Roman Schindlauer

Combining answer set programming with description logics for the Semantic Web

Towards the integration of rules and ontologies in the Semantic Web, we propose a combination of logic programming under the answer set semantics with the description logics SHIF(D) and SHOIN(D), which underly the Web ontology languages OWL Lite and OWL DL, respectively. This combination allows for building rules on top of ontologies but also, to a limited extent, building ontologies on top of rules. We introduce description logic programs (dl-programs), which consist of a description logic knowledge base L and a finite set of description logic rules (dl-rules) P. Such rules are similar to usual rules in logic programs with negation as failure, but may also contain queries to L, possibly default negated, in their bodies. We define Herbrand models for dl-programs, and show that satisfiable positive dl-programs have a unique least Her-brand model. More generally, consistent stratified dl-programs can be associated with a unique minimal Her-brand model that is characterized through iterative least Herbrand models. We then generalize the (unique) minimal Herbrand model semantics for positive and stratified dl-programs to a strong answer set semantics for all dl-programs, which is based on a reduction to the least model semantics of positive dl-programs. We also define a weak answer set semantics based on a reduction to the answer sets of ordinary logic programs. Strong answer sets are weak answer sets, and both properly generalize answer sets of ordinary normal logic programs. We then give fixpoint characterizations for the (unique) minimal Herbrand model semantics of positive and stratified dl-programs, and show how to compute these models by finite fixpoint iterations. Furthermore, we give a precise picture of the complexity of deciding strong and weak answer set existence for a dl-program.

Well-Founded Semantics for Description Logic Programs in the Semantic Web

In previous work, towards the integration of rules and ontologies in the Semantic Web, we have proposed a combination of logic programming under the answer set semantics with the description logics \({\cal SHIF}({\mathbf{D}})\) and \({\cal SHOIN}({\mathbf{D}})\), which underly the Web ontology languages OWL Lite and OWL DL, respectively. More precisely, we have introduced description logic programs (or dl-programs), which consist of a description logic knowledge base L and a finite set of description logic rules P, and we have defined their answer set semantics. In this paper, we continue this line of research. Here, as a central contribution, we present the well-founded semantics for dl-programs, and we analyze its semantic properties. In particular, we show that it generalizes the well-founded semantics for ordinary normal programs. Furthermore, we show that in the general case, the well-founded semantics of dl-programs is a partial model that approximates the answer set semantics, whereas in the positive and the stratified case, it is a total model that coincides with the answer set semantics. Finally, we also provide complexity results for dl-programs under the well-founded semantics.

Effective integration of declarative rules with external evaluations for semantic-web reasoning

Towards providing a suitable tool for building the Rule Layer of the Semantic Web, hex-programs have been introduced as a special kind of logic programs featuring capabilities for higher-order reasoning, interfacing with external sources of computation, and default negation. Their semantics is based on the notion of answer sets, providing a transparent interoperability with the Ontology Layer of the Semantic Web and full declarativity. In this paper, we identify classes of hex-programs feasible for implementation yet keeping the desirable advantages of the full language. A general method for combining and evaluating sub-programs belonging to arbitrary classes is introduced, thus enlarging the variety of programs whose execution is practicable. Implementation activity on the current prototype is also reported.

Reasoning with Rules and Ontologies

For realizing the Semantic Web vision, extensive work is underway for getting the layers of its conceived architecture ready. Given that the Ontology Layer has reached a certain level of maturity with W3C recommendations such as RDF and the OWL Web Ontology Language, current interest focuses on the Rules Layer and its integration with the Ontology Layer. Several proposals have been made for solving this problem, which does not have a straightforward solution due to various obstacles. One of them is the fact that evaluation principles like the closed-world assumption, which is common in rule languages, are usually not adopted in ontologies. Furthermore, naively adding rules to ontologies raises undecidability issues. In this paper, after giving a brief overview about the current state of the Semantic-Web stack and its components, we will discuss nonmonotonic logic programs under the answer-set semantics as a possible formalism of choice for realizing the Rules Layer. We will briefly discuss open issues in combining rules and ontologies, and survey some existing proposals to facilitate reasoning with rules and ontologies. We will then focus on description-logic programs (or dl-programs, for short), which realize a transparent integration of rules and ontologies supported by existing reasoning engines, based on the answer-set semantics. We will further discuss a generalization of dl-programs, viz.hex-programs, which offer access to different ontologies as well as higher-order language constructs.

