RDF Data Sources Research Articles

Tuning fuzzy SPARQL queries

During the last years, the study of fuzzy database query languages has attracted the attention of many researchers. In this line of research, our group has proposed and developed FSA-SPARQL (Fuzzy Sets and Aggregators based SPARQL), which is a fuzzy extension of the Semantic Web query language SPARQL. FSA-SPARQL works with fuzzy RDF datasets and allows the definition of fuzzy queries involving fuzzy conditions through fuzzy connectives and aggregators. However, there are two main challenges to be solved for the practical applicability of FSA-SPARQL. The first problem is the lack of fuzzy RDF data sources. The second is how to customize fuzzy queries on fuzzy RDF data sources. Our research group has also recently proposed a fuzzy logic programming language called FASILL that offers powerful tuning capabilities that can accept applications in many fields. The purpose of this paper is to show how the FASILL tuning capabilities serve to accomplish in a unified framework both challenges in FSA-SPARQL: data fuzzification and query customization. More concretely, from a FSA-SPARQL to FASILL transformation, data fuzzification and query customization in FSA-SPARQL become FASILL tuning problems. We have validated the approach with queries against datasets from online communities.

Completeness and soundness guarantees for conjunctive SPARQL queries over RDF data sources with completeness statements1

Completeness and soundness guarantees for conjunctive SPARQL queries over RDF data sources with completeness statements1

Completeness Management for RDF Data Sources

The Semantic Web is commonly interpreted under the open-world assumption, meaning that information available (e.g., in a data source) captures only a subset of the reality. Therefore, there is no certainty about whether the available information provides a complete representation of the reality. The broad aim of this article is to contribute a formal study of how to describe the completeness of parts of the Semantic Web stored in RDF data sources. We introduce a theoretical framework allowing augmentation of RDF data sources with statements, also expressed in RDF, about their completeness. One immediate benefit of this framework is that now query answers can be complemented with information about their completeness. We study the impact of completeness statements on the complexity of query answering by considering different fragments of the SPARQL language, including the RDFS entailment regime, and the federated scenario. We implement an efficient method for reasoning about query completeness and provide an experimental evaluation in the presence of large sets of completeness statements.

Querying clinical data in HL7 RIM based relational model with morph-RDB

BackgroundSemantic interoperability is essential when carrying out post-genomic clinical trials where several institutions collaborate, since researchers and developers need to have an integrated view and access to heterogeneous data sources. One possible approach to accommodate this need is to use RDB2RDF systems that provide RDF datasets as the unified view. These RDF datasets may be materialized and stored in a triple store, or transformed into RDF in real time, as virtual RDF data sources. Our previous efforts involved materialized RDF datasets, hence losing data freshness.ResultsIn this paper we present a solution that uses an ontology based on the HL7 v3 Reference Information Model and a set of R2RML mappings that relate this ontology to an underlying relational database implementation, and where morph-RDB is used to expose a virtual, non-materialized SPARQL endpoint over the data.ConclusionsBy applying a set of optimization techniques on the SPARQL-to-SQL query translation algorithm, we can now issue SPARQL queries to the underlying relational data with generally acceptable performance.

An analytical study of large SPARQL query logs

With the adoption of RDF as the data model for Linked Data and the Semantic Web, query specification from end-users has become more and more common in SPARQL endpoints. In this paper, we conduct an in-depth analytical study of the queries formulated by end-users and harvested from large and up-to-date query logs from a wide variety of RDF data sources. As opposed to previous studies, ours is the first assessment on a voluminous query corpus, spanning over several years and covering many representative SPARQL endpoints. Apart from the syntactical structure of the queries, that exhibits already interesting results on this generalized corpus, we drill deeper in the structural characteristics related to the graph and hypergraph representation of queries. We outline the most common shapes of queries when visually displayed as undirected graphs, and characterize their (hyper-)tree width. Moreover, we analyze the evolution of queries over time, by introducing the novel concept of a streak, i.e., a sequence of queries that appear as subsequent modifications of a seed query. Our study offers several fresh insights on the already rich query features of real SPARQL queries formulated by real users, and brings us to draw a number of conclusions and pinpoint future directions for SPARQL query evaluation, query optimization, tuning, and benchmarking.

