Query Processing in a Mediator Based Framework for Linked Data Integration

In this paper, the authors present a three-level mediator based framework for linked data integration. In the approach, the mediated schema is represented by a domain ontology, which provides a conceptual representation of the application. Each relevant data source is described by a source ontology, published on the Web according to the Linked Data principles. Each source ontology is rewritten as an application ontology, whose vocabulary is restricted to be a subset of the vocabulary of the domain ontology. The main contribution of the paper is an algorithm for reformulating a user query into sub-queries over the data sources. The reformulation algorithm exploits inter-ontology links to return more complete query results. The approach is illustrated by an example of a virtual store mediating access to online booksellers.

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Improving the Quality of Linked Data Using Statistical Distributions

Information Retrieval and Management ◽

10.4018/978-1-5225-5191-1.ch074 ◽

2018 ◽

pp. 1638-1664 ◽

Cited By ~ 1

Author(s):

Heiko Paulheim ◽

Christian Bizer

Keyword(s):

Knowledge Base ◽

Linked Data ◽

Relational Databases ◽

Knowledge Bases ◽

Structured Data ◽

Data Sources ◽

Data Sets ◽

Statistical Distributions ◽

The Web

Linked Data on the Web is either created from structured data sources (such as relational databases), from semi-structured sources (such as Wikipedia), or from unstructured sources (such as text). In the latter two cases, the generated Linked Data will likely be noisy and incomplete. In this paper, we present two algorithms that exploit statistical distributions of properties and types for enhancing the quality of incomplete and noisy Linked Data sets: SDType adds missing type statements, and SDValidate identifies faulty statements. Neither of the algorithms uses external knowledge, i.e., they operate only on the data itself. We evaluate the algorithms on the DBpedia and NELL knowledge bases, showing that they are both accurate as well as scalable. Both algorithms have been used for building the DBpedia 3.9 release: With SDType, 3.4 million missing type statements have been added, while using SDValidate, 13,000 erroneous RDF statements have been removed from the knowledge base.

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Semantic-Based Geospatial Data Integration With Unique Features

Geospatial Intelligence ◽

10.4018/978-1-5225-8054-6.ch012 ◽

2019 ◽

pp. 254-277 ◽

Cited By ~ 1

Author(s):

Ying Zhang ◽

Chaopeng Li ◽

Na Chen ◽

Shaowen Liu ◽

Liming Du ◽

...

Keyword(s):

Data Integration ◽

High Performance ◽

Data Access ◽

Heterogeneous Data ◽

Geospatial Data ◽

Experimental Results ◽

Data Sources ◽

Data Format ◽

Access Protocols ◽

Data Source

Since large amount of geospatial data are produced by various sources, geospatial data integration is difficult because of the shortage of semantics. Despite standardised data format and data access protocols, such as Web Feature Service (WFS), can enable end-users with access to heterogeneous data stored in different formats from various sources, it is still time-consuming and ineffective due to the lack of semantics. To solve this problem, a prototype to implement the geospatial data integration is proposed by addressing the following four problems, i.e., geospatial data retrieving, modeling, linking and integrating. We mainly adopt four kinds of geospatial data sources to evaluate the performance of the proposed approach. The experimental results illustrate that the proposed linking method can get high performance in generating the matched candidate record pairs in terms of Reduction Ratio(RR), Pairs Completeness(PC), Pairs Quality(PQ) and F-score. The integrating results denote that each data source can get much Complementary Completeness(CC) and Increased Completeness(IC).

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Semantic Web and Geospatial Unique Features Based Geospatial Data Integration

Geospatial Intelligence ◽

10.4018/978-1-5225-8054-6.ch011 ◽

2019 ◽

pp. 230-253

Author(s):

Ying Zhang ◽

Chaopeng Li ◽

Na Chen ◽

Shaowen Liu ◽

Liming Du ◽

...

Keyword(s):

