Yavor Nenov

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.

Complete Query Answering over Horn Ontologies Using a Triple Store

In our previous work, we showed how a scalable OWL 2 RL reasoner can be used to compute both lower and upper bound query answers over very large datasets and arbitrary OWL 2 ontologies. However, when these bounds do not coincide, there still remain a number of possible answer tuples whose status is not determined. In this paper, we show how in the case of Horn ontologies one can exploit the lower and upper bounds computed by the RL reasoner to efficiently identify a subset of the data and ontology that is large enough to resolve the status of these tuples, yet small enough so that the status can be computed using a fully-fledged OWL 2 reasoner. The resulting hybrid approach has enabled us to compute exact answers to queries over datasets and ontologies where previously only approximate query answering was possible.

Computing Datalog Rewritings for Disjunctive Datalog Programs and Description Logic Ontologies

We study the closely related problems of rewriting disjunctive datalog programs and non-Horn DL ontologies into plain datalog programs that entail the same facts for every dataset. We first propose the class of markable disjunctive datalog programs, which is efficiently recognisable and admits polynomial rewritings into datalog. Markability naturally extends to \(\mathcal{SHI}\) ontologies, and markable ontologies admit (possibly exponential) datalog rewritings. We then turn our attention to resolution-based rewriting techniques. We devise an enhanced rewriting procedure for disjunctive datalog, and propose a second class of \(\mathcal{SHI}\) ontologies that admits exponential datalog rewritings via resolution. Finally, we focus on conjunctive query answering over disjunctive datalog programs. We identify classes of queries and programs that admit datalog rewritings and study the complexity of query answering in this setting. We evaluate the feasibility of our techniques over a large corpus of ontologies, with encouraging results.

On the decidability of connectedness constraints in 2D and 3D Euclidean spaces

We investigate (quantifier-free) spatial constraint languages with equality, contact and connectedness predicates, as well as Boolean operations on regions, interpreted over low-dimensional Euclidean spaces. We show that the complexity of reasoning varies dramatically depending on the dimension of the space and on the type of regions considered. For example, the logic with the interior-connectedness predicate (and without contact) is undecidable over polygons or regular closed sets in R2, EXPTIME-complete over polyhedra in R3, and NP-complete over regular closed sets in R3.

Capturing Industrial Information Models with Ontologies and Constraints

This paper describes the outcomes of an ongoing collaboration between Siemens and the University of Oxford, with the goal of facilitating the design of ontologies and their deployment in applications. Ontologies are often used in industry to capture the conceptual information models underpinning applications. We start by describing the role that such models play in two use cases in the manufacturing and energy production sectors. Then, we discuss the formalisation of information models using ontologies, and the relevant reasoning services. Finally, we present SOMM—a tool that supports engineers with little background on semantic technologies in the creation of ontology-based models and in populating them with data. SOMM implements a fragment of OWL 2 RL extended with a form of integrity constraints for data validation, and it comes with support for schema and data reasoning, as well as for model integration. Our preliminary evaluation demonstrates the adequacy of SOMM’s functionality and performance.

Datalog rewritability of Disjunctive Datalog programs and non-Horn ontologies

We study the problem of rewriting a Disjunctive Datalog program into an equivalent plain Datalog program (i.e., one that entails the same facts for every dataset). We show that a Disjunctive Datalog program is Datalog rewritable if and only if it can be rewritten into a linear program (i.e., having at most one IDB body atom in each rule), thus providing a novel characterisation of Datalog rewritability in terms of linearisability. Motivated by this result, we propose the class of markable programs, which extends both Datalog and linear Disjunctive Datalog and admits Datalog rewritings of polynomial size. We show that our results can be seamlessly applied to ontological reasoning and identify two classes of non-Horn ontologies that admit Datalog rewritings of polynomial and exponential size, respectively. Finally, we shift our attention to conjunctive query answering and extend our results to the problem of computing a rewriting of a Disjunctive Datalog program that yields the same answers to a given query w.r.t. arbitrary data. Our empirical results suggest that a fair number of non-Horn ontologies are Datalog rewritable and that query answering over such ontologies becomes feasible using a Datalog engine.

Distributed RDF Query Answering with Dynamic Data Exchange

Evaluating joins over RDF data stored in a shared-nothing server cluster is key to processing truly large RDF datasets. To the best of our knowledge, the existing approaches use a variant of the data exchange operator that is inserted into the query plan statically (i.e., at query compile time) to shuffle data between servers. We argue that such approaches often miss opportunities for local computation, and we present a novel solution to distributed query answering that consists of two main components. First, we present a query answering algorithm based on dynamic data exchange, which exploits data locality to maximise the amount of computation on a single server. Second, we present a partitioning algorithm for RDF data based on graph partitioning whose aim is to increase data locality. We have implemented our approach in the RDFox system, and our performance evaluation suggests that our techniques outperform the state of the art by up to an order of magnitude in terms of query evaluation times, network communication, and memory use.

On the computability of region-based euclidean logics

By a Euclidean logic, we understand a formal language whose variables range over subsets of Euclidean space, of some fixed dimension, and whose non-logical primitives have fixed meanings as geometrical properties, relations and operations involving those sets. In this paper, we consider first-order Euclidean logics with primitives for the properties of connectedness and convexity, the binary relation of contact and the ternary relation of being closer-than. We investigate the computational properties of the corresponding first-order theories when variables are taken to range over various collections of subsets of 1-, 2- and 3- dimensional space. We show that the theories based on Euclidean spaces of dimension greater than 1 can all encode either first- or second-order arithmetic, and hence are undecidable. We show that, for logics able to express the closer-than relation, the theories of structures based on 1- dimensional Euclidean space have the same complexities as their higherdimensional counterparts. By contrast, in the absence of the closer-than predicate, all of the theories based on 1-dimensional Euclidean space considered here are decidable, but non-elementary.

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.

Topological Logics with Connectedness over Euclidean Spaces

We consider the quantifier-free languages, Bc and Bc°, obtained by augmenting the signature of Boolean algebras with a unary predicate representing, respectively, the property of being connected, and the property of having a connected interior. These languages are interpreted over the regular closed sets of Rn (n ≥ 2) and, additionally, over the regular closed semilinear sets of Rn. The resulting logics are examples of formalisms that have recently been proposed in the Artificial Intelligence literature under the rubric Qualitative Spatial Reasoning. We prove that the satisfiability problem for Bc is undecidable over the regular closed semilinear sets in all dimensions greater than 1, and that the satisfiability problem for Bc and Bc° is undecidable over both the regular closed sets and the regular closed semilinear sets in the Euclidean plane. However, we also prove that the satisfiability problem for Bc° is NP-complete over the regular closed sets in all dimensions greater than 2, while the corresponding problem for the regular closed semilinear sets is ExpTime-complete. Our results show, in particular, that spatial reasoning is much harder over Euclidean spaces than over arbitrary topological spaces.