Accelerating Partial Evaluation in Distributed SPARQL Query Evaluation

In the Semantic Web, items are represented by uniform resource identifiers (URIs) on the Web in order to describe relationships between things in resource description framework (RDF) graphs. Large scale open RDF data (linked data) describing information about various things are available. Hence, relational discovery research has been conducted using RDF data to explore relationships between things. The SPARQL protocol and RDF query language (SPARQL) is usually employed to process RDF data; however, SPARQL is not suitable for relational discovery using linked data because it is difficult to describe unknown vocabularies and relationships in SPARQL queries. In this paper, we propose an aggregation path search for RDF graphs as an extension of shortest path keyword search. The aggregation path search is devised to find RDF paths by merging multiple resource URIs into commonly reachable nodes in an RDF graph. This search method can discover new relationships between items that are connected by a common node. We present a method for treating equivalent resource URIs as a single resource URI; these equivalent URIs essentially represent the same thing based on equivalence relations in different RDF datasets. In our experiments, we show in the integration of multiple large scale RDF datasets that the integration of equivalent resources enables richer retrieval of aggregated paths.

k-Nearest Keyword Search in RDF Graphs

Knowledge graphs have become a popular formalism for representing entities and their properties using a graph data model, e.g., the Resource Description Framework (RDF). An RDF graph comprises entities of the same type connected to objects or other entities using labeled edges annotated with properties. RDF graphs usually contain entities that share the same objects in a certain group of properties, i.e., they match star patterns composed of these properties and objects. In case the number of these entities or properties in these star patterns is large, the size of the RDF graph and query processing are negatively impacted; we refer these star patterns as frequent star patterns. We address the problem of identifying frequent star patterns in RDF graphs and devise the concept of factorized RDF graphs, which denote compact representations of RDF graphs where the number of frequent star patterns is minimized. We also develop computational methods to identify frequent star patterns and generate a factorized RDF graph, where compact RDF molecules replace frequent star patterns. A compact RDF molecule of a frequent star pattern denotes an RDF subgraph that instantiates the corresponding star pattern. Instead of having all the entities matching the original frequent star pattern, a surrogate entity is added and related to the properties of the frequent star pattern; it is linked to the entities that originally match the frequent star pattern. Since the edges between the entities and the objects in the frequent star pattern are replaced by edges between these entities and the surrogate entity of the compact RDF molecule, the size of the RDF graph is reduced. We evaluate the performance of our factorization techniques on several RDF graph benchmarks and compare with a baseline built on top gSpan, a state-of-the-art algorithm to detect frequent patterns. The outcomes evidence the efficiency of proposed approach and show that our techniques are able to reduce execution time of the baseline approach in at least three orders of magnitude. Additionally, RDF graph size can be reduced by up to 66.56% while data represented in the original RDF graph is preserved.

Answering top-K query combined keywords and structural queries on RDF graphs

The ontology based framework is developed for representing image domain. The textual features of images are extracted and annotated as the part of the ontology. The ontology is represented in Web Ontology Language (OWL) format which is based on Resource Description Framework (RDF) and Resource Description Framework Schema (RDFS). Internally, the RDF statements represent an RDF graph which provides the way to represent the image data in a semantic manner. Various tools and languages are used to retrieve the semantically relevant textual data from ontology model. The SPARQL query language is more popular methods to retrieve the textual data stored in the ontology. The text or keyword based search is not adequate for retrieving images. The end users are not able to convey the visual features of an image in SPARQL query form. Moreover, the SPARQL query provides more accurate results by traversing through RDF graph. The relevant images cannot be retrieved by one to one mapping. So the relevancy can be provided by some kind of onto mapping. The relevancy is achieved by applying a decision tree algorithm. This study proposes methods to retrieve the images from ontology and compare the image retrieval performance by using SPARQL query language, decision tree algorithm and Lire which is an open source image search engine. The SPARQL query language is used to retrieving the semantically relevant images using keyword based annotation and the decision tree algorithms are used in retrieving the relevant images using visual features of an image. Lastly, the image retrieval efficiency is compared and graph is plotted to indicate the efficiency of the system.

