A Hybrid Approach to Perform Efficient and Effective Query Execution Against Public SPARQL Endpoints

Linked Open Data (LOD) is a huge effort in the direction of making the Web of Data a reality. The LOD cloud consists of about 200 datasets contributed by several independent data providers from various domains such as Publications, Geography, Government, Media, Biology and Drugs etc. The data is represented in RDF and SPARQL is the query language. Due to the open nature of the data and its heterogeneous origin, it is often the case that a real-world entity appears in different datasets and with different names. When such entities are to be reported in the query results, a mechanism of rank ordering them becomes essential. In this paper, we propose a new framework for calculating the importance scores of datasets and also resources inside the datasets. As consensus is a key element that adds value to the data in the Web of Data, we base our ranking framework on constructs that are used to express sameness or equivalence among the entities in RDF data. We also make use of the underlying graph structure of LOD in the framework. The framework is experimentally verified on the Billion Triple Challenge (BTC-2010) dataset. The results indicate that the framework is successful in giving entities from the most relevant dataset a higher score compared to other datasets.

There is currently a large amount of publicly accessible structured data available as RDF data sets. For example, the Linked Open Data (LOD) cloud now consists of thousands of RDF data sets with over 30 billion triples, and the number and size of the data sets is continuously growing. Many of the data sets in the LOD cloud provide public SPARQL endpoints to allow issuing queries over them. These end-points enable users to retrieve data using precise and highly expressive SPARQL queries. However, in order to do so, the user must have sufficient knowledge about the data sets that she wishes to query, that is, the structure of data, the vocabulary used within the data set, the exact values of literals, their data types, etc. Thus, while SPARQL is powerful, it is not easy to use. An alternative to SPARQL that does not require as much prior knowledge of the data is some form of keyword search over the structured data. Keyword search queries are easy to use, but inherently ambiguous in describing structured queries. This demonstration introduces Sapphire, a system for querying RDF data that strikes a middle ground between ambiguous keyword search and difficult-to-use SPARQL. Our system does not replace either, but utilizes both where they are most effective. Sapphire helps the user construct expressive SPARQL queries that represent her information needs without requiring detailed knowledge about the queried data sets. These queries are then executed over public SPARQL endpoints from the LOD cloud. Sapphire guides the user in the query writing process by showing suggestions of query terms based on the queried data, and by recommending changes to the query based on a predictive user model.

BackgroundNucleotide and protein sequence feature annotations are essential to understand biology on the genomic, transcriptomic, and proteomic level. Using Semantic Web technologies to query biological annotations, there was no standard that described this potentially complex location information as subject-predicate-object triples.DescriptionWe have developed an ontology, the Feature Annotation Location Description Ontology (FALDO), to describe the positions of annotated features on linear and circular sequences. FALDO can be used to describe nucleotide features in sequence records, protein annotations, and glycan binding sites, among other features in coordinate systems of the aforementioned “omics” areas. Using the same data format to represent sequence positions that are independent of file formats allows us to integrate sequence data from multiple sources and data types. The genome browser JBrowse is used to demonstrate accessing multiple SPARQL endpoints to display genomic feature annotations, as well as protein annotations from UniProt mapped to genomic locations.ConclusionsOur ontology allows users to uniformly describe – and potentially merge – sequence annotations from multiple sources. Data sources using FALDO can prospectively be retrieved using federalised SPARQL queries against public SPARQL endpoints and/or local private triple stores.

sparql is the w3c standard query language for querying data expressed in the Resource Description Framework (rdf). The increasing amounts of rdf data available raise a major need and research interest in building efficient and scalable distributed sparql query evaluators. In this context, we propose sparqlgx: our implementation of a distributed rdf datastore based on Apache Spark. sparqlgx is designed to leverage existing Hadoop infrastructures for evaluating sparql queries. sparqlgx relies on a translation of sparql queries into executable Spark code that adopts evaluation strategies according to (1) the storage method used and (2) statistics on data. We show that sparqlgx makes it possible to evaluate sparql queries on billions of triples distributed across multiple nodes, while providing attractive performance figures. We report on experiments which show how sparqlgx compares to related state-of-the-art implementations and we show that our approach scales better than these systems in terms of supported dataset size. With its simple design, sparqlgx represents an interesting alternative in several scenarios.

