Resource Description Framework Datasets Research Articles

Linked data to streaming data sensors sources for direct access

Goal: Industrial operations are complex, and data sensors assure safety and reliable information for production improvements. Multiple stakeholders can take advantage of data acquisition for post-analysis and process control. Providing users and systems with friendly access to operating data is fundamental to the digital transition in the industry 4.0 scenario. Linking data and systems over ontologies and Industry Foundation Classes will boost supply chain performance in many layers. This paper presents the concept of valid data points over Uniform Resource Identifiers for sensor time-series into triples stores via Application Programming Interfaces. Design/Methodology/Approach: A streaming data source approach to integrating industrial sensor data and sharing it via Uniform Resource Identifiers is developed and tested using Node-Red with multiple data connection types, such as the Industry Foundation Classes and open-source time series databases. Results: The detailed proof of concept presented valid the feasibility of sharing sensor data via Uniform Resource Identifiers. The findings provide a backbone of a system able to interop Message Queuing Telemetry Transport data, Resource Description Framework datasets and Industry Foundation Classes schema. Limitations of the investigation: The system envisaged was tested using simulated data. However, it is expected to have similar results from real data use. Nevertheless, more research will be needed to implement more features, such as three-dimensional object integration. Practical implications: The solution designed and tested presented can be used in practice for companies that desire to expand via linked data shareability and interoperability. Also, researchers can advance the solution for specific features, such as creating an open-source data query and manipulation language. Originality/Value: This paper examines future deployments of systems-to-systems interoperability targeting user-friendly data shareability. It is meant to be useful for industrial and academic developments.

An efficient and scalable SPARQL query processing framework for big data using MapReduce and hybrid optimum load balancing

The increasing RDF (Resource Description Framework) data volume requires a Hadoop platform for processing queries over large datasets. In this work, SPARQL (Simple Protocol and Rdf Query Language) queries are evaluated with Hadoop based on the objective of minimizing the number of joins through data partitioning for performing map/reduce jobs. The query evaluation time and the number of cross node joins are minimized with the proposed partitioning techniques. Extended vertical partitioning is proposed for distributed data stores based on objects’ explicit information for splitting predicates. For accessing the RDF data, hybrid monarch butterfly with beetle swarm load balancing optimization with Map-reduce (Hybrid Optimum Load Balancing) is applied. The proposed SPARQL query processing is evaluated over large RDF datasets. The proposed approach’s evaluation results are analyzed with the existing approaches, indicating the proposed framework’s efficiency. By using the proposed approach, an accuracy of 97 % is obtained.

Graph pattern detection and structural redundancy reduction to compress named graphs

The flexible paradigm of Resource Description Framework (RDF) has accelerated the raw data published on the web. Therefore, the volume of generated RDF data has increased impressively in the last decade promoting compression to manage and reduce the size of RDF datasets. Universal RDF compressors can be able to detect and remove redundancy at symbolic, syntactic, or semantic levels. However, these compressors rarely exploit the graph patterns as well as structural regularities in real-world datasets. An efficient approach for compressing the RDF datasets based on the structural properties is HDT (Header-Dictionary-Triple). However, it cannot manage the RDF datasets with named graphs, the regularities of the graph structure, and structural redundancies. Because HDT considers all the triples to reside in the same default graph. Though, the triples of an RDF dataset belong to various named graphs. In this study, we have proposed a novel approach to deal with the above-mentioned challenges. We introduce hybrid TI-GI (Triple Interpreter-Graph Interpreter) to manage the RDF datasets with named graphs and use compact RDF serialization. We also propose RDF-RR (RDF-Redundancy Reducer) and object mapping that detects and removes structural redundancies by identifying the common patterns related to the predicates and objects in the RDF datasets. We employ a differential compressor to discover the frequent graph pattern in a single pass by using the data structure-oriented approach of the dataset. Evaluation of real-world datasets affirms that our proposed approach can substantially reduce the size of the experimental RDF datasets at approximately 30.52%, 24.92%, and 26.96% when compared with the existing HDT, HDT-FoQ (HDT-Focused on Querying) and the 2Tp (two Triple Predicate based index) approaches. Moreover, the indexing time of our proposed system is also reduced at approximately 17.89%, 13.70%, and 9.32% when compared with the HDT, HDT-FoQ, and 2Tp approaches.