Well-founded semantics for description logic programs in the semantic web

The realization of the Semantic Web vision, in which computational logic has a prominent role, has stimulated a lot of research on combining rules and ontologies, which are formulated in different formalisms. In particular, combining logic programming with the Web Ontology Language (OWL), which is a standard based on description logics, emerged as an important issue for linking the Rules and Ontology Layers of the Semantic Web. Nonmonotonic description logic programs (dl-programs) were introduced for such a combination, in which a pair (L,P) of a description logic knowledge base L and a set of rules P with negation as failure is given a model-based semantics that generalizes the answer set semantics of logic programs. In this article, we reconsider dl-programs and present a well-founded semantics for them as an analog for the other main semantics of logic programs. It generalizes the canonical definition of the well-founded semantics based on unfounded sets, and, as we show, lifts many of the well-known properties from ordinary logic programs to dl-programs. Among these properties, our semantics amounts to a partial model approximating the answer set semantics, which yields for positive and stratified dl-programs, a total model coinciding with the answer set semantics; it has polynomial data complexity provided the access to the description logic knowledge base is polynomial; under suitable restrictions, it has lower complexity and even first-order rewritability is achievable. The results add to previous evidence that dl-programs are a versatile and robust combination approach, which moreover is implementable using legacy engines.

OntoDLV: An ASP-based System for Enterprise Ontologies

Enterprise/Corporate ontologies are widely adopted to conceptualize business enterprise information. In this area, the semantic peculiarities of Answer Set Programming (ASP), like the Closed World Assumption (CWA) and the Unique Name Assumption (UNA), are more appropriate than the Ontology Web Language (OWL) assumptions, also because such ontologies frequently stem from relational databases, where both CWA and UNA are adopted. This article presents OntoDLV, a system based on ASP for the specification and reasoning on enterprise ontologies. OntoDLV implements a powerful ontology representation language, called OntoDLP, extending (disjunctive) ASP with all the main ontology features including classes, inheritance, relations and axioms. OntoDLP is strongly typed, and includes also complex type constructors, like lists and sets. Importantly, OntoDLV supports a powerful interoperability mechanism with OWL, allowing the user to retrieve information from OWL ontologies, and build rule-based reasoning on top of OWL ontologies. The system is already used in a number of real-world applications including agent-based systems, information extraction, and text classification.

Exploiting conjunctive queries in description logic programs

Towards combining rules and ontologies for the Semantic Web, nonmonotonic Description Logic Programs (dl-programs) have been proposed as a powerful formalism to couple nonmonotonic logic programming and Description Logic reasoning on a clear semantic basis. In this paper, we present cq-programs, which enhance dl-programs with conjunctive queries (CQ) and union of conjunctive queries (UCQ) over Description Logics knowledge bases, as well as with disjunctive rules. The novel formalism has two advantages. First, it offers increased expressivity because it allows for (U)CQs in the bodies of the rules. The (U)CQs allow one to access unnamed individuals in the rules and they increase the expressivity of the formalism, as evident from the increase in complexity from NEXP to 2-EXP. And second, when implemented as a combination between a logic programming system and a DL-reasoner, this integration of rules and ontologies gives rise to strategies for optimizing calls to the DL-reasoner, by exploiting specific support for (U)CQs. To this end, we present equivalence preserving transformations which can be used for program rewriting, and we present respective generic rewriting algorithms. Experimental results for a cq-program prototype show that this can lead to significant performance improvements, and suggest that cq-programs and program rewriting provide a useful basis for dl- and cq-program optimization.

dlvhex: A Prover for Semantic-Web Reasoning under the Answer-Set Semantics

We present the system dlvhex, a solver for HEX-programs, which are nonmonotonic logic programs admitting both higher-order atoms as well as external atoms. Higher-order features are widely acknowledged as being useful for various tasks, including meta-reasoning. Furthermore, the possibility to exchange knowledge with external sources in a fully declarative paradigm such as answer-set programming (ASP) becomes increasingly important, in particular in view of applications in the semantic-Web area. Through external atoms, HEX-programs can deal with external knowledge and reasoners of various nature, such as RDF datasets or description-logics knowledge bases

Forgetting in Managing Rules and Ontologies

The language of HEX-programs under the answer-set semantics is designed for interoperating with heterogeneous sources via external atoms and for meta-reasoning via higher-order literals in the context of the semantic Web. As an important technique in managing knowledge bases, the notion of forgetting has received increasing interest in the knowledge-representation area. In this paper, we introduce a semantics-based theory of forgetting for HEX-programs and, in turn, for a class of OWL/RDF(S) ontologies which allows to fully employ semantic information in managing ontologies like editing, merging, aligning, and redundancy removal

Nonmonotonic description logic programs : Implementation and experiments

The coupling of description logic reasoning systems with other reasoning formalisms (possibly over the Web) is becoming an important research issue and calls for advanced methods and algorithms. Recently, several notions of description logic programs have been introduced, combining rule-based semantics with description logics. Among them are nonmonotonic description logic programs (or dl-programs for short) which combine nonmonotonic logic programs with description logics under a generalized version of the answer-set and the well-founded semantics, respectively, which are the predominant semantics for nonmonotonic logic programs. In this paper, we consider some technical issues regarding an efficient implementation for both semantics, which has been realized in a working prototype exploiting the two state-of-art tools DLV and RACER. A major issue in this respect is efficient interfacing between the two reasoning systems at hand, for which we devised special methods. Such methods may fruitfully be used for the implementation of systems of similar nature. Reported experimentation activities with our prototype show that the methods we have developed are effective and are a key for highly optimized nonmonotonic dl-program engines.