With the popularity of Linked Open Data (LOD) and the associated rise in freely accessible knowledge that can be accessed via LOD, exploiting LOD for recommender systems has been widely studied based on various approaches such as graph-based or using different machine learning models with LOD-enabled features. Many of the previous approaches require construction of an additional graph to run graph-based algorithms or to extract path-based features by combining user-item interactions (e.g., likes, dislikes) and background knowledge from LOD. In this paper, we investigate Factorization Machines (FMs) based on particularly lightweight LOD-enabled features which can be directly obtained via a public SPARQL Endpoint without any additional effort to construct a graph. Firstly, we aim to study whether using FM with these lightweight LOD-enabled features can provide competitive performance compared to a learning-to-rank approach leveraging LOD as well as other well-established approaches such as kNN-item and BPRMF. Secondly, we are interested in finding out to what extent each set of LOD-enabled features contributes to the recommendation performance. Experimental evaluation on a standard dataset shows that our proposed approach using FM with lightweight LOD-enabled features provides the best performance compared to other approaches in terms of five evaluation metrics. In addition, the study of the recommendation performance based on different sets of LOD-enabled features indicate that property-object lists and PageRank scores of items are useful for improving the performance, and can provide the best performance through using them together for FM. We observe that subject-property lists of items does not contribute to the recommendation performance but rather decreases the performance.

SPARQL endpoints are web services that allow for querying RDF knowledge graphs online using the SPARQL query language. In SPARQL 1.1, the federation extension allows for explicitly evaluating expressions of a query at certain SPARQL endpoints using the SERVICE keyword. However, the current foundations of federated SPARQL query processing rely on two central assumptions: (i) members of the federation are assumed to be equal in terms of quality, and (ii) a strict definition of answer completeness, where a SPARQL expression is evaluated over all relevant endpoints. These assumptions hinder the scalability of federated query processing approaches, as the number of contacted sources is potentially high. To address this challenge, in this work, we propose a solution that supports quality descriptions about federation members, and further exploit these descriptions to prune low-quality sources and to speed up query execution. To this end, we present a definition of quality-aware federations of SPARQL endpoints. Moreover, we define and investigate the semantics of recurring ALTERNATIVE SERVICE expressions which are the union of SERVICE requests around the same expression. We then devise a utility-aware semantics for them, in which quality information about the federation is leveraged to potentially reduce the number of endpoints to be contacted. We evaluate the proposed semantics in an experimental study and investigate the utility, execution time, and answer completeness of the proposed semantics for queries in over 60 quality-aware federations of varying size and quality.

Being able to query online public knowledge graphs such as Wikidata or DBpedia is extremely valuable. However, these queries can be interrupted due to the fair use policies enforced by SPARQL endpoint providers, leading to incomplete results. While these policies help maintain the responsiveness of public SPARQL endpoints, they compromise the completeness of query results, which limits the feasibility of various downstream tasks. Ideally, we should not have to choose between completeness and responsiveness. To address this issue, we introduce and formalize the concept of SPARQL continuation queries. When a SPARQL endpoint interrupts a query, it returns partial results along with a SPARQL continuation query to retrieve the remaining results. If the continuation query is also interrupted, the process repeats, generating further continuation queries until the complete results are obtained. In our experimentation, we show that our continuation server PASSAGE ensures completeness and responsiveness while delivering high performance.

The Linking Open Data(LOD) cloud is a collection of linked Resource Description Framework (RDF) data with over 26 billion RDF triples. Consuming linked data is a challenging task because each data set in the LOD cloud has specific ontology schema, and familiarity with ontology schema is required in order to query various linked data sets. However, manually checking each data set is time-consuming, especially when many data sets from various domains are used. This difficulty can be overcome without user interaction by using an automatic method that integrates different ontology schema. In this paper, we propose a Mid-Ontology learning approach that can automatically construct a simple ontology, linking related ontology predicates (class or property) in different data sets. Our Mid-Ontology learning approach consists of three main phases: data collection, predicate grouping, and Mid-Ontology construction. Experimental results show that our Mid-Ontology learning approach successfully integrates diverse ontology schema, and effectively retrieves related information.

