Abstract. We address the problem of answering Web ontology queries efficiently. An ontology is formalized as a Deductive Ontology Base (DOB), a deductive database that comprises the ontology’s inference axioms and facts, and we present a cost-based query optimization technique for DOB. A hybrid cost model is proposed to estimate the cost and cardinality of basic and inferred facts. Cardinality and cost of inferred facts are estimated using an adaptive sampling technique, while techniques of traditional relational cost models are used for estimating the cost of basic facts and conjunctive ontology queries. Finally, we implement a dynamic-programming optimization algorithm to identify query evaluation plans that minimize the number of intermediate inferred facts. We modeled a subset of the Web ontology language OWL Lite as a DOB, and performed an experimental study to analyze the predictive capacity of our cost model and the benefits of the query optimization technique. Our study has been conducted over synthetic and real-world OWL ontologies, and shows that the techniques are accurate and improve query performance. 1

Abstract. In this paper we describe the OnEQL system, a query engine that implements optimization techniques and evaluation strategies to speed up the evaluation time of querying and reasoning services in the Semantic Web. To identify execution plans that reduce the cost of evaluating a query, we developed a twofold optimization strategy that combines cost-based optimization and Magic Sets techniques. In the first stage, a dynamic programming-based algorithm is used to identify an ordering of predicates in the query that minimizes its estimated evaluation cost. In the second stage, Magic Sets techniques are used to push down query selections into the OnEQL ontology representation, in order to reduce the number of facts inferred during query evaluation. Additionally, we developed three physical operators that execute the sideways passing of bindings during the evaluation of the execution plan. To illustrate the advantages of this approach, we report the results of an experimental study over the most popular health ontologies. 1

In this report we describe a method to perform type inference over data stored in an RDBMS, where rules over the data are specified using OWL-DL. Since OWL-DL is an implementation of the Description Logic (DL) SHOIN(D), we are in effect implementing a method for SHOIN(D) reasoning in relational databases. Reasoning make be broken down into two processes of classification and type inference. Classification may be performed efficiently by a number of existing reasoners, and since classification alters the schema, it need only be performed once for any given relational schema as a preprocessor of the schema before creation of a database schema. However, type inference needs to be performed for each data value added to the database, and hence needs to be more tightly coupled with the database system. Previously, no technique has been proposed that implements SHOIN(D) type inference within an RDBMS. We propose such a technique, involving the use of triggers to perform reasoning over the data values as they inserted into the database. We demonstrate the soundness and performance of our approach by comparing an implementation of our technique against other existing approaches for less powerful reasoning over data in an RDBMS. The results show we provide the fastest query processing of any technique, despite having a more powerful reasoner.

Abstract not found

The Semantic Web is the extension of WWW whose purpose is to allow software agents to process documents more intelligently. Access to RDF data is one of the key moments for semantic applications and requires implementing both retrieval and reasoning. Reasoning with large ontologies and data sets becomes increasingly important. It is a serious challenge for the most advanced in-memory reasoners, and they begin to exploit databases. Most of the proposals apply a hybrid database/reasoner architecture which has several shortcomings. The paper presents an approach to query Semantic Web data in databases. Both TBox and ABox reasonings are performed using RDBMS features. It can be performed during querying or precomputed; caching of previous queries results is also supported. Integration of data stored in a relational database with Semantic Web data and support of most SPARQL features are obtained using SQL power. 1

The present decade has seen significant progress towards realizing the vision of the Semantic Web. This progress has most often been seen in the levels of maturity reached by each layer in the architectural layers representing the Semantic Web vision. The ontology layer reached a substantial level of maturity with the OWL Web Ontology Language (OWL) being recommended by the WorldWide Web Consortium (W3C) as the standard for representing ontologies on the Web. This move has triggered several other standardizations and led to interesting research results that have further strengthened the ontology layer. This has motivated the Semantic Web community to venture further towards the rules layer in the vision. One of the interesting lines of research in this context is to extend OWL with rules both syntactically and semantically and providing a sound, complete and terminating reasoning support for the extension. Our present work corresponds to this line of research. We investigate the problem of providing a description logic (DL) based rule reasoning support for OWL ontologies extended with rules. Guided by the Semantic Web Rule Language (SWRL) extension to OWL, we recognize a rule language called the SWRL-like rule language as a rule-extension to OWL. The SWRL-like rule language has a similar syntax as SWRL but differs in its semantics. We propose the system Fire, a rule reasoning engine for the extension of OWL ontologies with SWRL-like rules. The reasoning procedure proposed for the Fire system follows active domain semantics to ensure termination. The reasoning procedure is conjectured to be sound and complete based on the approach followed in the CARIN system. Contrary to several existing translation-based approaches for reasoning with OWL ontologies combined with rules, our proposal provides direct DL based rule inferencing that is synchronous with the OWL inferencing. This synchronous integration with a DL reasoner offers immediate feedback about rule consequences in the OWL knowledge base (KB). The proposal is supported with a prototype Java implementation iii of the Fire system. The prototype implements the RETE algorithm for pattern matching. Our experiments with the pattern matcher algorithm indicate higher efficiency and speed of the implemented RETE algorithm in matching DL facts with SWRL-like rule patterns, compared to a na¨ıve approach to pattern-matching. The implemented prototype is sound based on the sound and complete reasoning of the DL reasoner RACER. Termination is ensured by the active domain semantics. The prototype does not guarantee completeness of reasoning due to the unavailability of an important OWL reasoning service from any of the existing DL reasoners.

Abstract. In this paper we describe a method to perform type inference over data stored in an RDBMS, where rules over the data are specified using OWL-DL. Since OWL-DL is an implementation of the Description Logic (DL) called SHOIN(D), we are in effect implementing a method for SHOIN(D) reasoning in relational databases. Reasoning may be broken down into two processes of classification and type inference. Classification may be performed efficiently by a number of existing reasoners, and since classification alters the schema, it need only be performed once for any given relational schema as a preprocessor of the schema before creation of a database schema. However, type inference needs to be performed for each data value added to the database, and hence needs to be more tightly coupled with the database system. We propose a technique to meet this requirement based on the use of triggers, which is the first technique to fully implement SHOIN(D) as part of normal transaction processing. 1