Federating Queries to RDF repositories

Currently large amounts of RDF data are being published in the Web. These data is commonly accessed by means of SPARQL endpoints. However to query a set of SPARQL endpoints new mechanisms are needed due to neither the SPARQL protocol nor the language provide any norms or guidelines about how to proceed. In this paper we present an approach for federating queries to a set of SPARQL endpoints, using relational database distributed query processing techniques and part of the WS-DAI specification for web-service based access to relational and XML databases

Distributed Join Approaches for W3C-Conform SPARQL Endpoints

Currently many SPARQL endpoints are freely available and accessible without any costs to users: Everyone can submit SPARQL queries to SPARQL endpoints via a standardized protocol, where the queries are processed on the datasets of the SPARQL endpoints and the query results are sent back to the user in a standardized format. As these distributed execution environments for semantic big data (as intersection of semantic data and big data) are freely accessible, the Semantic Web is an ideal playground for big data research. However, when utilizing these distributed execution environments, questions about the performance arise. Especially when several datasets (locally and those residing in SPARQL endpoints) need to be combined, distributed joins need to be computed. In this work we give an overview of the various possibilities of distributed join processing in SPARQL endpoints, which follow the SPARQL specification and hence are "W3C conform". We also introduce new distributed join approaches as variants of the Bitvector-Join and combination of the Semi- and Bitvector-Join. Finally we compare all the existing and newly proposed distributed join approaches for W3C conform SPARQL endpoints in an extensive experimental evaluation

Semantic querying and search in distributed ontologies

We have observed in recent years a continuous growth in the quantity of RDF data accessible on the web. This evolution is primarily based on increasing data on the web by different sectors such as governments, life science researchers, or academic institutes. RDF data creation is mainly developed by replacing existing data resources with RDF, changing relational databases into RDF. These RDF data are usually called qualified linked data URIs and endpoints of SPARQL. Continuous development that we are experiencing in SPARQL endpoints requires accessing sets of distributed RDF data repositories is getting popularity. This research has offered an extensive analysis of accessing RDF data across distributed ontologies. The existing approaches lack a broad mix of RDF indexing and retrieving of distributed RDF data in one package. In addition, the efficiency of the current methods is not so dynamic and mainly depend on manual fixed strategies for accessing RDF data from a distributed environment. The literature review has acknowledged the need for a robust, reliable, dynamic, and comprehensive accessing mechanism for distributed RDF data using RDF indexing. This thesis presents the conceptual framework that demonstrates the SPARQL query execution process, which accesses the data within distributed RDF sets across a stored index. This thesis introduces the semantic algebra involved in the conversion of traditional SPARQL query language into different phases. The proposed framework elaborates the concepts included in selecting, projection, joins, specialisation and generalisation operators. These operators are usually in assistance during the process of processing and converting a SPARQL query. This thesis introduces the algorithms behind the proposed conceptual framework, which covert the main SPARQL query into sub-queries, sending each subquery to the required distributed repository to fetch the data and merging the sub queries results. 4 This research demonstrates the testing of the proposed framework using the unit and functional testing strategies. The author developed and utilised the Museum ontology to test and evaluate the developed system. It demonstrates all how the complete developed and processed system works. Different tests have been performed in this thesis, like the algebraic operator's test (e.g., select, join, outer join, generalisation, and specialisation operators test) and test the proposed algorithm. After comprehensive testing, it shows that all developed system units worked as expected, and no errors found during the testing of all phases of the tested framework. Finally, the thesis presents implemented framework's performance and accuracy by comparing it to other similar systems. Evaluation of the implemented system demonstrated that the proposed framework could handle distributed SPARQL queries very effectively. The author selected FedX, ANAPSID and ADERIS existing frameworks to compare with developed system and described the results in a graphical format to illustrate the performance and accuracy of all systems

User interfaces supporting entity search for linked data

One of the main goals of semantic search is to retrieve and connect information related to queries, offering users rich structured information about a topic instead of a set of documents relevant to the topic. Previous work reports that searching for information about individual entities such as persons, places and organisations is the most common form of Web search. Since the Semantic Web was first proposed, the amount of structured data on the Web has increased dramatically. This is particularly the case for what is known as Linked Data, information that has been published using Semantic Web standards such as RDF and OWL. Such structured data opens up new possibilities for improving entity search on the Web, integrating facts from independent sources, and presenting users with contextually-rich information about entities. This research focuses on entity search of Linked Data in terms of three different forms of search: structured queries, where users can use the SPARQL query language for manipulating data sources; exploratory search, where users can browse from one entity to another; and focused search, where users can input an entity query as a free text keyword search. We undertake a comparative study between two distinct information architectures for structured querying to manipulate Linked Data over the Web. Specifically, we evaluate some of the main operators in SPARQL using several datasets of Linked Data. We introduce a framework of five criteria to evaluate 15 current state-of-the-art semantic tools available for exploratory search of Linked Data, in order to establish how well these browsers make available the benefits of Linked Data and entity search for human users. We also use the criteria to determine the browsers that are best suited to entity exploration. Further, we propose a new model, the Attribute Importance Model, for entity-aggregated search, with the purpose of improving user experience when finding information about entities. The model develops three techniques: (1) presenting entity type-based query suggestions; (2) clustering aggregated attributes; and (3) ranking attributes based on their importance to a given query. Together these constitute a model for developing more informative views and enhancing users’ understanding of entity descriptions on the Web. We then use our model to provide an interactive approach, with the Information Visualisation toolkit InfoVis, that enables users to visualise entity clusters generated by our Attribute Importance Model. Thus this thesis addresses two challenges of searching Linked Data. The first challenge concerns the specific issue of information resolution during the search: the reduction of query ambiguity and redundant results that contain irrelevant descriptions when searching for information about an entity. The second challenge concerns the more general problem of technical complexity, and addresses to the limited adoption of Linked Data that we ascribe to the lack of understanding of Semantic Web technologies and data structures among general users. These technologies pose new design problems for human interaction such as overloading data, navigation styles, and browsing mechanisms. The Attribute Importance Model addresses both these challenges