Integrating Heterogeneous Enterprise Data Using Ontology in Supply Chain Management

2019 ◽  
pp. 71-102
Author(s):  
Kamalendu Pal
Keyword(s):  
Relational Databases ◽  
Query Languages ◽  
Heterogeneous Data ◽  
Integration Framework ◽  
Rdf Graph ◽  
Semantic Web Technology ◽  
Business Operations ◽  
Semantic Translation ◽  
Description Framework ◽  
Resource Description

Many industries prefer worldwide business operations due to the economic advantage of globalization on product design and development. These industries increasingly operate globalized multi-tier supply chains and deliver products and services all over the world. This global approach produces huge amounts of heterogeneous data residing at various business operations, and the integration of these data plays an important role. Integrating data from multiple heterogeneous sources need to deal with different data models, database schema, and query languages. This chapter presents a semantic web technology-based data integration framework that uses relational databases and XML data with the help of ontology. To model different source schemas, this chapter proposes a method based on the resource description framework (RDF) graph patterns and query rewriting techniques. The semantic translation between the source schema and RDF ontology is described using query and transformational language SPARQL.

scholarly journals RDF 1.1: Knowledge Representation and Data Integration Language for the Web

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 84 ◽  
Author(s):  
Dominik Tomaszuk ◽  
David Hyland-Wood
Keyword(s):  
Knowledge Representation ◽  
Resource Description Framework ◽  
Relational Databases ◽  
Formal Definition ◽  
Natural Languages ◽  
Description Framework ◽  
Generalized Knowledge ◽  
Definition Of ◽  
Resource Description ◽  
The Web

Resource Description Framework (RDF) can seen as a solution in today’s landscape of knowledge representation research. An RDF language has symmetrical features because subjects and objects in triples can be interchangeably used. Moreover, the regularity and symmetry of the RDF language allow knowledge representation that is easily processed by machines, and because its structure is similar to natural languages, it is reasonably readable for people. RDF provides some useful features for generalized knowledge representation. Its distributed nature, due to its identifier grounding in IRIs, naturally scales to the size of the Web. However, its use is often hidden from view and is, therefore, one of the less well-known of the knowledge representation frameworks. Therefore, we summarise RDF v1.0 and v1.1 to broaden its audience within the knowledge representation community. This article reviews current approaches, tools, and applications for mapping from relational databases to RDF and from XML to RDF. We discuss RDF serializations, including formats with support for multiple graphs and we analyze RDF compression proposals. Finally, we present a summarized formal definition of RDF 1.1 that provides additional insights into the modeling of reification, blank nodes, and entailments.

UPDATE-ENABLED TRIPLIFICATION OF RELATIONAL DATA INTO VIRTUAL RDF STORES

2010 ◽  
Vol 04 (04) ◽  
pp. 423-451 ◽  
Author(s):  
SUNITHA RAMANUJAM ◽  
VAIBHAV KHADILKAR ◽  
LATIFUR KHAN ◽  
MURAT KANTARCIOGLU ◽  
BHAVANI THURAISINGHAM ◽  
...  
Keyword(s):  
Semantic Web ◽  
Relational Database ◽  
Relational Databases ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Relational Data ◽  
Semantic Web Technologies ◽  
Heterogeneous Data Sources ◽  
Description Framework ◽  
Almost All

The current buzzword in the Internet community is the Semantic Web initiative proposed by the W3C to yield a Web that is more flexible and self-adapting. However, for the Semantic Web initiative to become a reality, heterogeneous data sources need to be integrated in order to enable access to them in a homogeneous manner. Since a vast majority of data currently resides in relational databases, integrating relational data sources with semantic web technologies is at the top of the list of activities required to realize the semantic web vision. Several efforts exist that publish relational data as Resource Description Framework (RDF) triples; however almost all current work in this arena is uni-directional, presenting data from an underlying relational database into a corresponding virtual RDF store in a read-only manner. An enhancement over previous relational-to-RDF bridging work in the form of bi-directionality support is presented in this paper. The bi-directional bridge proposed here allows RDF data updates specified as triples to be propagated back into the underlying relational database as tuples. Towards this end, we present various algorithms to translate the triples to be updated/inserted/deleted into equivalent relational attributes/tuples whenever possible. Particular emphasis is laid, in this paper, on the translation and update propagation process for triples containing blank nodes and reification nodes, and a platform enhanced with our algorithms, called D2RQ++, through which bi-directional translation can be achieved, is presented.

