Semantic Web Databases Research Articles

Development of Semantic-Web Based Forest Resources Database

There are some forest resources (such as Trees, Shrubs, and Water bodies) in the university main campus. These resources are used for various purposes ranging from teaching, researches medicinal and domestic purposes and among many others. However, there is no record of official documentation on them. Thus, this study aims to provide a semantic-web based database system for efficient management of forest resources. We adopted the W3C semantic web framework in the design and development of this work. Specifically, we focused on the layers 1 to 5 of the framework. The layers include; Resource Description Framework (RDF), Web Ontology Language (OWL), Extensible Markup Language (XML), User Interface). The Uschold’s and Kings’ method was applied in creating the ontology. The implementation process was carried out by populating the database with the Taxonomies of the Trees on University of Energy and Natural Resources (U.E.N.R) campus by means of creating an Ontology File using protégé. Some of the basic information that we provided by the proposed semantic web database include trees, their scientific name, geographical coordinate, physical location, medicinal value and other important data. We performed alpha test by involving six senior forest science students who used the application, evaluated it and came out with a good result which is shown in the chapter four of this documentation.

A Comparative Analysis of Semantic Web Databases Based on Scalability and Performance

Research is rapidly increasing day by day that taken too much efforts in exploring some interesting and some related publications over the internet.as we already know that every data bases have a different architecture that varies the performance in terms of storage architecture and medium. In this research paper we analyzed of two main big data types of Semantic web that iscategorized into two types (i) in memory Native (ii) Non-native Non-memory which are disk reside and Non-native is used for services management for instance, SQL, MySQL, and another is Oracle that is just used for storing purpose. Data bases is very important model specially, when any model come into existence. For instance, when we offer for storing purpose of that data then where it should have o store and then definitely it must be access efficiently. The proposed methodology consist test case for data retrieving and query optimization method to analyze performance of databases. When we talk about access data bases from any source then we query them for accessing. LUMB (Lehigh University Benchmark) is being used for testing performance and it cannot be used for storing data. Semantic Web Data (SWD) give a capability in such a way if anybody want to access / encode related data then it can be retrieved efficiently. Our main objective of research we have compared two types of SWD Native store and Non-nativestore and then we analyzed them

A Robust Optimization Approach of SQL-to-SPARQL Query Rewriting

In order to ensure the interoperability between semantic web and relational databases, several approaches have been developed to ensure SQL-to-SPARQL query transformation direction, but all these approaches have the same weakness. In fact, they convert directly the input SQL query to its equivalent SPARQL one without any pre-processing phase enabling the optimization of this input query filled by users before starting the conversion process. This weakness has motivated us to add a pretreatment phase aiming to optimize the most important SQL statements which seem to have the biggest impact on the effectiveness of the transformed queries. Our main contribution is to enrich these rewriting systems by adding an optimization layer that integrate a set of simplification rules of Left, Right and Full Outer Join in order to avoid, firstly unnecessary operations during the conversion process, and secondly SPARQL queries with a high complexity due to Optional patterns obtained from outer join in this conversion context.

Hardware-aided update acceleration in a hybrid Semantic Web database system

In this paper, we focus on update optimizations in a Semantic Web database system aided by a field programmable gate array (FPGA). Many databases utilize B\(^+\)-tree index structures for querying data. In this scenario, the B\(^+\)-tree levels are distributed between the host system with the lower inner levels including the leaves and the FPGA with the upper inner levels including the root. In this way we can perform a parallel search inside the nodes of the FPGA by exploiting its parallel nature. Since update operations presuppose a search for the correct position inside a B\(^+\)-tree leaf, these operations can benefit from these parallel searches. We present our scheduler ideas to estimate the expected benefit against the setup of the system and further adjustments made necessary by performed updates. In a best, average and worst-case scenario, we show how our scheduler would calculate the possible acceleration of such a system.

Fast Compression of Large Semantic Web Data Using X10

The Semantic Web comprises enormous volumes of semi-structured data elements. For interoperability, these elements are represented by long strings. Such representations are not efficient for the purposes of applications that perform computations over large volumes of such information. A common approach to alleviate this problem is through the use of compression methods that produce more compact representations of the data. The use of dictionary encoding is particularly prevalent in Semantic Web database systems for this purpose. However, centralized implementations present performance bottlenecks, giving rise to the need for scalable, efficient distributed encoding schemes. In this paper, we propose an efficient algorithm for fast encoding large Semantic Web data. Specially, we present the detailed implementation of our approach based on the state-of-art asynchronous partitioned global address space (APGAS) parallel programming model. We evaluate performance on a cluster of up to 384 cores and datasets of up to 11 billion triples (1.9 TB). Compared to the state-of-art approach, we demonstrate a speed-up of $2.6 - 7.4\times$ and excellent scalability. In the meantime, these results also illustrate the significant potential of the APGAS model for efficient implementation of dictionary encoding and contributes to the engineering of more efficient, larger scale Semantic Web applications.

