Translating OWL and Semantic Web Rules into Prolog: Moving Toward Description Logic Programs

To appear in Theory and Practice of Logic Programming (TPLP), 2008. We are researching the interaction between the rule and the ontology layers of the Semantic Web, by comparing two options: 1) using OWL and its rule extension SWRL to develop an integrated ontology/rule language, and 2) layering rules on top of an ontology with RuleML and OWL. Toward this end, we are developing the SWORIER system, which enables efficient automated reasoning on ontologies and rules, by translating all of them into Prolog and adding a set of general rules that properly capture the semantics of OWL. We have also enabled the user to make dynamic changes on the fly, at run time. This work addresses several of the concerns expressed in previous work, such as negation, complementary classes, disjunctive heads, and cardinality, and it discusses alternative approaches for dealing with inconsistencies in the knowledge base. In addition, for efficiency, we implemented techniques called extensionalization, avoiding reanalysis, and code minimization.

💡 Research Summary

This paper presents the SWORIER system, a novel approach to integrating and reasoning over the rule and ontology layers of the Semantic Web by translating them into Prolog. The core research investigates two paradigms for combining rules and ontologies: an integrated approach using OWL with its rule extension SWRL, and a layered approach using RuleML on top of OWL. To compare and operationalize these paradigms, the authors developed SWORIER, which enables efficient automated reasoning by converting OWL ontologies, SWRL rules, and RuleML rules into Prolog code via XSLT transformations.

The translation process is not merely syntactic. A critical component is a set of “General Rules” written in Prolog that formally capture the semantics of OWL primitives (e.g., transitivity of subclass relationships, property characteristics). This ensures the Prolog program behaves according to the official OWL semantics. The system design employs a dual-predicate convention (e.g., issubclassof for input facts and isSubClassOf for queries/inferred closure) to elegantly handle recursive definitions and prevent infinite loops during inference.

A significant contribution of this work is addressing key challenges identified in prior research, such as the work by Volz et al. (2003). The paper proposes solutions for: 1) Negation: Bridging the gap between Prolog’s negation-as-failure and OWL’s logical negation by introducing a new logicNot predicate and corresponding inference rules. 2) Open World Assumption (OWA): Enabling queries to return “unknown” by requiring users to submit both a query Q and logicNot(Q) and interpreting the combined results. 3) Complementary and Disjoint Classes: Demonstrating that these can be implemented using the logicNot predicate within the rule-based framework. 4) Multiple Head Terms: Discussing workarounds for Horn rule limitations by generating multiple rules for conjunctive conclusions.

Beyond semantic fidelity, SWORIER emphasizes practicality and performance. It supports dynamic changes at runtime, allowing facts to be added/removed and rule sets to be swapped, which is crucial for real-world applications like the military command and control scenario described. To achieve the required efficiency (answering queries within seconds), the authors implemented three optimization techniques: Extensionalization (pre-compiling complex class definitions into extensional sets of instances), Avoiding Reanalysis (incrementally processing changes instead of recompiling the entire knowledge base), and Code Minimization (eliminating duplicate facts and redundant code).

The paper concludes by positioning SWORIER within the emerging field of Description Logic Programming, which seeks a marriage between the expressive power of Description Logics (like OWL) and the efficient reasoning capabilities of Logic Programming. It argues that while theoretical expressivity may be sacrificed, the system provides a highly practical and efficient platform for deploying Semantic Web knowledge in operational settings. Future work includes applying more advanced logic programming optimizations and reducing the burden on rule authors.