Optimizing Keyword Search Over Federated RDF Systems

The issue of keyword search in federated resource description framework(RDF) systems is revisited in this paper. Numerous independent SPARQL endpoints that solely offer SPARQL query interfaces make up a federated RDF system. Others find it challenging to remotely build up the global inverted indices over the full RDF network by downloading the dataset at various SPARQL endpoints. This poses a challenge for existing keyword search approaches. In this paper, we propose a keyword search approach for federated RDF systems without building up global inverted indices on the entire RDF graph. We build an offline schema graph for the federated RDF system. During query evaluation, the full-text search interfaces provided by existing SPARQL endpoints were used to map keywords to vertices in the schema graph. Subsequently, the schema graph was explored to construct SPARQL queries from the keywords. The constructed queries were evaluated over the underlying federated RDF systems. Some cost models were proposed to measure keyword mapping and query construction. On the other hand, to further improve the efficiency, a multiple query optimization strategy was designed to rewrite the queries and share the common computation during evaluation. Extensive experiments on real RDF datasets show that the proposed techniques are effective.

A Cost-Driven Top-K Queries Optimization Approach on Federated RDF Systems

RDF (Resource Description Framework) is a model widely used to construct knowledge bases, while SPARQL (SPARQL Protocol and RDF Query Language) is the standardized structured query language to manipulate RDF data. Recently, many data providers have published their RDF datasets in their own autonomous sites and provided SPARQL query interfaces, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RDF sources</i> . In order to integrate multiple RDF sources, researchers put forward the federated RDF system to support the federated SPARQL queries. However, existing studies can only support efficient basic queries but not top-k queries. Toward this end, we propose a cost-driven top-k queries optimization approach in federated RDF systems, which can support both top-k queries for single variable ordering and expression ordering. Firstly, we propose an optimized query decomposition method to decompose the federated query into multiple subqueries. Secondly, while considering the top-k operator, we propose a cost model to evaluate the query cost and join cost of subqueries. The optimal query plan can be obtained by the costed-based query plan generation algorithm. Finally, combined with the characteristics of top-k queries, an incremental query plan execution strategy is developed to minimize the total query cost. Experimental results show that the proposed method is effective, efficient and scalable.

Two-stage Detection of Semantic Redundancies in RDF Data

With the enrichment of the RDF (resource description framework), integrating diverse data sources may result in RDF data duplication. Failure to effectively detect the duplicates brings redundancies into the integrated RDF datasets. This not only increases unnecessarily the size of the datasets, but also reduces the dataset quality. Traditionally a similarity calculation is applied to detect if a pair of candidates contains duplicates. For massive RDF data, a simple similarity calculation may lead to extremely low efficiency. To detect duplicates in the massive RDF data, in this paper we propose a detection approach based on RDF data clustering and similarity measurements. We first propose a clustering method based on locality sensitive hashing (LSH), which can efficiently select candidate pairs in RDF data. Then, a similarity calculation is performed on the selected candidate pairs. We finally obtain the duplicate candidates. We show through experiments that our approach can quickly extract the duplicate candidates in RDF datasets. Our approach had the highest F score and time performance in the OAEI (Ontology Alignment Evaluation Initiative) 2019 competition.

JQPro:Join Query Processing in a Distributed System for Big RDF Data Using the Hash-Merge Join Technique

In the last decade, the volume of semantic data has increased exponentially, with the number of Resource Description Framework (RDF) datasets exceeding trillions of triples in RDF repositories. Hence, the size of RDF datasets continues to grow. However, with the increasing number of RDF triples, complex multiple RDF queries are becoming a significant demand. Sometimes, such complex queries produce many common sub-expressions in a single query or over multiple queries running as a batch. In addition, it is also difficult to minimize the number of RDF queries and processing time for a large amount of related data in a typical distributed environment encounter. To address this complication, we introduce a join query processing model for big RDF data, called JQPro. By adopting a MapReduce framework in JQPro, we developed three new algorithms, which are hash-join, sort-merge, and enhanced MapReduce-join for join query processing of RDF data. Based on an experiment conducted, the result showed that the JQPro model outperformed the two popular algorithms, gStore and RDF-3X, with respect to the average execution time. Furthermore, the JQPro model was also tested against RDF-3X, RDFox, and PARJs using the LUBM benchmark. The result showed that the JQPro model had better performance in comparison with the other models. In conclusion, the findings showed that JQPro achieved improved performance with 87.77% in terms of execution time. Hence, in comparison with the selected models, JQPro performs better.

