SPARQL is the de facto language for querying RDF data, since its standardization in 2008. A new version, called SPARQL 1.1, was released in 2013, with the aim of enriching the 2008 language with reasoning capabilities to deal with RDFS and OWL vocabularies, and a mechanism to express navigation patterns through regular expressions. However, SPARQL 1.1 is not powerful enough for expressing some relevant navigation patterns, and it misses a general form of recursion. In this work, we focus on OWL 2 QL and we propose TriQ-Lite 1.0, a tractable rule-based formalism that supports the above functionalities, and thus it can be used for querying RDF data. Unlike existing composite approaches, our formalism has simple syntax and semantics in the same spirit as good old Datalog.

Combined approaches have become a successful technique for CQ answering over ontologies. Existing algorithms, however, are restricted to the logics underpinning the OWL 2 profiles. Our goal is to make combined approaches applicable to a wider range of ontologies. We focus on RSA: a class of Horn ontologies that extends the profiles while ensuring tractability of standard reasoning. We show that CQ answering over RSA ontologies without role composition is feasible in NP. Our reasoning procedure generalises the combined approach for ELHO and DL-LiteR using an encoding of CQ answering into fact entailment w.r.t. a logic program with function symbols and stratified negation. Our results have significant practical implications since many out-of-profile Horn ontologies are RSA.

We study confidentiality enforcement in ontologies under the Controlled Query Evaluation framework, where a policy specifies the sensitive information and a censor ensures that query answers that may compromise the policy are not returned. We focus on censors that ensure confidentiality while maximising information access, and consider both Datalog and the OWL 2 profiles as ontology languages.

Context-aware systems use data collected at runtime to recognize certain predefined situations and trigger adaptations. This can be implemented using ontology-based data access (OBDA), which augments classical query answering in databases by adopting the open-world assumption and including domain knowledge provided by an ontology.

We consider existential rules (aka Datalog /-) as a formalism for specifying ontologies. In recent years, many classes of existential rules have been exhibited for which conjunctive query (CQ) entailment is decidable. However, most of these classes cannot express transitivity of binary relations, a frequently used modelling construct. In this paper, we address the issue of whether transitivity can be safely combined with decidable classes of existential rules. First, we prove that transitivity is incompatible with one of the simplest decidable classes, namely aGRD (acyclic graph of rule dependencies), which clarifies the landscape of'finite expansion sets' of rules. Second, we show that transitivity can be safely added to linear rules (a subclass of guarded rules, which generalizes the description logic DL-LiteR) in the case of atomic CQs, and also for general CQs if we place a minor syntactic restriction on the rule set. This is shown by means of a novel query rewriting algorithm that is specially tailored to handle transitivity rules. Third, for the identified decidable cases, we pinpoint the combined and data complexities of query entailment.

Aiming at ontology-based data access over temporal, in particular streaming data, we design a language of ontology-mediated queries by extending OWL 2 QL and SPARQL with temporal operators, and investigate rewritability of these queries into two-sorted first-order logic with and PLUS over time.

Ontology matching is the process of finding semantic correspondences between entities from different ontologies. As an effective solution to linking different heterogeneous ontologies, ontology matching has attracted considerable attentions in recent years. In this paper, we propose a novel graph-based approach to ontology matching problem. Different from previous work, we formulate ontology matching as a random walk process on the association graph constructed from the to-be-matched ontologies. In particular, two variants of the conventional random walk process, namely, Affinity-Preserving Random Walk (APRW) and Mapping-Oriented Random Walk (MORW), have been proposed to alleviate the adverse effect of the false-mapping nodes in the association graph and to incorporate the 1-to-1 matching constraints presumed in ontology matching, respectively. Experiments on the Ontology Alignment Evaluation Initiative (OAEI) datasets show that our approach achieves a competitive performance when compared with state-of-the-art systems, even though our approach does not utilize any external resources.

Building ontologies is a difficult task requiring skills in logics and ontological analysis. Domain experts usually reach as far as organizing a set of concepts into a hierarchy in which the semantics of the relations is under-specified. The categorization of Wikipedia is a huge concept hierarchy of this form, covering a broad range of areas. We propose an automatic method for bootstrapping domain ontologies from the categories of Wikipedia. The method first selects a subset of concepts that are relevant for a given domain. The relevant concepts are subsequently split into classes and individuals, and, finally, the relations between the concepts are classified into subclass_of, instance_of, part_of, and generic related_to. We evaluate our method by generating ontology skeletons for the domains of Computing and Music. The quality of the generated ontologies has been measured against manually built ground truth datasets of several hundred nodes.

In natural language processing and information retrieval, the bag of words representation is used to implicitly represent the meaning of the text. Implicit semantics, however, are insufficient in supporting text or natural language based interfaces, which are adopted by an increasing number of applications. Indeed, in applications ranging from automatic ontology construction to question answering, explicit representation of semantics is starting to play a more prominent role. In this paper, we introduce the task of conceptual labeling (CL), which aims at generating a minimum set of conceptual labels that best summarize a bag of words. We draw the labels from a data driven semantic network that contains millions of highly connected concepts. The semantic network provides meaning to the concepts, and in turn, it provides meaning to the bag of words through the conceptual labels we generate. To achieve our goal, we use an information theoretic approach to trade-off the semantic coverage of a bag of words against the minimality of the output labels. Specifically, we use Minimum Description Length (MDL) as the criteria in selecting the best concepts. Our extensive experimental results demonstrate the effectiveness of our approach in representing the explicit semantics of a bag of words.

The popularization and quick growth of Linked Open Data (LOD) has led to challenging aspects regarding quality assessment and data exploration of the RDF triples that shape the LOD cloud.Particularly, we are interested in the completeness of data and its potential to provide concept definitions in terms of necessary and sufficient conditions.In this work we propose a novel technique based on Formal Concept Analysis which organizes RDF data into a concept lattice.This allows data exploration as well as the discovery of implications, which are used to automatically detect missing information and then to complete RDF data.Moreover, this is a way of reconciling syntax and semantics in the LOD cloud.Finally, experiments on the DBpedia knowledge base show that the approach is well-founded and effective.