Fixing the inconsistencies in fuzzy spatiotemporal RDF graph

The RDF (Resource Description Framework) datamodel has been used in various domains, such as Web,government, biology etc. Now, the volume of RDF datasets is growing significantly. The explosion on the volume of RDF data raises serious challenges: how to answer SPARQL queries on large RDF data sets efficiently. Here, we present a large-scale RDF data system - TripleParallel, which implements blockbased parallel processing SPARQL queries on RDF data sets with billion triples. The system improves parallelism while strengthening the overlapping data and calculations and reduces the overall execution time of the query. TripleParallel also implements multiple parallel operations for parallel processing joins. Experimental studies with several RDF datasets, including the LUBM and the UniProt collection, demonstrate the performance gains of our approach, outperforming the previous fastest system by more than an order of magnitude.

Publish/subscribe is a promising paradigm with a growing appeal in building large-scale distributed applications. Existing publish/subscribe systems have limited support for semantic information. In this paper, we propose JTangPS (JTang Publish/subscribe System), an RDF (Resource Description Framework) based publish/subscribe system which uses RDF graphs to describe events and RDF graph patterns to represent subscriptions. A fast graph-based matching algorithm is presented to reconcile the conflict of expressiveness and scalability. Experimental results using synthetic workload demonstrate the scalability of system with large numbers of subscriptions.

The increasing use of Semantic Web technologies in the life sciences, in particular the use of the Resource Description Framework (RDF) and the RDF query language SPARQL, opens the path for novel integrative analyses, combining information from multiple data sources. However, analyzing evolutionary data in RDF is not trivial, due to the steep learning curve required to understand both the data models adopted by different RDF data sources, as well as the equivalent SPARQLconstructs required to benefit from this data - in particular, recursive property paths. In this article, we provide a hands-on introduction to querying evolutionary data across several data sources that publish orthology information in RDF, namely: The Orthologous MAtrix (OMA), the European Bioinformatics Institute (EBI) RDF platform, the Database of Orthologous Groups (OrthoDB) and the Microbial Genome Database (MBGD). We present four protocols in increasing order of complexity. In these protocols, we demonstrate through SPARQL queries how to retrieve pairwise orthologs, homologous groups, and hierarchical orthologous groups. Finally, we show how orthology information in different data sources can be compared, through the use of federated SPARQL queries.

The continuing development of Semantic Web technologies and the increasing user adoption in the recent years have accelerated the progress incorporating explicit semantics with data on the Web. With the rapidly growing RDF (Resource Description Framework) data on the Semantic Web, processing large semantic graph data have become more challenging. Constructing a summary graph structure from the raw RDF can help obtain semantic type relations and reduce the computational complexity for graph processing purposes. In this paper, we addressed the problem of graph summarization in RDF graphs, and we proposed an approach for building summary graph structures automatically from RDF graph data. Moreover, we introduced a measure to help discover optimum class dissimilarity thresholds and an effective method to discover the type classes automatically. In future work, we plan to investigate further improvement options on the scalability of the proposed method.

"The purpose of the present paper is to research a rule-based approach for transforming X3D (eXtensible 3D) models to RDF (Resource Description Framework). The transformation is performed by using the RDF Mapping Language (RML). Its advantages are summarized, which are mainly due to the fact that the rules created build a knowledge base. By applying SPARQL (SPARQL Protocol and RDF Query Language) queries to it, the possibility of explore in order to validate and improve the defined RML rules themselves, is pointed out. An approach for reversing from the RDF triples to the original X3D in a unique way is considered, and the types of SPARQL queries needed for its implementation are systematized. Rules are formulated for all elements defined in the X3D schema, their attributes and properties are described. Their accessibility is ensured. The conversion of X3D models to RDF is confirmed to be consistent with guidelines and best practices for creating accessible, understandable, and reusable ontologies on the Web. The systematized SPARQL query types for reversing from RDF triples to the original X3D are checked for specific elements and sample data, and the obtained results establish their correctness. The prerequisites and limitations of the represented approach are explained. The proposed approach allows building a comprehensive knowledge base that includes the RML rules, the transformed X3D models and the domain-specific ontology and its use to analyzing data and semantic reasoning. The electronic libraries that include 3D content could take advantage from the benefits and possible future applications of the solutions discussed in this study."