Aggregation Path Search using Multiple Large RDF Datasets with Equivalence Relations

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.

Optimisation Techniques for Flexible SPARQL Queries

Resource Description Framework datasets can be queried using the SPARQL language but are often irregularly structured and incomplete, which may make precise query formulation hard for users. The SPARQLARlanguage extends SPARQL 1.1 with two operators—APPROX and RELAX—to allow flexible querying over property paths. These operators encapsulate different dimensions of query flexibility, namely, approximation and generalisation, and they allow users to query complex, heterogeneous knowledge graphs without needing to know precisely how the data is structured. Earlier work has described the syntax, semantics, and complexity of SPARQLAR, has demonstrated its practical feasibility, but has also highlighted the need for improving the speed of query evaluation. In the present article, we focus on the design of two optimisation techniques targeted at speeding up the execution of SPARQLARqueries and on their empirical evaluation on three knowledge graphs: LUBM, DBpedia, and YAGO. We show that applying these optimisations can result in substantial improvements in the execution times of longer-running queries (sometimes by one or more orders of magnitude) without incurring significant performance penalties for fast queries.

Space/time-efficient RDF stores based on circular suffix sorting

The resource description framework (RDF) has gained popularity as a format for the standardized publication and exchange of information in the Web of Data. In this paper, we introduce RDFCSA, a compressed representation of RDF datasets that in addition supports efficient querying. RDFCSA regards the triples of the RDF store as short circular strings and applies suffix sorting on those strings, so that triple-pattern queries reduce to prefix searching on the string set. The RDF store is then represented compactly using a compressed suffix array (CSA), a proved technology in text indexing that efficiently supports prefix searches. Our experiments show that RDFCSA is competitive with state-of-the-art alternatives. It compresses the raw data to 60% of its size, close to the most compact alternatives. While most alternatives perform better in some kinds of triple-patterns than in others, RDFCSA features fast and consistent query times, a few microseconds per result in all cases. This enables efficiently supporting join queries by using either merge- or chaining-join strategies over the triple patterns coupled with some specific optimizations such as variable filling. Our experiments on binary joins show that RDFCSA is faster than the alternatives in most cases.

Learned Semantic Index Structure Using Knowledge Graph Embedding and Density-Based Spatial Clustering Techniques

Recently, a pragmatic approach toward achieving semantic search has made significant progress with knowledge graph embedding (KGE). Although many standards, methods, and technologies are applicable to the linked open data (LOD) cloud, there are still several ongoing problems in this area. As LOD are modeled as resource description framework (RDF) graphs, we cannot directly adopt existing solutions from database management or information retrieval systems. This study addresses the issue of efficient LOD annotation organization, retrieval, and evaluation. We propose a hybrid strategy between the index and distributed approaches based on KGE to increase join query performance. Using a learned semantic index structure for semantic search, we can efficiently discover interlinked data distributed across multiple resources. Because this approach rapidly prunes numerous false hits, the performance of join query processing is remarkably improved. The performance of the proposed index structure is compared with some existing methods on real RDF datasets. As a result, the proposed indexing method outperforms existing methods due to its ability to prune a lot of unnecessary data scanned during semantic searching.

Skyline Queries in SPARQL: An Overview

The growth of RDF (Resource Description Framework) datasets and the expansion of their use in conjunction with the definition of SPARQL, a declarative query language, have made RDF data management an active area of research and development. In this regard,mechanisms have been proposed to help users find their desired answers in less time, including ranking methods and preference-based queries. Skyline queries constitute one of the most practical and predominant types of preference-based queries. The aim ofthis work was to provide a guide to specifying SPARQL skyline queries using syntax proposed in state-of-the-art works, and SPARQL versions 1.0 and 1.1. The results show the possibility of rewriting skyline queries in SPARQL to express preferences. We planto develop a tool to translate SPARQL skyline queries applying the different grammars proposed, into SPARQL 1.0 and 1.1 with the aim of providing an automatic mechanism of translation.

GRDF: An Efficient Compressor with Reduced Structural Regularities That Utilizes gRePair.