TOWARD SEMANTIC WEB INFRASTRUCTURE FOR SPATIAL FEATURES' INFORMATION

Abstract. The Web and its capabilities can be employed as a tool for data and information integration if comprehensive datasets and appropriate technologies and standards enable the web with interpretation and easy alignment of data and information. Semantic Web along with the spatial functionalities enable the web to deal with the huge amount of data and information. The present study investigate the advantages and limitations of the Spatial Semantic Web and compare its capabilities with relational models in order to build a spatial data infrastructure. An architecture is proposed and a set of criteria is defined for the efficiency evaluation. The result demonstrate that when using the data with special characteristics such as schema dynamicity, sparse data or available relations between the features, the spatial semantic web and graph databases with spatial operations are preferable.

Accelerated join evaluation in Semantic Web databases by using FPGAs

SummaryWhile the amount of information steadily increases, the requirements on the response time to query these information become more strict. Under those conditions, conventional database systems reach their limits and cannot meet these performance requirements anymore. In recent years, systems with many processing cores are considered to satisfy these demands. Furthermore, these systems include more and more heterogeneous cores tailor‐made to solve one specific task in an efficient manner. However, dedicated hardware accelerators are inflexible and cannot be adapted to the requirements of a dedicated query. Thus, the challenge is orchestrating the diversity of the functionality of all the cores to be optimized for performance/energy efficiency. In this paper, a concept is introduced on how to develop a flexible Field‐Programmable Gate Arrays (FPGA)‐based hardware accelerator to improve the performance of query evaluation in a Semantic Web database. As a first step to the hardware/software system, several joint algorithms are implemented on an FPGA and evaluated against a well‐developed software solution (implemented in C). The comparison shows a significant speedup of up to 10 times. Because of the complexity of the join operator, it is promising that the overall performance of query evaluation can be further enhanced by processing whole queries on an FPGA. Copyright © 2015 John Wiley & Sons, Ltd.

SPARQLGraph: a web-based platform for graphically querying biological Semantic Web databases.

BackgroundSemantic Web has established itself as a framework for using and sharing data across applications and database boundaries. Here, we present a web-based platform for querying biological Semantic Web databases in a graphical way.ResultsSPARQLGraph offers an intuitive drag & drop query builder, which converts the visual graph into a query and executes it on a public endpoint. The tool integrates several publicly available Semantic Web databases, including the databases of the just recently released EBI RDF platform. Furthermore, it provides several predefined template queries for answering biological questions. Users can easily create and save new query graphs, which can also be shared with other researchers.ConclusionsThis new graphical way of creating queries for biological Semantic Web databases considerably facilitates usability as it removes the requirement of knowing specific query languages and database structures. The system is freely available at http://sparqlgraph.i-med.ac.at.

Data Partitioning for Semantic Web

Semantic web database is an RDF database. Tremendous increase can be seen in semantic web data, as real life applications of semantic web are using this data. Efficient management of this data at a larger scale, and efficient query performance are the two major concerns. This work aims at analyzing query performance issues in terms of execution time and scalability using data partitioning techniques. An experiment is devised to show effect of data partitioning technique on query performance. It demonstrates the query performance analysis for partitioning techniques applied. Vertical partitioning, hybrid partitioning and property table was used to store the RDF data and query execution time is analyzed. The experiment was carried out on a very small dummy data and now it will be scaled up using Barton library catalogue.

Scalability and Performance Evaluation of Semantic Web Databases

The existing evaluation methodologies of semantic web databases are limited because they measure performance of the databases only in terms of time and do not cover resource utilization for their data manipulation operations. To cope with deficiencies of the existing methodologies, we have proposed a new scalability and performance evaluation methodology to perform comparative analysis of the databases and defined metrics for their query cost estimation. The proposed methodology comprises test cases for data access methods and query optimization techniques to analyze performance of the databases. The methodology was applied to the existing semantic web databases using the Barton library dataset. We highlighted the key strengths and weaknesses of the databases and discovered their scalability behavior. The evaluation results show that the proposed methodology depicts scalability behavior and performance analysis of each database better than the existing evaluation methodologies.

A survey of view selection methods

Materialized view selection is a critical problem in many applications such as query processing, data warehousing, distributed and semantic web databases, etc. We refer to the problem of selecting an appropriate set of materialized views as the view selection problem. Many different view selection methods have been proposed in the literature to address this issue. The present paper provides a survey of view selection methods. It defines a framework for highlighting the view selection problem by identifying the main dimensions that are the basis in the classification of view selection methods. Based on this classification, this study reviews most of the view selection methods by identifying respective potentials and limits.

