Robert Piro

Description logic TBoxes: model-theoretic characterizations and rewritability

We characterize the expressive power of description logic (DL) TBoxes, both for expressive DLs such as ALC and ALCQIO and lightweight DLs such as DL-Lite and EL. Our characterizations are relative to first-order logic, based on a wide range of semantic notions such as bisimulation, equisimulation, disjoint union, and direct product. We exemplify the use of the characterizations by a first study of the following novel family of decision problems: given a TBox T formulated in a DL L, decide whether T can be equivalently rewritten as a TBox in the fragment L′ of L.

RDFox: A Highly-Scalable RDF Store.

We present RDFox—a main-memory, scalable, centralised RDF store that supports materialisation-based parallel datalog reasoning and SPARQL query answering. RDFox uses novel and highly-efficient parallel reasoning algorithms for the computation and incremental update of datalog materialisations with efficient handling of owl:sameAs. In this system description paper, we present an overview of the system architecture and highlight the main ideas behind our indexing data structures and our novel reasoning algorithms. In addition, we evaluate RDFox on a high-end SPARC T5-8 server with 128 physical cores and 4TB of RAM. Our results show that RDFox can effectively exploit such a machine, achieving speedups of up to 87 times, storage of up to 9.2 billion triples, memory usage as low as 36.9 bytes per triple, importation rates of up to 1 million triples per second, and reasoning rates of up to 6.1 million triples per second.

Hybrid Reasoning on OWL RL

Both materialization and backward-chaining as different modes of performing inference have complementary advan- tages and disadvantages. Materialization enables very efficient responses at query time, but at the cost of an expensive up front closure computation, which needs to be redone every time the knowledge base changes. Backward-chaining does not need such an expensive and change-sensitive pre-computation, and is therefore suitable for more frequently changing knowledge bases, but has to perform more computation at query time. Materialization has been studied extensively in the recent semantic web literature, and is now available in industrial-strength systems. In this work, we focus instead on backward-chaining, and we present a general hybrid algorithm to perform efficient backward-chaining reasoning on very large RDF data sets. To this end, we analyze the correctness of our algorithm by proving its completeness using the theory developed in deductive databases and we introduce a number of techniques that exploit the characteristics of our method to execute efficiently (most of) the OWL RL rules. These techniques reduce the computation and hence improve the response time by reducing the size of the generated proof tree and the number of duplicates produced in the derivation. We have implemented these techniques in an experimental prototype called QueryPIE and present an evaluation on both realistic and artificial data sets of a size that is between five and ten billion of triples. The evaluation was performed using one machine with commodity hardware and it shows that (i) with our approach the initial pre-computation takes only a few minutes against the hours (or even days) necessary for a full materialization and that (ii) the remaining overhead introduced by reasoning still allows atomic queries to be processed with an interactive response time. To the best of our knowledge our method is the first that demonstrates complex rule-based reasoning at query time over an input of several billion triples and it takes a step forward towards truly large-scale reasoning by showing that complex and large-scale OWL inference can be performed without an expensive distributed hardware architecture.

Semantic Technologies for Data Analysis in Health Care

A fruitful application of Semantic Technologies in the field of healthcare data analysis has emerged from the collaboration between Oxford and Kaiser Permanente a US healthcare provider (HMO). US HMOs have to annually deliver measurement results on their quality of care to US authorities. One of these sets of measurements is defined in a specification called HEDIS which is infamous amongst data analysts for its complexity. Traditional solutions with either SAS-programs or SQL-queries lead to involved solutions whose maintenance and validation is difficult and binds considerable amount of resources. In this paper we present the project in which we have applied Semantic Technologies to compute the most difficult part of the HEDIS measures. We show that we arrive at a clean, structured and legible encoding of HEDIS in the rule language of the RDF-triple store RDFox. We use RDFox’s reasoning capabilities and SPARQL queries to compute and extract the results. The results of a whole Kaiser Permanente regional branch could be computed in competitive time by RDFox on readily available commodity hardware. Further development and deployment of the project results are envisaged in Kaiser Permanente.

RDFox: A Highly-Scalable RDF Store

We present RDFox—a main-memory, scalable, centralised RDF store that supports materialisation-based parallel datalog reasoning and SPARQL query answering. RDFox uses novel and highly-efficient parallel reasoning algorithms for the computation and incremental update of datalog materialisations with efficient handling of owl:sameAs. In this system description paper, we present an overview of the system architecture and highlight the main ideas behind our indexing data structures and our novel reasoning algorithms. In addition, we evaluate RDFox on a high-end SPARC T5-8 server with 128 physical cores and 4TB of RAM. Our results show that RDFox can effectively exploit such a machine, achieving speedups of up to 87 times, storage of up to 9.2 billion triples, memory usage as low as 36.9 bytes per triple, importation rates of up to 1 million triples per second, and reasoning rates of up to 6.1 million triples per second.