RDF Datasets Research Articles

A demonstration of MAGiQ

Existing RDF engines follow one of two design paradigms: relational or graph-based. Such engines are typically designed for specific hardware architectures, mainly CPUs, and are not easily portable to new architectures. Porting an existing engine to a different architecture (e.g., many-core architectures) entails almost redesign from scratch. We explore sparse matrix algebra as a third paradigm for designing a portable, scalable, and efficient RDF engine. We demonstrate MAGiQ; a matrix algebra approach for evaluating complex SPARQL queries over large RDF datasets. MAGiQ represents an RDF graph as a sparse matrix, and translates SPARQL queries to matrix algebra programs. MAGiQ takes advantage of the existing rich software infrastructure for processing sparse matrices, optimized for many architectures (e.g., CPUs, GPUs, distributed), effortlessly. This demo motivates the adoption of matrix algebra in RDF graph processing by showing MAGiQ's performance with different matrix algebra backend engines. MAGiQ, using a GPU, is orders of magnitude faster in solving complex queries on a billion edge graph than state-of-the-art RDF systems.

A study of semantic integration across archaeological data and reports in different languages

This study investigates the semantic integration of data extracted from archaeological datasets with information extracted via natural language processing (NLP) across different languages. The investigation follows a broad theme relating to wooden objects and their dating via dendrochronological techniques, including types of wooden material, samples taken and wooden objects including shipwrecks. The outcomes are an integrated RDF dataset coupled with an associated interactive research demonstrator query builder application. The semantic framework combines the CIDOC Conceptual Reference Model (CRM) with the Getty Art and Architecture Thesaurus (AAT). The NLP, data cleansing and integration methods are described in detail together with illustrative scenarios from the web application Demonstrator. Reflections and recommendations from the study are discussed. The Demonstrator is a novel SPARQL web application, with CRM/AAT-based data integration. Functionality includes the combination of free text and semantic search with browsing on semantic links, hierarchical and associative relationship thesaurus query expansion. Queries concern wooden objects (e.g. samples of beech wood keels), optionally from a given date range, with automatic expansion over AAT hierarchies of wood types and specialised associative relationships. Following a ‘mapping pattern’ approach (via the STELETO tool) ensured validity and consistency of all RDF output. The user is shielded from the complexity of the underlying semantic framework by a query builder user interface. The study demonstrates the feasibility of connecting information extracted from datasets and grey literature reports in different languages and semantic cross-searching of the integrated information. The semantic linking of textual reports and datasets opens new possibilities for integrative research across diverse resources.

Answering why-not questions on SPARQL queries

SPARQL, the W3C standard for RDF query languages, has gained significant popularity in recent years. An increasing amount of effort is currently being exerted to improve the functionality and usability of SPARQL-based search engines. However, explaining missing items in the results of SPARQL queries or the so-called why-not question has not received sufficient attention. In this study, we first formalize why-not questions on SPARQL queries and then propose a novel explanation model, called answering why-not questions on SPARQL (ANNA) to answer why-not questions using a divide-and-conquer strategy. ANNA adopts a graph-based approach and an operator-based approach to generate logical explanations at the triple pattern level and the query operator level, respectively, which helps users refine their initial queries. Extensive experimental results on two real-world RDF datasets show that the proposed model and algorithms can provide high-quality explanations in terms of both effectiveness and efficiency.

Decentralized Collaborative Knowledge Management Using Git

The World Wide Web and the Semantic Web are designed as a network of distributed services and datasets. The distributed character of the Web brings manifold collaborative possibilities to interchange data. The commonly adopted collaborative solutions for RDF data are centralized (e. g. SPARQL endpoints and wiki systems). But to support distributed collaboration, a system is needed, that supports divergence of datasets, brings the possibility to conflate diverged states, and allows distributed datasets to be synchronized. In this paper, we present Quit Store, it was inspired by and it builds upon the successful Git system. The approach is based on a formal expression of evolution and consolidation of distributed datasets. During the collaborative curation process, the system automatically versions the RDF dataset and tracks provenance information. It also provides support to branch, merge, and synchronize distributed RDF datasets. The merging process is guarded by specific merge strategies for RDF data. Finally, we use our reference implementation to show overall good performance and demonstrate the practical usability of the system.