An analytical study of large SPARQL query logs

With the adoption of RDF as the data model for Linked Data and the Semantic Web, query specification from end-users has become more and more common in SPARQL endpoints. In this paper, we conduct an in-depth analytical study of the queries formulated by end-users and harvested from large and up-to-date query logs from a wide variety of RDF data sources. As opposed to previous studies, ours is the first assessment on a voluminous query corpus, spanning over several years and covering many representative SPARQL endpoints. Apart from the syntactical structure of the queries, that exhibits already interesting results on this generalized corpus, we drill deeper in the structural characteristics related to the graph and hypergraph representation of queries. We outline the most common shapes of queries when visually displayed as undirected graphs, and characterize their (hyper-)tree width. Moreover, we analyze the evolution of queries over time, by introducing the novel concept of a streak, i.e., a sequence of queries that appear as subsequent modifications of a seed query. Our study offers several fresh insights on the already rich query features of real SPARQL queries formulated by real users, and brings us to draw a number of conclusions and pinpoint future directions for SPARQL query evaluation, query optimization, tuning, and benchmarking.

SPARQL: Semantic Information Retrieval by Embedding Prepositions

Semantic web document representation is formulated using RDF/OWL. RDF representation is in the form of triples & OWL in form of ontologies. The above representation leads to a data set which needs to be queried using software agents, machines. W3C has recommended SPARQL to be the de facto query language for RDF. This paper proposes to suggest a model to enable SPARQL to make search efficient, easier and produce meaningful search distinguished on the basis of preposition. An RDF data source primarily consist of data that is represented in the form of a Triple pattern which has an appropriate RDF syntax and further results into an RDF Graph .The RDF repository stores the Data on Subject, Predicate, Object Model. The Predicate may also be thought of as a property linking the Subject and Object. This paper shall evaluate the information retrieval by incorporating preposition as property.

An automatic key discovery approach for data linking

In the context of Linked Data, different kinds of semantic links can be established between data. However when data sources are huge, detecting such links manually is not feasible. One of the most important types of links, the identity link, expresses that different identifiers refer to the same real world entity. Some automatic data linking approaches use keys to infer identity links, nevertheless this kind of knowledge is rarely available. In this work we propose KD2R, an approach which allows the automatic discovery of composite keys in RDF data sources that may conform to different schemas. We only consider data sources for which the Unique Name Assumption is fulfilled. The obtained keys are correct with respect to the RDF data sources in which they are discovered. The proposed algorithm is scalable since it allows the key discovery without having to scan all the data. KD2R has been tested on real datasets of the international contest OAEI 2010 and on datasets available on the web of data, and has obtained promising results.

An Automatic Key Discovery Approach for Data Linking

In the context of Linked Data, different kinds of semantic links can be established between data. However when data sources are huge, detecting such links manually is not feasible. One of the most important types of links, the identity link, expresses that different identifiers refer to the same real world entity. Some automatic data linking approaches use keys to infer identity links, nevertheless this kind of knowledge is rarely available. In this work we propose KD2R, an approach which allows the automatic discovery of composite keys in RDF data sources that may conform to different schemas. We only consider data sources for which the Unique Name Assumption is fulfilled. The obtained keys are correct with respect to the RDF data sources in which they are discovered. The proposed algorithm is scalable since it allows the key discovery without having to scan all the data. KD2R has been tested on real datasets of the international contest OAEI 2010 and on data sets available on the web of data, and has obtained promising results.

Ontology-Based Query in Heterogeneous and Distributed Data Sources

Information sharing, exchanging and retrieving from heterogeneous data sources not only needs complete data accessibility, but also it needs solving data heterogeneity between these data sources. To solve this rising problems of heterogeneity, a lot of recent work has been done towards solving this issue. This research aims to develop a software system based on ontology to semantically integrate heterogeneous data sources such as XML and RDF to solve some conflicts that occur in these sources. We developed an agent framework based on ontology to retrieve data from distributed heterogeneous data sources. With this technique, user will be able to send a mobile agent with classical input query to a data source. Then the mobile agent will carry the needed module of the global ontology prepared by the user stationary agents and transport itself from the user site to a remote XML or RDF data source. At remote XML data source, stationary agents will transform the heterogeneous XML source into temporary local RDF ontology. The stationary agents in all sources perform a mapping between the local and global ontologies, convert the query to XML or RDF query, execute it and set the results in a suitable form. Finally, the mobile agent will return back with the results. A partial implementation of this framework has been carried out using some modules and libraries of Java, Aglet, Jena and