The explosive volume of semantic data published in the Resource Description Framework (RDF) data model demands efficient management and compression with better compression ratio and runtime. Although extensive work has been carried out for compressing the RDF datasets, they do not perform well in all dimensions. However, these compressors rarely exploit the graph patterns and structural regularities of real-world datasets. Moreover, there are a variety of existing approaches that reduce the size of a graph by using a grammar-based graph compression algorithm. In this study, we introduce a novel approach named gRDF (graph repair for RDF) that uses gRePair, one of the most efficient grammar-based graph compression schemes, to compress the RDF dataset. In addition to that, we have improved the performance of HDT (header-dictionary-triple), an efficient approach for compressing the RDF datasets based on structural properties, by introducing modified HDT (M-HDT). It can detect the frequent graph pattern by employing the data-structure-oriented approach in a single pass from the dataset. In our proposed system, we use M-HDT for indexing the nodes and edge labels. Then, we employ gRePair algorithm for identifying the grammar from the RDF graph. Afterward, the system improves the performance of -trees by introducing a more efficient algorithm to create the trees and serialize the RDF datasets. Our experiments affirm that the proposed gRDF scheme can substantially achieve at approximately 26.12%, 13.68%, 6.81%, 2.38%, and 12.76% better compression ratio when compared with the most prominent state-of-the-art schemes such as HDT, HDT++, -trees, RDF-TR, and gRePair in the case of real-world datasets. Moreover, the processing efficiency of our proposed scheme also outperforms others.

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

The Resource Description Framework (RDF) has been widely applied to represent and exchange domain information because of its machine-readable characteristic. With a huge amount of RDF data available, retrieving RDF data is essential, so that many RDF query approaches have been developed. But many traditional approaches generally require users to know RDF model and query language, and this seriously prevents a large number of common non-expert users from obtaining information in RDF datasets. In this paper, we propose an approach that users can explore massive RDF datasets by interactively manipulating property graphs. Our approach provides users with a series of operations in interactively constructing property graphs to describe their query intents. To efficiently explore massive RDF datasets, we convert RDF data into property graphs for storage. This can greatly reduce the size of massive RDF datasets and more importantly the converted property graphs can instruct users to understand the underlying structure of RDF datasets, which is very useful in users’ construction of their query property graphs. The constructed query property graphs are finally transformed into expressions with the query language of graph databases. With high-performance graph databases as query engines, we developed an RDF data exploration system – PG-Explorer. Through experiments over real-world datasets, we evaluated the effectiveness and superiority of our approach. The user study demonstrates that the proposed approach simplifies users’ exploration of RDF data and can satisfy their exploration needs.

A classification using RDFLIB and SPARQL on RDF dataset

Web Semantics is one of the prominent domain in the current era of web. This web comprises of linked open data, which is an interconnection of related data over web and the base of data representation is Resource Description Framework (RDF). Applying machine learning algorithms will enhance the capabilities of semantic web over the concepts to visualize and analyze data over web. It will integrate the capabilities of web semantics and machine learning. In this paper, classification on a sample RDF dataset has been implemented and their results have been analyzed. This will give researcher a baseline to work with machine learning algorithms on semantic web data. The machine learning algorithms like decision tree classifier, random forest classifier along with artificial neural network classification has been analyzed in this paper along with their accuracy measures. The integration of machine learning and semantic web, based on RDF based knowledge graphs is the singularity of the proposed work and represented using experimental results.

Application of Harmony Search Algorithm to Optimize SPARQL Protocol and Resource Description Framework Query Language Queries in Healthcare Data

The rapid developing international of internet, Semantic Web has become a platform for intelligent agents mainly in the healthcare sector. Inside the beyond few years there is a widening in the Semantic web data field in the healthcare industry. With a growth in the quantity of Semantic web data field in health industry, there exist some challenges to be resolved. One such challenge is to provide an efficient querying mechanism that can handle large number of Semantic web data. Consider many query languages; especially SPARQL (SPARQL Protocol and RDF Query Language) is the most popular query language. Each of these query languages has their own design strategy and it was identified in research that it is difficult to handle and query large quantity of RDF data efficiently using these languages. In the proposed process, Harmony search identify met heuristic algorithm to optimize the SPARQL queries in the healthcare data in the applicable manner. The application of Harmony search algorithm is evaluated with large Resource Description Framework (RDF) datasets and SPARQL queries. To assess performance, the algorithm’s implementation is compared to existing nature-inspired algorithms. The performance analysis shows that the proposed application performs well for large RDF datasets.