Lusail

The RDF data model allows publishing interlinked RDF datasets, where each dataset is independently maintained and is queryable via a SPARQL endpoint. Many applications would benefit from querying the resulting large, decentralized, geo-distributed graph through a federated SPARQL query processor. A crucial factor for good performance in federated query processing is pushing as much computation as possible to the local endpoints. Surprisingly, existing federated SPARQL engines are not effective at this task since they rely only on schema information. Consequently, they cause unnecessary data retrieval and communication, leading to poor scalability and response time. This paper addresses these limitations and presents Lusail , a scalable and efficient federated SPARQL system for querying large RDF graphs that are geo-distributed on different endpoints. Lusail uses a novel query rewriting algorithm to push computation to the local endpoints by relying on information about the RDF instances and not only the schema. The query rewriting algorithm has the additional advantage of exposing parallelism in query processing, which Lusail exploits through advanced scheduling at query run time. Our experiments on billions of triples of real and synthetic data show that Lusail outperforms state-of-the-art systems by orders of magnitude in terms of scalability and response time.

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.

Stylus

RDF is one of the most commonly used knowledge representation forms. Many highly influential knowledge bases, such as Freebase and PubChemRDF, are in RDF format. An RDF data set is usually represented as a collection of subject-predicate-object triples. Despite the flexibility of RDF triples, it is challenging to serve SPARQL queries on RDF data efficiently by directly managing triples due to the following two reasons. First, heavy joins on a large number of triples are needed for query processing, resulting in a large number of data scans and large redundant intermediate results; Second, weakly-typed triple representation provides suboptimal random access - typically with logarithmic complexity. This data access challenge, unfortunately, cannot be easily met by a better query optimizer as large graph processing is extremely I/O-intensive. In this paper, we argue that strongly-typed graph representation is the key to high-performance RDF query processing. We propose Stylus - a strongly-typed store for serving massive RDF data. Stylus exploits a strongly-typed storage scheme to boost the performance of RDF query processing. The storage scheme is essentially a materialized join view on entities, it thus can eliminate a large number of unnecessary joins on triples. Moreover, it is equipped with a compact representation for intermediate results and an efficient graph-decomposition based query planner. Experimental results on both synthetic and real-life RDF data sets confirm that the proposed approach can dramatically boost the performance of SPARQL query processing.

Possibilistic testing of OWL axioms against RDF data

We develop the theory of a possibilistic framework for OWL 2 axiom testing against RDF datasets, as an alternative to statistics-based heuristics. The intuition behind it is to evaluate the credibility of OWL 2 axioms based on the evidence available in the form of a set of facts contained in a chosen RDF dataset. To achieve it, we first define the notions of development, content, support, confirmation and counterexample of an axiom. Then we use these notions to define the possibility and necessity of an axiom and its acceptance/rejection index combining both of them. Finally, we report a practical application of the proposed framework to test SubClassOf axioms against the DBpedia RDF dataset.

A survey and experimental comparison of distributed SPARQL engines for very large RDF data

Distributed SPARQL engines promise to support very large RDF datasets by utilizing shared-nothing computer clusters. Some are based on distributed frameworks such as MapReduce; others implement proprietary distributed processing; and some rely on expensive preprocessing for data partitioning. These systems exhibit a variety of trade-offs that are not well-understood, due to the lack of any comprehensive quantitative and qualitative evaluation. In this paper, we present a survey of 22 state-of-the-art systems that cover the entire spectrum of distributed RDF data processing and categorize them by several characteristics. Then, we select 12 representative systems and perform extensive experimental evaluation with respect to preprocessing cost, query performance, scalability and workload adaptability, using a variety of synthetic and real large datasets with up to 4.3 billion triples. Our results provide valuable insights for practitioners to understand the trade-offs for their usage scenarios. Finally, we publish online our evaluation framework, including all datasets and workloads, for researchers to compare their novel systems against the existing ones.

Enhancing Semantic Expressivity in the Cultural Heritage Domain

Describing cultural heritage objects from the perspective of Linked Open Data (LOD) is not a trivial task. The process often requires not only choosing pertinent ontologies but also developing new models that preserve the most information and express the semantic power of cultural heritage data. Indeed, data managed in archives, libraries, and museums are complex objects themselves, which require a deep reflection on even nonconventional conceptual models. Starting from these considerations, this article describes a research project: to expose the vastness of one of the most important collections of European cultural heritage, the Zeri Photo Archive, as LOD. We describe here the steps we undertook to this end. First, we developed two ad hoc ontologies for describing all issues not completely covered by existent models (the F Entry and the OA Entry Ontology ). Then we mapped into RDF the descriptive elements used in the current Zeri Photo Archive catalog, converting into CIDOC CRM and into the two new aforementioned models the source data based on the Italian content standards Scheda F ( photography entry , in English) and Scheda OA ( work of art entry , in English). Finally, we created an RDF dataset of the output of the mapping that could show a result capable of demonstrating the complexity of our scenario.

