Finding the commonalities between descriptions of data or knowledge is a foundational reasoning problem of Machine Learning. It was formalized in the early 70’s as computing a least general generalization (lgg) of such descriptions. We revisit this well-established problem in the SPARQL query language for RDF graphs. In particular, and by contrast to the literature, we address it for the entire...

We address the problem of scalable distributed reasoning, proposing a technique for materialising the closure of an RDF graph based on MapReduce. We have implemented our approach on top of Hadoop and deployed it on a compute cluster of up to 64 commodity machines. We show that a naive implementation on top of MapReduce is straightforward but performs badly and we present several non-trivial opt...

VizCurator permits the exploration, understanding and curation of open RDF data, its schema, and how it has been linked to other sources. We provide visualizations that enable one to seamlessly navigate through RDFS and RDF layers and quickly understand the open data, how it has been mapped or linked, how it has been structured (and could be restructured), and how deeply it has been related to ...

We present a taxonomy automatically generated from the system of categories in Wikipedia. Categories in the resource are identified as either classes or instances and included in a large subsumption, i.e. isa, hierarchy. The taxonomy is made available in RDFS format to the research community, e.g. for direct use within AI applications or to bootstrap the process of manual ontology creation.

The exponential growth of the web and the extended use of semantic web technologies has brought to the fore the need for quick understanding, flexible exploration and selection of complex web documents and schemas. To this direction, ontology summarization aspires to produce an abridged version of the original ontology that highlights its most representative concepts. In this paper, we present ...

This is the tenth in a series of articles on ontology-based approaches to modeling. The main focus is on popular ontology languages proposed for the Semantic Web, such as the Resource Description Framework (RDF), RDF Schema (RDFS), and the Web Ontology Language (OWL). OWL is based on description logic. A later series of articles will explore other logic-based languages such as datalog. The firs...

The web of data consists of distributed, diverse (in terms of schema adopted), and large RDF datasets. In this paper we present a SPARQL query rewriting method which can be used to achieve interoperability in semantic information retrieval and/or knowledge discovery processes over interconnected RDF data sources. Formal mappings between different overlapping ontologies are exploited in order to...

The increasing interest in the RDF data model has turned the efficient processing of queries over RDF datasets to a challenging and crucial task. Indeed, the triple format of the RDF data model, along with the lack of structure that characterizes it, raise new challenges in data management both in terms of performance and scalability. In this paper, we consider improving the performance of RDF ...

As the number and scale of Semantic Web applications in use increases, so does the need to efficiently store and retrieve RDF data. Current published schemes for RDF data management either fail to embrace the schema flexibility inherent in RDF or make restrictive assumptions about application usage models. This paper describes a storage and indexing scheme based on linked lists and memory-mappe...

This paper describes OntoLog, a prototype annotation system for temporal media. It is a Java application built to explore the issues and benefits of using ontologies, application profiles and RDF for temporal annotation. It uses an annotation scheme based on hierarchical ontologies, and an RDF-based data model that may be adapted and extended through the use of RDF Schema. Dublin Core is used a...