An integration approach of multi-source heterogeneous fuzzy spatiotemporal data based on RDF

2021 ◽  
Vol 40 (1) ◽  
pp. 1065-1082
Author(s):  
Luyi Bai ◽  
Nan Li ◽  
Huilei Bai
Keyword(s):  
World Wide ◽  
Integration Method ◽  
Graph Model ◽  
Spatiotemporal Data ◽  
Rdf Graph ◽  
Integration Approach ◽  
The World ◽  
Description Framework ◽  
Resource Description ◽  
Significant Superiority

With the growing importance of the fuzzy spatiotemporal data in information application, there is an increasing need for researching on the integration method of multi-source heterogeneous fuzzy spatiotemporal data. In this paper, we first propose a fuzzy spatiotemporal RDF graph model based on RDF (Resource Description Framework) that proposed by the World Wide Web Consortium (W3C) to represent data in triples (subject, predicate, object). Secondly, we analyze and classify the related heterogeneous problems of multi-source heterogeneous fuzzy spatiotemporal data, and use the fuzzy spatiotemporal RDF graph model to define the corresponding rules to solve these heterogeneous problems. In addition, based on the characteristics of RDF triples, we analyze the heterogeneous problem of multi-source heterogeneous fuzzy spatiotemporal data integration in RDF triples, and provide the integration methods FRDFG in this paper. Finally, we report our experiments results to validate our approach and show its significant superiority.

scholarly journals NLIMED: Natural Language Interface for Model Entity Discovery in Biosimulation Model Repositories

2019 ◽  
Author(s):  
Yuda Munarko ◽  
Dewan M. Sarwar ◽  
Koray Atalag ◽  
David P. Nickerson
Keyword(s):  
Natural Language ◽  
Semantic Annotation ◽  
Apical Plasma Membrane ◽  
Rdf Graph ◽  
Text Feature ◽  
Natural Language Interface ◽  
Natural Language Query ◽  
Description Framework ◽  
Entity Discovery ◽  
Resource Description

AbstractMotivationSemantic annotation is a crucial step to assure reusability and reproducibility of biosimulation models in biology and physiology. For this purpose, the COmputational Modeling in BIology NEtwork (COMBINE) community recommend the use of the Resource Description Framework (RDF). The RDF implementation provides the flexibility of model entity searching (e.g. flux of sodium across apical plasma membrane) by utilising SPARQL. However, the rigidity and complexity of SPARQL syntax and the nature of semantic annotation which is not merely as a simple triple yet forming a tree-like structure may cause a difficulty. Therefore, the availability of an interface to convert a natural language query to SPARQL is beneficial.ResultsWe propose NLIMED, a natural language query to SPARQL interface to retrieve model entities from biosimulation models. Our interface can be applied to various repositories utilising RDF such as the PMR and Biomodels. We evaluate our interface by collecting RDF in the biosimulation models coded using CellML in PMR. First, we extract RDF as a tree structure and then store each subtree of a model entity as a modified triple of a model entity name, path, and class ontology into the RDF Graph Index. We also extract class ontology’s textual metadata from the BioPortal and CellML and manage it in the Text Feature Index. With the Text Feature Index, we annotate phrases resulted by the NLQ Parser (Stanford parser or NLTK parser) into class ontologies. Finally, the detected class ontologies then are composed as SPARQL by incorporating the RDF Graph Index. Our annotator performance is far more powerful compared to the available service provided by BioPortal with F-measure of 0.756 and our SPARQL composer can find all possible SPARQL in the collection based on the annotation results. Currently, we already implement our interface in Epithelial Modelling Platform tool.Availabilityhttps://github.com/napakalas/NLIMED

Storing massive Resource Description Framework (RDF) data: a survey

2016 ◽  
Vol 31 (4) ◽  
pp. 391-413 ◽  
Author(s):  
Zongmin Ma ◽  
Miriam A. M. Capretz ◽  
Li Yan
Keyword(s):  
Data Storage ◽  
Resource Description Framework ◽  
Relational Databases ◽  
Query Language ◽  
Current State ◽  
Rdf Data ◽  
Description Framework ◽  
Resource Description ◽  
Flexible Model ◽  
The Web

AbstractThe Resource Description Framework (RDF) is a flexible model for representing information about resources on the Web. As a W3C (World Wide Web Consortium) Recommendation, RDF has rapidly gained popularity. With the widespread acceptance of RDF on the Web and in the enterprise, a huge amount of RDF data is being proliferated and becoming available. Efficient and scalable management of RDF data is therefore of increasing importance. RDF data management has attracted attention in the database and Semantic Web communities. Much work has been devoted to proposing different solutions to store RDF data efficiently. This paper focusses on using relational databases and NoSQL (for ‘not only SQL (Structured Query Language)’) databases to store massive RDF data. A full up-to-date overview of the current state of the art in RDF data storage is provided in the paper.

scholarly journals gst-store: Querying Large Spatiotemporal RDF Graphs

2017 ◽  
Vol 1 (2) ◽  
pp. 84-103 ◽  
Author(s):  
Dong Wang ◽  
Lei Zou ◽  
Dongyan Zhao
Keyword(s):  
Query Language ◽  
Data Management System ◽  
Rdf Graph ◽  
Spatiotemporal Information ◽  
Simple Protocol ◽  
Rdf Data ◽  
Description Framework ◽  
Query Algorithm ◽  
Rdf Graphs ◽  
Resource Description