Enhancing answer completeness of SPARQL queries via crowdsourcing

Linked Open Data initiatives have encouraged the publication of large RDF datasets into the Linking Open Data (LOD) cloud, including DBpedia, YAGO, and Geo-Names. Despite the size of LOD datasets and the development of (semi-)automatic methods to create and link LOD data, these datasets may be still incomplete, negatively affecting thus accuracy of Linked Data processing techniques. We acquire query answer completeness by capturing knowledge collected from the crowd, and propose a novel hybrid query processing engine that brings together machine and human computation to execute SPARQL queries. Our system, HARE, implements these hybrid query processing techniques. HARE encompasses several features: (1) a completeness model for RDF that exploits the characteristics of RDF in order to estimate the completeness of an RDF dataset; (2) a crowd knowledge base that captures crowd answers about missing values in the RDF dataset; (3) a query engine that combines on-the-fly crowd knowledge and estimates provided by the RDF completeness model, to decide upon the sub-queries of a SPARQL query that should be executed against the dataset or via crowd computing to enhance query answer completeness; and (4) a microtask manager that exploits the semantics encoded in the dataset RDF properties, to crowdsource SPARQL sub-queries as microtasks and update the crowd knowledge base with the results from the crowd. Effectiveness and efficiency of HARE are empirically studied on a collection of 50 SPARQL queries against the DBpedia dataset. Experimental results clearly show that our solution accurately enhances answer completeness.

Distributed Top-K Join Queries Optimizing for RDF Datasets

In recent years, the scale of RDF datasets is increasing rapidly, the query research on RDF datasets in the transitional centralized environment is unable to meet the increasing demand of data query field, especially the top-k query. Based on the Spark distributed computing system and the HBase distributed storage system, a novel method is proposed for top-k query. A top–k query plan STA (Spark Threshold Algorithm) is proposed to reduce the connection operation of RDF data. Furthermore, a better algorithm SSJA (Spark Simple Join Algorithm) is presented to reduce the sorting related operations for the intermediate data. A cache mechanism is also proposed to speed up the SSJA algorithm. The experimental results show that the SSJA algorithm performs better than the STA algorithm in term of the cost and applicability, and it can significantly improve the SSJA's performance by introducing the cache mechanism.

Pattern oriented RDF graphs exploration

An increasing number of RDF datasets are available on the Web. In order to query these datasets, users must have some information about their content as well as some knowledge of a query language such as SPARQL. Our goal is to facilitate the exploration of these datasets. In this paper, we introduce two complementary approaches designed to explore RDF(S)/OWL data: theme-based exploration and keyword search. These two approaches rely on the definition of patterns to formalize users' requirements during the exploration process. We present PatEx, a system designed to explore RDF(S)/OWL datasets using the two exploration strategies, allowing the user to interactively switch between them. We also present some experiments on real datasets to illustrate the effectiveness of our approach.

Automating data citation: the eagle-i experience.

Data citation is of growing concern for owners of curated databases, who wish to give credit to the contributors and curators responsible for portions of the dataset and enable the data retrieved by a query to be later examined. While several databases specify how data should be cited, they leave it to users to manually construct the citations and do not generate them automatically. We report our experiences in automating data citation for an RDF dataset called eagle-i, and discuss how to generalize this to a citation framework that can work across a variety of different types of databases (e.g. relational, XML, and RDF). We also describe how a database administrator would use this framework to automate citation for a particular dataset.

Type Prediction in Noisy RDF Knowledge Bases Using Hierarchical Multilabel Classification with Graph and Latent Features

Semantic Web knowledge bases, in particular large cross-domain data, are often noisy, incorrect, and incomplete with respect to type information. This incompleteness can be reduced, as previous work shows, with automatic type prediction methods. Most knowledge bases contain an ontology defining a type hierarchy, and, in general, entities are allowed to have multiple types (classes of an instance assigned with the rdf:type relation). In this paper, we exploit these characteristics and formulate the type prediction problem as hierarchical multi classification, where the labels are types. We evaluate different sets of features, including entity embeddings, which can be extracted from the knowledge graph exclusively. We propose SLCN, a modification of the local classifier per node approach, which performs feature selection, instance sampling, and class balancing for each local classifier with the objective of improving scalability. Furthermore, we explore different variants of creating features for the classifier, including both graph and latent features. We compare the performance of our proposed method with the state-of-the-art type prediction approach and popular hierarchical multilabel classifiers, and report on experiments with large-scale cross-domain RDF datasets.

