We present a novel approach to parallel materialisation (i.e., fixpoint computation) of datalog programs in centralised, main-memory, multi-core RDF systems. Our approach comprises an algorithm that evenly distributes the workload to cores, and an RDF indexing data structure that supports efficient, 'mostly' lock-free parallel updates. Our empirical evaluation shows that our approach parallelises computation very well: with 16 physical cores, materialisation can be up to 13.9 times faster than with just one core.

Mapping relational data to RDF is an important task for the development of the Semantic Web. To this end, the W3C has recently released a Recommendation for the so-called direct mapping of relational data to RDF. In this work, we propose an enrichment of the direct mapping to make it more faithful by transferring also semantic information present in the relational schema from the relational world to the RDF world. We thus introduce expressive identification constraints to capture functional dependencies and define an RDF Normal Form, which precisely captures the classical Boyce-Codd Normal Form of relational schemas.

For expressive ontology languages such as OWL 2 DL, classification is a computationally expensive task—2NEXPTIME-complete in the worst case. Hence, it is highly desirable to be able to accurately estimate classification time, especially for large and complex ontologies. Recently, machine learning techniques have been successfully applied to predicting the reasoning hardness category for a given (ontology, reasoner) pair. In this paper, we further develop predictive models to estimate actual classification time using regression techniques, with ontology metrics as features. Our large-scale experiments on 6 state-of-the-art OWL 2 DL reasoners and more than 450 significantly diverse ontologies demonstrate that the prediction models achieve high accuracy, good generalizability and statistical significance. Such prediction models have a wide range of applications. We demonstrate how they can be used to efficiently and accurately identify performance hotspots in a large and complex ontology, an otherwise very time-consuming and resource-intensive task.

In this paper we consider the problem of repairing missing is-a relations in ontologies. We formalize the problem as a generalized TBox abduction problem (GTAP). Based on this abduction framework, we present complexity results for the existence, relevance and necessity decision problems for the GTAP with and without some specific preference relations for ontologies that can be represented using a member of the EL family of description logics. Further, we present algorithms for finding solutions, a system as well as experiments.

Creating knowledge bases based on the crowd-sourced wikis, like Wikipedia, has attracted significant research interest in the field of intelligent Web. However, the derived taxonomies usually contain many mistakenly imported taxonomic relations due to the difference between the user-generated subsumption relations and the semantic taxonomic relations. Current approaches to solving the problem still suffer the following issues: (i) the heuristic-based methods strongly rely on specific language dependent rules. (ii) the corpus-based methods depend on a large-scale high-quality corpus, which is often unavailable. In this paper, we formulate the cross-lingual taxonomy derivation problem as the problem of cross-lingual taxonomic relation prediction. We investigate different linguistic heuristics and language independent features, and propose a cross-lingual knowledge validation based dynamic adaptive boosting model to iteratively reinforce the performance of taxonomic relation prediction. The proposed approach successfully overcome the above issues, and experiments show that our approach significantly outperforms the designed state-of-the-art comparison methods.

Two essential tasks in managing Description Logic (DL) ontologies are eliminating problematic axioms and incorporating newly formed axioms. Such elimination and incorporation are formalised as the operations of contraction and revision in belief change.In this paper, we deal with contraction and revision for the DL-Lite family through a model-theoretic approach.Standard DL semantics yields infinite numbers of models for DL-Lite TBoxes, thus it is not practical to develop algorithms for contraction and revision that involve DL models. The key to our approach is the introduction of an alternative semantics called type semantics which is more succinct than DL semantics. More importantly, with a finite signature, type semantics always yields finite humber of models.We then define model-based contraction and revision for DL-Lite TBoxesunder type semantics and provide representation theorems for them.Finally, the succinctness of type semantics allows us to develop tractable algorithms for both operations.

Diagnosis, or the process of identifying the nature and cause of an anomaly in an ontology, has been largely studied by the Semantic Web community. In the context of ontology stream, diagnosis results are not captured by a unique fixed ontology but numerous time-evolving ontologies. Thus any anomaly can be diagnosed by a large number of different explana- tions depending on the version and evolution of the ontology. We address the problems of identifying, representing, exploiting and exploring the evolution of diagnoses representations. Our approach consists in a graph-based representation, which aims at (i) efficiently organizing and linking time-evolving di- agnoses and (ii) being used for scalable exploration. The ex- periments have shown scalable diagnoses exploration in the context of real and live data from Dublin City.

We present an enhanced hybrid approach to OWL query answering that combines an RDF triple-store with an OWL reasoner in order to provide scalable pay-as-you-go performance. The enhancements presented here include an extension to deal with arbitrary OWL ontologies, and optimisations that significantly improve scalability. We have implemented these techniques in a prototype system, a preliminary evaluation of which has produced very encouraging results.

Schema Matching, i.e. the process of discovering semantic correspondences between concepts adopted in different data source schemas, has been a key topic in Database and Artificial Intelligence research areas for many years. In the past, it was largely investigated especially for classical database models (e.g., E/R schemas, relational databases, etc.). However, in the latest years, the widespread adoption of XML in the most disparate application fields pushed a growing number of researchers to design XML-specific Schema Matching approaches, called XML Matchers, aiming at finding semantic matchings between concepts defined in DTDs and XSDs. XML Matchers do not just take well-known techniques originally designed for other data models and apply them on DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical structure of a DTD/XSD) to improve the performance of the Schema Matching process. The design of XML Matchers is currently a well-established research area. The main goal of this paper is to provide a detailed description and classification of XML Matchers. We first describe to what extent the specificities of DTDs/XSDs impact on the Schema Matching task. Then we introduce a template, called XML Matcher Template, that describes the main components of an XML Matcher, their role and behavior. We illustrate how each of these components has been implemented in some popular XML Matchers. We consider our XML Matcher Template as the baseline for objectively comparing approaches that, at first glance, might appear as unrelated. The introduction of this template can be useful in the design of future XML Matchers. Finally, we analyze commercial tools implementing XML Matchers and introduce two challenging issues strictly related to this topic, namely XML source clustering and uncertainty management in XML Matchers.

We propose a novel approach for performance prediction of OWL reasoners that selects suitable, small ontology subsets, and then extrapolates reasoner's performance on them to the whole ontology. We investigate intercorrelation of ontology features using PCA and discuss various error measures for performance prediction.