MuSe: a multi-level storage scheme for big RDF data using MapReduce

Resource Description Framework (RDF) model owing to its flexible structure is increasingly being used to represent Linked data. The rise in amount of Linked data and Knowledge graphs has resulted in an increase in the volume of RDF data. RDF is used to model metadata especially for social media domains where the data is linked. With the plethora of RDF data sources available on the Web, scalable RDF data management becomes a tedious task. In this paper, we present MuSe—an efficient distributed RDF storage scheme for storing and querying RDF data with Hadoop MapReduce. In MuSe, the Big RDF data is stored at two levels for answering the common triple patterns in SPARQL queries. MuSe considers the type of frequently occuring triple patterns and optimizes RDF storage to answer such triple patterns in minimum time. It accesses only the tables that are sufficient for answering a triple pattern instead of scanning the whole RDF dataset. The extensive experiments on two synthetic RDF datasets i.e. LUBM and WatDiv, show that MuSe outperforms the compared state-of-the art frameworks in terms of query execution time and scalability.

VEDAS: an efficient GPU alternative for store and query of large RDF data sets

Resource Description Framework (RDF) is commonly used as a standard for data interchange on the web. The collection of RDF data sets can form a large graph which consumes time to query. It is known that modern Graphic Processing Units (GPUs) can be employed to execute parallel programs in order to speedup the running time. In this paper, we propose a novel RDF data representation along with the query processing algorithm that is suitable for GPU processing. Since the main challenges of GPU architecture are the limited memory sizes, the memory transfer latency, and the vast number of GPU cores. Our system is designed to strengthen the use of GPU cores and reduce the effect of memory transfer. We propose a representation consists of indices and column-based RDF ID data that can reduce the GPU memory requirement. The indexing and pre-upload filtering techniques are then applied to reduce the data transfer between the host and GPU memory. We add the index swapping process to facilitate the sorting and joining data process based on the given variable and add the pre-upload step to reduce the size of results’ storage, and the data transfer time. The experimental results show that our representation is about 35% smaller than the traditional NT format and 40% less compared to that of gStore. The query processing time can be speedup ranging from 1.95 to 397.03 when compared with RDF3X and gStore processing time with WatDiv test suite. It achieves speedup 578.57 and 62.97 for LUBM benchmark when compared to RDF-3X and gStore. The analysis shows the query cases which can gain benefits from our approach.

A Hybrid Approach Combining R*-Tree and k-d Trees to Improve Linked Open Data Query Performance

Semantic Web has recently gained traction with the use of Linked Open Data (LOD) on the Web. Although numerous state-of-the-art methodologies, standards, and technologies are applicable to the LOD cloud, many issues persist. Because the LOD cloud is based on graph-based resource description framework (RDF) triples and the SPARQL query language, we cannot directly adopt traditional techniques employed for database management systems or distributed computing systems. This paper addresses how the LOD cloud can be efficiently organized, retrieved, and evaluated. We propose a novel hybrid approach that combines the index and live exploration approaches for improved LOD join query performance. Using a two-step index structure combining a disk-based 3D R*-tree with the extended multidimensional histogram and flash memory-based k-d trees, we can efficiently discover interlinked data distributed across multiple resources. Because this method rapidly prunes numerous false hits, the performance of join query processing is remarkably improved. We also propose a hot-cold segment identification algorithm to identify regions of high interest. The proposed method is compared with existing popular methods on real RDF datasets. Results indicate that our method outperforms the existing methods because it can quickly obtain target results by reducing unnecessary data scanning and reduce the amount of main memory required to load filtering results.

Web Service Discovery Using Bio-Inspired Holistic Matching Based Linked Data Clustering Model for RDF Data

Introduction: Resource Description Framework (RDF) is the de-facto standard language model for semantic data representation on semantic web. Designing an efficient management of RDF data with huge volume and efficient querying techniques are the primary research areas in semantic web. Methods: So far, several RDF management methods have been offered with data storage designs and query processing algorithms for data retrieval. However, these methods do not adequately address the presence of irrelevant links that degrade the performance of web service discovery. In this paper, we propose a Bio-inspired Holistic Matching based Linked Data Clustering (BHM-LDC) technique for efficient management and querying of RDF data. This technique is essentially based on three algorithms which are designed for RDF data storing, clustering the linked data and web service discovery respectively. Initially, the BHM-LDC technique store the RDF dataset as graph based linked data. Results and Discussion: Then, an Integrated Holistic Entity Matching based Distributed Genetic Algorithm (IHEM-DGA) is proposed to cluster the linked data. Finally, a sub-graph matching based web Service discovery Algorithm that uses the clustered triples has been proposed to find the best web services. Our experimental results reveal the performance of the proposed web service discovery approach by applying on business RDF dataset.