Accelerating large semantic web databases by parallel join computations of SPARQL queries

While a number of optimizing techniques have been developed to efficiently process increasing large Semantic Web databases, these optimization approaches have not fully leveraged the powerful computation capability of modern computers. Today's multi-core computers promise an enormous performance boost by providing a parallel computing platform. Although the parallel relational database systems have been well built, parallel query computing in Semantic Web databases have not extensively been studied. In this work, we develop the parallel algorithms for join computations of SPARQL queries. Our performance study shows that the parallel computation of SPARQL queries significantly speeds up querying large Semantic Web databases.

View selection in Semantic Web databases

We consider the setting of a Semantic Web database, containing both explicit data encoded in RDF triples, and implicit data, implied by the RDF semantics. Based on a query workload, we address the problem of selecting a set of views to be materialized in the database, minimizing a combination of query processing, view storage, and view maintenance costs. Starting from an existing relational view selection method, we devise new algorithms for recommending view sets, and show that they scale significantly beyond the existing relational ones when adapted to the RDF context. To account for implicit triples in query answers, we propose a novel RDF query reformulation algorithm and an innovative way of incorporating it into view selection in order to avoid a combinatorial explosion in the complexity of the selection process. The interest of our techniques is demonstrated through a set of experiments.

A knowledge-based approach to merging information

There is an increasing need for technology for merging semi-structured information (such as structured reports) from heterogeneous sources. For this, we advocate a knowledge-based approach when the information to be merged incorporates diverse, and potentially complex, conflicts (inconsistencies). In this paper, we contrast the goals of knowledge-based merging with other technologies such as semantic web technologies, information mediators, and database integration systems. We then explain how a system for knowledge-based merging can be constructed for a given application. To support the use of a knowledgebase, we use fusion rules to manage the semi-structured information that is input for merging. Fusion rules are a form of scripting language that defines how structured reports should be merged. The antecedent of a fusion rule is a call to investigate the information in the structured reports and the background knowledge, and the consequent of a fusion rule is a formula specifying an action to be undertaken to form a merged report. Fusion rules are not necessarily a definitive specification of how the input can be merged. They can be used by the user to explore different ways that the input can be merged. However, if the user has sufficient confidence in the output from a set of fusion rules, they can be regarded as a definitive specification for merging, and furthermore, they can then be treated as a form of meta-knowledge that gives the provenance of the merged reports. The integrated usage of fusion rules with a knowledgebase offers a practical and valuable technology for merging conflicting information.

On querying simple conceptual graphs with negation

We consider basic conceptual graphs, namely simple conceptual graphs (SGs), which are equivalent to the existential conjunctive positive fragment of first-order logic. The fundamental problem, deduction, is performed by a graph homomorphism called projection. The existence of a projection from a SG Q to a SG G means that the knowledge represented by Q is deducible from the knowledge represented by G. In this framework, a knowledge base is composed of SGs representing facts and a query is itself a SG. We focus on the issue of querying SGs, which highlights another fundamental problem, namely query answering. Each projection from a query to a fact defines an answer to the query, with an answer being itself a SG. The query answering problem asks for all answers to a query. This paper introduces atomic negation into this framework. Several understandings of negation are explored, which are all of interest in real world applications. In particular, we focus on situations where, in the context of incomplete knowledge, classical negation is not satisfactory because deduction can be proven but there is no answer to the query. We show that intuitionistic deduction captures the notion of an answer and can be solved by projection checking. Algorithms are provided for all studied problems. They are all based on projection. They can thus be combined to deal with several kinds of negation simultaneously. Relationships with problems on conjunctive queries in databases are recalled and extended. Finally, we point out that this discussion can be put in the context of semantic web databases.

Expressing inter‐link constraints in OWL knowledge bases

Abstract: In this paper the Web Ontology Language (OWL) is examined to instantiate expert system knowledge bases intended for semantic Web applications. In particular, OWL is analyzed for expressing Unified Modeling Language (UML) representations that have been augmented with propositional logic asserted as inter‐link constraints. The motivation is ultimately to provide declarative propositional logic constraints that can be represented in UML and declaratively implemented using OWL and other constructs to realize semantic Web knowledge base repositories and databases to facilitate expert system applications. The results of this paper show that OWL is sufficient for capturing most inter‐link constraints asserted on generalization/specialization instances; however, OWL alone is inadequate for representing some inter‐link constraints asserted on associations. We propose enhancements to OWL via RDF extensions for the reification of associations into classes. These extensions mitigate all concerns that were identified in OWL as part of this study. The result is increased support of declarative constraint representations, which can be expressed in knowledge bases in the context of the semantic Web.