A RADAR for information reconciliation in Question Answering systems over Linked Data1

In the latest years, more and more structured data is published on the Web and the need to support typical Web users to access this body of information has become of crucial importance. Question Answering systems over Linked Data try to address this need by allowing users to query Linked Data using natural language. These systems may query at the same time different heterogenous interlinked datasets, that may provide different results for the same query. The obtained results can be related by a wide range of heterogenous relations, e.g., one can be the specification of the other, an acronym of the other, etc. In other cases, such results can contain an inconsistent set of information about the same topic. A well known example of such heterogenous interlinked datasets are language-specific DBpedia chapters, where the same information may be reported in different languages. Given the growing importance of multilingualism in the Semantic Web community, and in Question Answering over Linked Data in particular, we choose to apply information reconciliation to this scenario. In this paper, we address the issue of reconciling information obtained by querying the SPARQL endpoints of language-specific DBpedia chapters. Starting from a categorization of the possible relations among the resulting instances, we provide a framework to: (i) classify such relations, (ii) reconcile information using argumentation theory, (iii) rank the alternative results depending on the confidence of the source in case of inconsistencies, and (iv) explain the reasons underlying the proposed ranking. We release the resource obtained applying our framework to a set of language-specific DBpedia chapters, and we integrate such framework in the Question Answering system QAKiS, that exploits such chapters as RDF datasets to be queried using a natural language interface.

Enhancing Answer Completeness of SPARQL Queries via Crowdsourcing

Linked Open Data initiatives have encouraged the publication of large RDF datasets into the Linking Open Data (LOD) cloud, including DBpedia, YAGO, and Geo-Names. Despite the size of LOD datasets and the development of (semi-)automatic methods to create and link LOD data, these datasets may be still incomplete, negatively affecting thus accuracy of Linked Data processing techniques. We acquire query answer completeness by capturing knowledge collected from the crowd, and propose a novel hybrid query processing engine that brings together machine and human computation to execute SPARQL queries. Our system, HARE, implements these hybrid query processing techniques. HARE encompasses several features: (1) a completeness model for RDF that exploits the characteristics of RDF in order to estimate the completeness of an RDF dataset; (2) a crowd knowledge base that captures crowd answers about missing values in the RDF dataset; (3) a query engine that combines on-the-fly crowd knowledge and estimates provided by the RDF completeness model, to decide upon the sub-queries of a SPARQL query that should be executed against the dataset or via crowd computing to enhance query answer completeness; and (4) a microtask manager that exploits the semantics encoded in the dataset RDF properties, to crowdsource SPARQL subqueries as microtasks and update the crowd knowledge base with the results from the crowd. Effectiveness and efficiency of HARE are empirically studied on a collection of 50 SPARQL queries against the DBpedia dataset. Experimental results clearly show that our solution accurately enhances answer completeness.

Change management and validation for collaborative editing of RDF datasets

The dynamic and distributed nature of the Semantic Web implies that datasets are often the result of collective participation rather than isolated works. Change management, provenance tracking and validation of changes performed by contributing agents are all requirements of systems for collaborative dataset development. Different scenarios may as well require mechanisms to foster consensus, resolve conflicts between competing changes, reversing or ignoring changes etc. In this paper, we perform a landscape analysis of version control for RDF datasets, emphasising the importance of change reversion to support validation. Firstly, we discuss different representations of changes in RDF datasets and introduce higher-level perspectives on change. Secondly, we analyse diverse approaches to version control. We conclude by focusing on validation, characterising it as a separate need from the mere preservation of different versions of a dataset.

Change management and validation for collaborative editing of RDF datasets

Change management and validation for collaborative editing of RDF datasets

A Novel Approach for Semantic Integration of Data using Ontology

Integration process of data is recently recognized like a significant visualization of the Semantic Web explore for which researchers focus on numerous areas, such as integration of information, ontologies and databases. Objectives: Users generally requires an incorporated analysis of information accessible from various data and it was proposed to grant users with this view of data. Methods: The Meta data is created from different data sets like excel data set, RDF data set and XML data set. Findings: In this work a survey was made for integrating databases using ontology and a new approach for integration of databases is devised for finding correspondence between ontologies. Applications: View and Search functionality was provided by filtering data from Meta data. This provides easy access to the integrated data.