This chapter accompanies the lecture on SPARQL with entailment regimes at the 7th Reasoning Web Summer School in Galway, Ireland, 2011. SPARQL is a query language and protocol for data specified in the Resource Description Format (RDF). The basic evaluation mechanism for SPARQL queries is based on subgraph matching. The query criteria are given in the form of RDF triples possibly with variables in place of the subject, object, or predicate of a triple, called basic graph patterns. Each instantiation of the variables that yields a subgraph of the queried RDF graph constitutes a solution. The query language further contains capabilities for querying for optional basic graph patterns, alternative graph patterns etc. We first introduce the main features of SPARQL as a query language. In order to define the semantics of a query, we show how a query can be translated to an abstract query, which can then be evaluated according to SPARQL's query evaluation mechanism. Apart from the features of SPARQL 1.0, we also briefly introduce the new features of SPARQL 1.1, which is currently being developed by the Data Access Working Group of the World Wide Web Consortium. In the second part of these notes, we introduce SPARQL's extension point for basic graph pattern matching. We illustrate how this extension point can be used to define a semantics for basic graph pattern evaluation based on more elaborate semantics such as RDF Schema (RDFS) entailment or OWL entailment. This allows for solutions to a query that implicitly follow from an RDF graph, but which are not necessarily explicitly present. We illustrate what constitutes an extension point and how problems that arise from using a semantic entailment relation can be addressed. We first introduce SPARQL in combination with the RDFS entailment relation and then move on to the more expressive Web Ontology Language OWL. We cover OWL's Direct Semantics, which is based on Description Logics, and the RDF-Based Semantics, which is an extension of the RDFS semantics. For the RDF-Based Semantics we mainly focus on the OWL 2 RL profile, which allows for an efficient implementation using rule engines. We assume that readers have a basic knowledge of RDF and Turtle, which we use in examples. For the OWL parts, we assume some background in OWL or Description Logics (see lecture notes Foundations of Description Logics). The examples for the OWL part are given in Turtle, OWL's functional-style syntax and Description Logics syntax. Although the inferences that are relevant for the example queries are explained, a basic idea about OWL's modeling constructs and their semantics are certainly helpful.

BackgroundKnowledge graphs (KGs) play a key role to enable explainable artificial intelligence (AI) applications in healthcare. Constructing clinical knowledge graphs (CKGs) against heterogeneous electronic health records (EHRs) has been desired by the research and healthcare AI communities. From the standardization perspective, community-based standards such as the Fast Healthcare Interoperability Resources (FHIR) and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) are increasingly used to represent and standardize EHR data for clinical data analytics, however, the potential of such a standard on building CKG has not been well investigated. ObjectiveTo develop and evaluate methods and tools that expose the OMOP CDM-based clinical data repositories into virtual clinical KGs that are compliant with FHIR Resource Description Framework (RDF) specification. MethodsWe developed a system called FHIR-Ontop-OMOP to generate virtual clinical KGs from the OMOP relational databases. We leveraged an OMOP CDM-based Medical Information Mart for Intensive Care (MIMIC-III) data repository to evaluate the FHIR-Ontop-OMOP system in terms of the faithfulness of data transformation and the conformance of the generated CKGs to the FHIR RDF specification. ResultsA beta version of the system has been released. A total of more than 100 data element mappings from 11 OMOP CDM clinical data, health system and vocabulary tables were implemented in the system, covering 11 FHIR resources. The generated virtual CKG from MIMIC-III contains 46,520 instances of FHIR Patient, 716,595 instances of Condition, 1,063,525 instances of Procedure, 24,934,751 instances of MedicationStatement, 365,181,104 instances of Observations, and 4,779,672 instances of CodeableConcept. Patient counts identified by five pairs of SQL (over the MIMIC database) and SPARQL (over the virtual CKG) queries were identical, ensuring the faithfulness of the data transformation. Generated CKG in RDF triples for 100 patients were fully conformant with the FHIR RDF specification. ConclusionThe FHIR-Ontop-OMOP system can expose OMOP database as a FHIR-compliant RDF graph. It provides a meaningful use case demonstrating the potentials that can be enabled by the interoperability between FHIR and OMOP CDM. Generated clinical KGs in FHIR RDF provide a semantic foundation to enable explainable AI applications in healthcare.

Resource Description Framework (RDF) is increasingly being used for representing information on the web. This popularity has made storage of large RDF data a difficult task. To overcome these issues many distributed RDF systems are being proposed that can store and efficiently process Big RDF data. Hadoop framework is widely being used for storing and handling a large amount of RDF data. One of the major obstacles faced while handling this large amount of RDF data is query processing on such large datasets. In this paper, we present JOTR: a SPARQL query optimization technique for Big RDF data using triple pattern reordering on a distributed Hadoop based RDF system. The proposed technique is based on selectivity calculation and has been tested on one of the popular RDF benchmark datasets, LUBM dataset. We have tested JOTR on large sized RDF datasets and compared it with other optimization approaches in respect to the query execution time. From the results, it can be concluded that our approach gives a notable performance on distributed RDF systems and thus is applicable to centralized systems as well.

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.