Querying the ever-growing Web of Data poses a significant challenge in today’s Semantic Web. The complete lack of any centralised control leads to potentially arbitrary data distribution, high variability of latency between hosts participating in query answering, and, in the extreme, even the (sudden) unavailability of some hosts during query execution. In this thesis we address the question of how to efficiently query the Web of Data while taking into account its scale, diversity and unreliable and uncontrollable nature. We begin by first introducing Avalanche, a federated SPARQL engine which: 1) makes no assumptions about RDF data distribution to SPARQL endpoints, 2) is adaptive to changing network conditions, i.e, can adapt to slow network connections or endpoint unavailability, 3) retrieves up-to-date results from SPARQL endpoints, and 4) is flexible by making limiting assumptions about the structure of participating triple stores. Tailored to address the semantic heterogeneity derived from the Web of Data’s rich and broad semantic diversity, coupled with its characteristic lack of guarantees, Avalanche employs a fragmented query planning approach, under a concurrent and parallel execution model. By fragmented execution, we refer to the fact that the original SPARQL query is rewritten as the union of all fragments which comprise it. A query fragment is defined as the conjunction of all query triple patterns, where a triple pattern can be resolved by only one endpoint. As the Web of Data continues to grow, we postulate that so is the likelihood that large numbers of endpoints will index data, sharing the same vocabularies, thus forming semantically homogenous partitions of the Semantic Web. Focusing on this scenario and in order to address some of Avalanche’s limitations, we introduce x-Avalanche an extension of our original system. Here, we add support for disjunctions by using a distributed union operator capable of scaling to hundreds or thousands of endpoints. Furthermore, we enhance the distributed state management with: a) remote caches aimed to reduce the high latency typical of SPARQL endpoints, b) multicast parallel bind-joins exploiting the SPARQL 1.1 VALUES clause, and c) proxy based execution of x-Avalanche operators. Finally, in x-Avalanche, we introduce a novel and parallel-friendly optimisation paradigm designed not only to offer an optimal tradeoff between total query execution time and fast first results, but also to consider an extended planning space unexplored so far, thus taking the fragmented execution model first introduced in Avalanche to its logical conclusion. Combined, x-Avalanche’s enhancements and optimisations can lead to dramatic performance improvements over top performing state of the art federated SPARQL engines. To conclude, our results show that on average x-Avalanche can be more than one order of magnitude faster when executing SPARQL queries.

Getting complete results when processing aggregate queries on public SPARQL endpoints is challenging, mainly due to the application of quotas. Although Web preemption supports processing of aggregate queries online, on preemptable SPARQL servers, data transfer is still very large when processing count-distinct aggregate queries. In this paper, it is shown that count-distinct aggregate queries can be approximated with low data transfer by extending the partial aggregation operator with HyperLogLog++ sketches. Experimental results demonstrate that the proposed approach outperforms existing approaches by orders of magnitude in terms of the amount of data transferred.

We present LSQ: a Linked Dataset describing SPARQL queries extracted from the logs of public SPARQL endpoints. We argue that LSQ has a variety of uses for the SPARQL research community, be it for example to generate custom benchmarks or conduct analyses of SPARQL adoption. We introduce the LSQ data model used to describe SPARQL query executions as RDF. We then provide details on the four SPARQL endpoint logs that we have RDFised thus far. The resulting dataset contains 73 million triples describing 5.7 million query executions.

Publication and Usage of Official Czech Pension Statistics Linked Open Data

This paper is concerned with the problems of understanding the relations in automatically extracted semantic datasets such as DBpedia and utilizing them in semantic queries such as SPARQL. Although...

Linked Data (LD) is supplementing the World Wide Web of documents with a Web of data. This is becoming apparent from the number of LD repositories available as part of the Linked Open Data (LOD) cloud. At the instance-level, LD sources use a combination of terms from various vocabularies, expressed as RDFS/OWL, to describe their data and publish them to the Web. However, LD sources do not organise their data under a specific structure analogous to a relational schema; instead data can adhere to multiple vocabularies. Expressing SPARQL queries over LD sources -- usually over a SPARQL endpoint that is presented to the user -- requires a knowledge of the predicates used, to allow queries to express user requirements as graph patterns. Although LD provides low barriers to data publication using a homogeneous language (i.e., RDF), sources organise their data with different structures and terminologies. We would like to have a synopsis of how such data are organised in LD sources to inform the expressing of queries over such sources. With this paper we make the case that structural summaries over LD sources can inform query formulation and provide support for data integration and query processing over multiple LD sources. To fulfil this aim we propose an approach, that builds on a hierarchical clustering algorithm, for inferring structural summaries over LD sources. We have conducted an experimental evaluation using various LD sources to ascertain the extent to which our technique can successfully infer structural summaries from LD sources.

Since most bio-medical Linked Data Sets are simply extracted from the Relational database, lots of them are lack of ontology or concept hierarchy structure for user better understanding the data sets. This problem also limited usage of bio-medical Linked Data Sets. To resolve the problem, this paper introduced a method to dynamically generate the concept hierarchy from the Linked Data Sets. Based on the hierarchical clustering algorithm, we applied Vector Space Model(VSM) and Jaccard's Coefficient(JC) to formalize the hierarchy structure after pre-processing data. We implemented our method using two Linked Data Sets: DrugBank and Diseasome from Linked Life Data and evaluated performance with the gold standard.