Abstract The Simple Protocol and RDF Query Language (SPARQL) query language allows users to issue a structural query over a resource description framework (RDF) graph. However, the lack of a spatiotemporal query language limits the usage of RDF data in spatiotemporal-oriented applications. As the spatiotemporal information continuously increases in RDF data, it is necessary to design an effective and efficient spatiotemporal RDF data management system. In this paper, we formally define the spatiotemporal information-integrated RDF data, introduce a spatiotemporal query language that extends the SPARQL language with spatiotemporal assertions to query spatiotemporal information-integrated RDF data, and design a novel index and the corresponding query algorithm. The experimental results on a large, real RDF graph integrating spatial and temporal information (> 180 million triples) confirm the superiority of our approach. In contrast to its competitors, gst-store outperforms by more than 20%-30% in most cases.

scholarly journals AN APPROACH TO EXTENDING QUERY SENTENCE FOR SEMANTIC ORIENTED SEARCH ON KNOWLEDGE GRAPH

2021 ◽  
Vol 50 (02) ◽  
Author(s):  
TẠ DUY CÔNG CHIẾN
Keyword(s):  
Relational Databases ◽  
Question Answering ◽  
Query Language ◽  
Knowledge Graph ◽  
Web Information Retrieval ◽  
Ontology Language ◽  
Description Framework ◽  
The Relationship ◽  
Resource Description ◽  
Terminological Confusion

There are many applications related to semantic web, information retrieval, information extraction, and question answering applying ontologies in recent years. To avoid the conceptual and terminological confusion, an ontology is built as a taxonomy ontology which identifies and distinguishes concepts as well as terminology. It accomplishes this by specifying a set of generic concepts that characterizes the domain as well as their definitions and interrelationships. There are some methods to represent ontologies, such as Resource Description Framework (RDF), Web Ontology Language (OWL), databases etc. depending on the characteristic of data. RDF, OWL usually is used the cases when data structure is objects which the relationship among the objects is simple. But if the relationship among the objects is more complex, using databases for storing ontologies is an approach to be better. However, using relational databases do not sufficiently support the semantic orientated search by Structured Query Language (SQL) and the searching speed is slow. Therefore, this paper introduces an approach to extending query sentences for semantic oriented search on knowledge graph.

scholarly journals An adaptive spark-based framework for querying large-scale NoSQL and relational databases

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255562
Author(s):  
Eman Khashan ◽  
Ali Eldesouky ◽  
Sally Elghamrawy
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Relational Databases ◽  
Large Scale ◽  
Query Languages ◽  
Heterogeneous Data ◽  
Query Execution ◽  
Database Queries ◽  
Nosql Databases ◽  
Complex Queries

The growing popularity of big data analysis and cloud computing has created new big data management standards. Sometimes, programmers may interact with a number of heterogeneous data stores depending on the information they are responsible for: SQL and NoSQL data stores. Interacting with heterogeneous data models via numerous APIs and query languages imposes challenging tasks on multi-data processing developers. Indeed, complex queries concerning homogenous data structures cannot currently be performed in a declarative manner when found in single data storage applications and therefore require additional development efforts. Many models were presented in order to address complex queries Via multistore applications. Some of these models implemented a complex unified and fast model, while others’ efficiency is not good enough to solve this type of complex database queries. This paper provides an automated, fast and easy unified architecture to solve simple and complex SQL and NoSQL queries over heterogeneous data stores (CQNS). This proposed framework can be used in cloud environments or for any big data application to automatically help developers to manage basic and complicated database queries. CQNS consists of three layers: matching selector layer, processing layer, and query execution layer. The matching selector layer is the heart of this architecture in which five of the user queries are examined if they are matched with another five queries stored in a single engine stored in the architecture library. This is achieved through a proposed algorithm that directs the query to the right SQL or NoSQL database engine. Furthermore, CQNS deal with many NoSQL Databases like MongoDB, Cassandra, Riak, CouchDB, and NOE4J databases. This paper presents a spark framework that can handle both SQL and NoSQL Databases. Four scenarios’ benchmarks datasets are used to evaluate the proposed CQNS for querying different NoSQL Databases in terms of optimization process performance and query execution time. The results show that, the CQNS achieves best latency and throughput in less time among the compared systems.

scholarly journals VP-RDF: An RDF Based Framework to Introduce the Viewpoint in the Description of Resources

2021 ◽  
Vol 26 (1) ◽  
pp. 44-53
Author(s):  
Ouahiba Djama
Keyword(s):  
Resource Description Framework ◽  
Intelligent Systems ◽  
Query Languages ◽  
View Point ◽  
Share Data ◽  
Common Framework ◽  
Description Framework ◽  
Rdf Graphs ◽  
Resource Description ◽  
The Web

Abstract The description of resources and their relationships is an essential task on the web. Generally, the web users do not share the same interests and viewpoints. Each user wants that the web provides data and information according to their interests and specialty. The existing query languages, which allow querying data on the web, cannot take into consideration the viewpoint of the user. We propose introducing the viewpoint in the description of the resources. The Resource Description Framework (RDF) represents a common framework to share data and describe resources. In this study, we aim at introducing the notion of the viewpoint in the RDF. Therefore, we propose a View-Point Resource Description Framework (VP-RDF) as an extension of RDF by adding new elements. The existing query languages (e.g., SPARQL) can query the VP-RDF graphs and provide the user with data and information according to their interests and specialty. Therefore, VP-RDF can be useful in intelligent systems on the web.

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