FishMark: A Linked Data Application Benchmark

Abstract. FishBase is an important species data collection produced by the FishBase Information and Research Group Inc (FIN), a not-forprofit NGO with the aim of collecting comprehensive information (from the taxonomic to the ecological) about all the world’s finned fish species. FishBase is exposed as a MySQL backed website (supporting a range of canned, although complex queries) and serves over 33 million hits per month. FishDelish is a transformation of FishBase into LinkedData weighing in at 1.38 billion triples. We have ported a substantial number of FishBase SQL queries to FishDelish SPARQL query which form the basis of a new linked data application benchmark (using our derivative of the Berlin SPARQL Benchmark harness). We use this benchmarking framework to compare the performance of the native MySQL application, the Virtuoso RDF triple store, and the Quest OBDA system on a fishbase.org like application.

Assessing and refining mappings to RDF to improve dataset quality

RDF dataset quality assessment is currently performed primarily after data is published. However, there is neither a systematic way to incorporate its results into the dataset nor the assessment into the publishing workflow. Adjustments are manually -but rarely- applied. Nevertheless, the root of the violations which often derive from the mappings that specify how the RDF dataset will be generated, is not identified. We suggest an incremental, iterative and uniform validation workflow for RDF datasets stemming originally from (semi-) structured data (e.g., CSV, XML, JSON). In this work, we focus on assessing and improving their mappings. We incorporate (i) a test-driven approach for assessing the mappings instead of the RDF dataset itself, as mappings reflect how the dataset will be formed when generated; and (ii) perform semi-automatic mapping refinements based on the results of the quality assessment. The proposed workflow is applied to diverse cases, e.g., large, crowdsourced datasets such as DBpedia, or newly generated, such as iLastic. Our evaluation indicates the efficiency of our workflow, as it significantly improves the overall quality of an RDF dataset in the observed cases

Milan: automatic generation of R2RML mappings

Milan automatically generates R2RML mappings between a source relational database and a target ontology, using a novel multi-level algorithms. It address real world inter-model semantic gap by resolving naming conflicts, structural and semantic heterogeneity, thus enabling high fidelity mapping generation for realistic databases. Despite the importance of mappings for interoperability across relational databases and ontologies, a labour and expertise-intensive task, the current state of the art has achieved only limited automation. The paper describes an experimental evaluation of Milan with respect to the state of the art systems using the RODI benchmarking tool which shows that Milan outperforms all systems in all categorie

Semantic Query Reformulation in Social PDMS

We consider social peer-to-peer data management systems (PDMS), where each peer maintains both semantic mappings between its schema and some acquaintances, and social links with peer friends. In this context, reformulating a query from a peer's schema into other peer's schemas is a hard problem, as it may generate as many rewritings as the set of mappings from that peer to the outside and transitively on, by eventually traversing the entire network. However, not all the obtained rewritings are relevant to a given query. In this paper, we address this problem by inspecting semantic mappings and social links to find only relevant rewritings. We propose a new notion of 'relevance' of a query with respect to a mapping, and, based on this notion, a new semantic query reformulation approach for social PDMS, which achieves great accuracy and flexibility. To find rapidly the most interesting mappings, we combine several techniques: (i) social links are expressed as FOAF (Friend of a Friend) links to characterize peer's friendship and compact mapping summaries are used to obtain mapping descriptions; (ii) local semantic views are special views that contain information about external mappings; and (iii) gossiping techniques improve the search of relevant mappings. Our experimental evaluation, based on a prototype on top of PeerSim and a simulated network demonstrate that our solution yields greater recall, compared to traditional query translation approaches proposed in the literature.Comment: 29 pages, 8 figures, query rewriting in PDM

Evaluating (linked) metadata transformations across cultural heritage domains

This paper describes an approach to the evaluation of different aspects in the transformation of existing metadata into Linked data-compliant knowledge bases. At Oslo and Akershus University College of Applied Sciences, in the TORCH project, we are working on three different experimental case studies on extraction and mapping of broadcasting data and the interlinking of these with transformed library data. The case studies are investigating problems of heterogeneity and ambiguity in and between the domains, as well as problems arising in the interlinking process. The proposed approach makes it possible to collaborate on evaluation across different experiments, and to rationalize and streamline the process

On the Foundations of Data Interoperability and Semantic Search on the Web

This dissertation studies the problem of facilitating semantic search across disparate ontologies that are developed by different organizations. There is tremendous potential in enabling users to search independent ontologies and discover knowledge in a serendipitous fashion, i.e., often completely unintended by the developers of the ontologies. The main difficulty with such search is that users generally do not have any control over the naming conventions and content of the ontologies. Thus terms must be appropriately mapped across ontologies based on their meaning. The meaning-based search of data is referred to as semantic search, and its facilitation (aka semantic interoperability) then requires mapping between ontologies. In relational databases, searching across organizational boundaries currently involves the difficult task of setting up a rigid information integration system. Linked Data representations more flexibly tackle the problem of searching across organizational boundaries on the Web. However, there exists no consensus on how ontology mapping should be performed for this scenario, and the problem is open. We lay out the foundations of semantic search on the Web of Data by comparing it to keyword search in the relational model and by providing effective mechanisms to facilitate data interoperability across organizational boundaries. We identify two sharply distinct goals for ontology mapping based on real-world use cases. These goals are: (i) ontology development, and (ii) facilitating interoperability. We systematically analyze these goals, side-by-side, and contrast them. Our analysis demonstrates the implications of the goals on how to perform ontology mapping and how to represent the mappings. We rigorously compare facilitating interoperability between ontologies to information integration in databases. Based on the comparison, class matching is emphasized as a critical part of facilitating interoperability. For class matching, various class similarity metrics are formalized and an algorithm that utilizes these metrics is designed. We also experimentally evaluate the effectiveness of the class similarity metrics on real-world ontologies. In order to encode the correspondences between ontologies for interoperability, we develop a novel W3C-compliant representation, named skeleton