…In both cases, the ontology only serves as input to the rules and not vice versa, i.e., information flow is one way. Description Logic Programs (DLP) [24,44] are a fragment of OWL that can be transformed into logic programs of positive rules. In the same spirit, Horn-SHIQ [33] is a fragment of OWL that can be translated into Datalog, and (like DLP) is of tractable data complexity.…”

Section: Related Workmentioning

confidence: 99%

Local closed world reasoning with description logics under the well-founded semantics

Knorr¹,

Alferes²,

Hitzler

2011

Artificial Intelligence

111

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An important question for the upcoming Semantic Web is how to best combine open world ontology languages, such as the OWL-based ones, with closed world rule-based languages. One of the most mature proposals for this combination is known as hybrid MKNF knowledge bases [52], and it is based on an adaptation of the Stable Model Semantics to knowledge bases consisting of ontology axioms and rules. In this paper we propose a well-founded semantics for nondisjunctive hybrid MKNF knowledge bases that promises to provide better efficiency of reasoning, and that is compatible with both the OWL-based semantics and the traditional Well-Founded Semantics for logic programs. Moreover, our proposal allows for the detection of inconsistencies, possibly occurring in tightly integrated ontology axioms and rules, with only little additional effort. We also identify tractable fragments of the resulting language.

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Section: Related Workmentioning

confidence: 99%

Local closed world reasoning with description logics under the well-founded semantics

Knorr¹,

Alferes²,

Hitzler

2011

Artificial Intelligence

111

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“…property but that require rather unwieldy syntactic definitions [Krötzsch et al 2010]. Hornness, in contrast, appears to be a more natural way of defining DLP-like logics.…”

Section: The Tractable Horn Description Logic Rl Rl Rlmentioning

confidence: 98%

Complexities of Horn Description Logics

Krötzsch

Rudolph

Hitzler

2013

ACM Trans. Comput. Logic

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Description logics (DLs) have become a prominent paradigm for representing knowledge in a variety of application areas, partly due to their ability to achieve a favourable balance between expressivity of the logic and performance of reasoning. Horn description logics are obtained, roughly speaking, by disallowing all forms of disjunctions. They have attracted attention since their (worst-case) data complexities are in general lower than those of their non-Horn counterparts, which makes them attractive for reasoning with large sets of instance data (ABoxes). It is therefore natural to ask whether Horn DLs also provide advantages for schema (TBox) reasoning, that is, whether they also feature lower combined complexities. This article settles this question for a variety of Horn DLs. An example of a tractable Horn logic is the DL underlying the ontology language OWL RL, which we characterize as the Horn fragment of the description logic SROIQ without existential quantifiers. If existential quantifiers are allowed, however, many Horn DLs become intractable. We find that Horn-ALC already has the same worst-case complexity as ALC, that is, EXPTIME, but we also identify various DLs for which reasoning is PSPACE-complete. As a side effect, we derive simplified syntactic definitions of Horn DLs for which we exploit suitable normal form transformations.

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“…The ability to include closed world reasoning in OWL would be ideal and has been made possible to a certain degree via the incorporation of the semantic web rule language (SWRL) into the semantic web stack. SWRL is based on first-order Hornlogic in which rules in Datalog are also expressed [15], but requires an ABox. Another expressive logic formalism allowing some integration of open-and closed-world reasoning is minimal knowledge and negation as failure (MKNF) [16].…”

Section: Automatic Reasoningmentioning

confidence: 99%

Ontologies as a Tool for Formalizing Data Validation Rules

Nicholson¹,

Štotl²

2023

Latest Advances and New Visions of Ontology in Information Science

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Comparison of health data across national or even regional boundaries is a challenging task. Data sources, data collection methods, and data quality can vary widely and the quality of the indicators themselves is dependent upon the veracity of the underlying data. For any trans-regional or trans-national comparison of indicators, it is imperative to ensure data are appropriately validated. Ontologies provide a number of functionalities to help in this process. Data rules can be formalized using the ontology axioms, which are useful for removing the ambiguities of rules expressed in natural language. In addition, the axioms serve to identify the metadata and their corresponding semantic relationships, which can in turn be linked to standard data dictionaries or other ontologies. Moreover, ontologies provide the means for encapsulating the underlying data model of the domain allowing the rules and the data model to be maintained in a single application. Finally the expression of the axioms in description logic, as supported for example by the web ontology language, allows machine reasoning to validate data sets automatically against the formalized rules.

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On the Semantic Relationship between Datalog and Description Logics

Cited by 4 publications

References 19 publications

Local closed world reasoning with description logics under the well-founded semantics

Local closed world reasoning with description logics under the well-founded semantics

Complexities of Horn Description Logics

Ontologies as a Tool for Formalizing Data Validation Rules

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