Abstract. In this paper, we argue that query relaxation over RDF data is an important but largely overlooked research topic: the Semantic Web standards allow for answering crisp queries over crisp RDF data, but what of use-cases that require approximate answers for fuzzy queries over crisp data? We introduce a use-case from an EADS project that aims to aggregate intelligence information for police post-incident analysis. Query relaxation is needed to match incomplete descriptions of entities involved in crimes to structured descriptions thereof. We first discuss the use-case, formalise the problem, and survey current literature for possible approaches. We then present a proof-of-concept framework for enabling relaxation of structured entity-lookup queries, evaluating different distance measures for performing relaxation. We argue that beyond our specific scenario, query relaxation is important to many potential use-cases for Semantic Web technologies, and worthy of more attention. 1

There has been a recent, tangible growth in RDF published on the Web in accordance with the Linked Data principles and best practices, the result of which has been dubbed the “Web of Data”. Linked Data guidelines are designed to facilitate ad hoc re-use and integration of conformant structured data—across the Web—by consumer applications; however, thus far, systems have yet to emerge that convincingly demonstrate the potential applications for consuming currently available Linked Data. Herein, we compile a list of fourteen concrete guidelines as given in the “How to Publish Linked Data on the Web ” tutorial. Thereafter, we evaluate conformance of current RDF data providers with respect to these guidelines. Our evaluation is based on quantitative empirical analyses of a crawl of ∼4 million RDF/XML documents constituting over 1 billion quadruples, where we also look at the stability of hosted documents for a corpus consisting of nine monthly snapshots from a sample of 151 thousand documents. Backed by our empirical survey, we provide insights into the current level of conformance with respect to various Linked Data guidelines, enumerating lists of the most (non-)conformant data providers. We show that certain guidelines are broadly adhered to (esp. use HTTP URIs, keep URIs stable), whilst others are commonly overlooked (esp. provide licencing and human-readable meta-data). We also compare PageRank scores for the data-providers and their conformance to Linked Data guidelines, showing that both factors negatively correlate for guidelines restricting use of RDF features, while positively correlating for guidelines encouraging external linkage and vocabulary re-use. Finally, we present a summary of conformance for the different guidelines, and present the top-ranked data providers in terms of a combined PageRank and Linked Data conformance score. Key words: linked data, web of data, semantic web, rdf, web

With respect to large-scale, static, Linked Data corpora, in this paper we discuss scalable and distributed methods for entity consolidation (aka. smushing, entity resolution, object consolidation, etc.) to locate and process names that signify the same entity. We investigate (i) a baseline approach, which uses explicit owl:sameAs relations to perform consolidation; (ii) extended entity consolidation which additionally uses a subset of OWL 2 RL/RDF rules to derive novel owl:sameAs relations through the semantics of inverse-functional properties, functional-properties and (max-)cardinality restrictions with value one; (iii) deriving weighted concurrence measures between entities in the corpus based on shared inlinks/outlinks and attribute values using statistical analyses; (iv) disambiguating (initially) consolidated entities based on inconsistency detection using OWL 2 RL/RDF rules. Our methods are based upon distributed sorts and scans of the corpus, where we deliberately avoid the requirement for indexing all data. Throughout, we offer evaluation over a diverse Linked Data corpus consisting of 1.118 billion quadruples derived from a domain-agnostic, open crawl of 3.985 million RDF/XML Web documents, demonstrating the feasibility of our methods at that scale, and giving insights into the quality of the results for real-world data. Key words: entity consolidation, web data, linked data, rdf

One of the main challenges that the Semantic Web faces is the integration of a growing number of independently designed ontologies. In this work, we present paris, an approach for the automatic alignment of ontologies. paris aligns not only instances, but also relations and classes. Alignments at the instance level cross-fertilize with alignments at the schema level. Thereby, our system provides a truly holistic solution to the problem of ontology alignment. The heart of the approach is probabilistic, i.e., we measure degrees of matchings based on probability estimates. This allows paris to run without any parameter tuning. We demonstrate the efficiency of the algorithm and its precision through extensive experiments. In particular, we obtain a precision of around 90 % in experiments with some of the world’s largest ontologies. 1.

The Internet has enabled the creation of a growing number of large-scale knowledge bases in a variety of domains containing complementary information. Tools for automatically aligning these knowledge bases would make it possible to unify many sources of structured knowledge and answer complex queries. However, the efficient alignment of large-scale knowledge bases still poses a considerable challenge. Here, we present Simple Greedy Matching (SiGMa), a simple algorithm for aligning knowledge bases with millions of entities and facts. SiGMa is an iterative propagation algorithm which leverages both the structural information from the relationship graph as well as flexible similarity measures between entity properties in a greedy local search, thus making it scalable. Despite its greedy nature, our experiments indicate that SiGMa can efficiently match some of the world’s largest knowledge bases with high accuracy. We provide additional experiments on benchmark datasets which demonstrate that SiGMa can outperform state-of-the-art approaches both in accuracy and efficiency. 1

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