Incremental View Maintenance for SPARQL Queries: Adapting the Counting Algorithm

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.

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.

PG-explorer: Resource Description Framework data exploration with property graphs

In the realm of Digital Humanities, the management of cultural heritage metadata is pivotal for ensuring data trustworthiness. Provenance information—contextual metadata detailing the origin and history of data—plays a crucial role in this process. However, tracking provenance and changes in metadata using the Resource Description Framework (RDF) presents significant challenges due to the limitations of foundational Semantic Web technologies. This article offers a comprehensive review of existing models and approaches for representing provenance and tracking changes in RDF, with a specific focus on cultural heritage metadata. It examines W3C standard proposals such as RDF Reification and n-ary relations, along with various alternative systems. Through an in-depth analysis, the study identifies Named Graphs, RDF-star, the Provenance Ontology (PROV-O), Dublin Core (DC), Conjectural Graphs, and the OpenCitations Data Model (OCDM) as the most effective solutions. These models are evaluated based on their compliance with RDF standards, scalability, and applicability across different domains. The findings underscore the importance of selecting the appropriate model to ensure robust and reliable management of provenance in RDF datasets, thereby contributing to the ongoing discourse on provenance representation in the Digital Humanities.

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.

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.

Partial evaluation has recently been used for processing SPARQL queries over a large resource description framework (RDF) graph in a distributed environment. However, the previous approach is inefficient when dealing with complex queries. In this study, we further improve the "partial evaluation and assembly" framework for answering SPARQL queries over a distributed RDF graph, while providing performance guarantees. Our key idea is to explore the intrinsic structural characteristics of partial matches to filter out irrelevant partial results while providing performance guarantees on the data shipment and the response time. We also propose an efficient assembly algorithm to utilize the characteristics of partial matches to merge them and form final results. To improve the efficiency of finding partial matches further, we propose an optimization that communicates variables' candidates among sites to avoid redundant computations. In addition, although our approach is partitioning-tolerant, different partitioning strategies result in different performances, and we evaluate different partitioning strategies for our approach. Experiments over both real and synthetic RDF datasets confirm the superiority of our approach.

"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."

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.

With the development of information technology, the resource description framework (RDF) occupies very important position in structure of the semantic web. An effective way to enhance the efficiency of the RDF data query is RDF closures. Our work is improve the RDF reasoning in MapReduce framework. The algorithm is based on packet classification efficiency of a few of candidate rules, as well as the NoSQL data storage avoiding repetition, corresponding to the RDF properties and concepts. The optimization algorithm of MapReduce is given a new closure generation process. Our approach use RDFS inference rules and the RDF triples in relationship between Map and Reduce operations and generate the RDF closures, improve the generate efficiency of closure. Based on RDF MapReduce algorithm for RDF closure, look forward more suitable RDF closure computing scheme on cloud computing environment.

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.

The Resource Description Framework (RDF) is a popular data model for representing linked data sets arising from the web, as well as large scientific data repositories such as UniProt. RDF data intrinsically represents a labeled and directed multi-graph. SPARQL is a query language for RDF that expresses subgraph pattern-finding queries on this implicit multigraph in a SQL-like syntax. SPARQL queries generate complex intermediate join queries; to compute these joins efficiently, this paper presents a new strategy based on bitmap indexes. We store the RDF data in column-oriented compressed bitmap structures, along with two dictionaries. We find that our bitmap index-based query evaluation approach is up to an order of magnitude faster the state-of-the-art system RDF-3X, for a variety of SPARQL queries on gigascale RDF data sets.

The Resource Description Framework (RDF) and SPARQL query language are gaining wide popularity and acceptance. In this paper, we present DREAM, a distributed and adaptive RDF system. As opposed to existing RDF systems, DREAM avoids partitioning RDF datasets and partitions only SPARQL queries. By not partitioning datasets, DREAM offers a general paradigm for different types of pattern matching queries, and entirely averts intermediate data shuffling (only auxiliary data are shuffled). Besides, by partitioning queries, DREAM presents an adaptive scheme, which automatically runs queries on various numbers of machines depending on their complexities. Hence, in essence DREAM combines the advantages of the state-of-the-art centralized and distributed RDF systems, whereby data communication is avoided and cluster resources are aggregated. Likewise, it precludes their disadvantages, wherein system resources are limited and communication overhead is typically hindering. DREAM achieves all its goals via employing a novel graph-based, rule-oriented query planner and a new cost model. We implemented DREAM and conducted comprehensive experiments on a private cluster and on the Amazon EC2 platform. Results show that DREAM can significantly outperform three related popular RDF systems.

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.

k-Nearest Keyword Search in RDF Graphs