Semantic Web ◽

Data Integration ◽

High Performance ◽

Data Access ◽

Heterogeneous Data ◽

Geospatial Data ◽

Data Sources ◽

Modeling Process ◽

Translation Function ◽

Data Source

Since large amount of geospatial data are produced by various sources and stored in incompatible formats, geospatial data integration is difficult because of the shortage of semantics. Despite standardised data format and data access protocols, such as Web Feature Service (WFS), can enable end-users with access to heterogeneous data stored in different formats from various sources, it is still time-consuming and ineffective due to the lack of semantics. To solve this problem, a prototype to implement the geospatial data integration is proposed by addressing the following four problems, i.e., geospatial data retrieving, modeling, linking and integrating. First, we provide a uniform integration paradigm for users to retrieve geospatial data. Then, we align the retrieved geospatial data in the modeling process to eliminate heterogeneity with the help of Karma. Our main contribution focuses on addressing the third problem. Previous work has been done by defining a set of semantic rules for performing the linking process. However, the geospatial data has some specific geospatial relationships, which is significant for linking but cannot be solved by the Semantic Web techniques directly. We take advantage of such unique features about geospatial data to implement the linking process. In addition, the previous work will meet a complicated problem when the geospatial data sources are in different languages. In contrast, our proposed linking algorithms are endowed with translation function, which can save the translating cost among all the geospatial sources with different languages. Finally, the geospatial data is integrated by eliminating data redundancy and combining the complementary properties from the linked records. We mainly adopt four kinds of geospatial data sources, namely, OpenStreetMap(OSM), Wikmapia, USGS and EPA, to evaluate the performance of the proposed approach. The experimental results illustrate that the proposed linking method can get high performance in generating the matched candidate record pairs in terms of Reduction Ratio(RR), Pairs Completeness(PC), Pairs Quality(PQ) and F-score. The integrating results denote that each data source can get much Complementary Completeness(CC) and Increased Completeness(IC).

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Semantic-Based Geospatial Data Integration With Unique Features

Innovations, Developments, and Applications of Semantic Web and Information Systems - Advances in Web Technologies and Engineering ◽

10.4018/978-1-5225-5042-6.ch015 ◽

2018 ◽

pp. 393-416

Author(s):

Ying Zhang ◽

Chaopeng Li ◽

Na Chen ◽

Shaowen Liu ◽

Liming Du ◽

...

Keyword(s):

Data Integration ◽

High Performance ◽

Data Access ◽

Heterogeneous Data ◽

Geospatial Data ◽

Experimental Results ◽

Data Sources ◽

Data Format ◽

Access Protocols ◽

Data Source

Since large amount of geospatial data are produced by various sources, geospatial data integration is difficult because of the shortage of semantics. Despite standardised data format and data access protocols, such as Web Feature Service (WFS), can enable end-users with access to heterogeneous data stored in different formats from various sources, it is still time-consuming and ineffective due to the lack of semantics. To solve this problem, a prototype to implement the geospatial data integration is proposed by addressing the following four problems, i.e., geospatial data retrieving, modeling, linking and integrating. We mainly adopt four kinds of geospatial data sources to evaluate the performance of the proposed approach. The experimental results illustrate that the proposed linking method can get high performance in generating the matched candidate record pairs in terms of Reduction Ratio(RR), Pairs Completeness(PC), Pairs Quality(PQ) and F-score. The integrating results denote that each data source can get much Complementary Completeness(CC) and Increased Completeness(IC).

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An E-Commerce Platform for Industrialized Construction Procurement Based on BIM and Linked Data

Sustainability ◽

10.3390/su10082613 ◽

2018 ◽

Vol 10 (8) ◽

pp. 2613

Author(s):

Dandan He ◽

Zhongfu Li ◽

Chunlin Wu ◽

Xin Ning

Keyword(s):

Data Integration ◽

Construction Industry ◽

Resource Description Framework ◽

Linked Data ◽

Query Language ◽

Rapid Development ◽

Data Sources ◽

Product Data ◽

Description Framework ◽

Resource Description

Industrialized construction has raised the requirements of procurement methods used in the construction industry. The rapid development of e-commerce offers efficient and effective solutions, however the large number of participants in the construction industry means that the data involved are complex, and problems arise related to volume, heterogeneity, and fragmentation. Thus, the sector lags behind others in the adoption of e-commerce. In particular, data integration has become a barrier preventing further development. Traditional e-commerce platform, which considered data integration for common product data, cannot meet the requirements of construction product data integration. This study aimed to build an information-integrated e-commerce platform for industrialized construction procurement (ICP) to overcome some of the shortcomings existing platforms. We proposed a platform based on Building Information Modelling (BIM) and linked data, taking an innovative approach to data integration. It uses industrialized construction technology to support product standardization, BIM to support procurement process, and linked data to connect different data sources. The platform was validated using a case study. With the development of an e-commerce ontology, industrialized construction component information was extracted from BIM models and converted to Resource Description Framework (RDF) format. Related information from different data sources was also converted to RDF format, and Simple Protocol and Resource Description Framework Query Language (SPARQL) queries were implemented. The platform provides a solution for the development of e-commerce platform in the construction industry.

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Integrate inconsistent and heterogeneous data based on user feedback

International Journal of Intelligent Computing and Cybernetics ◽

10.1108/ijicc-04-2014-0013 ◽

2015 ◽

Vol 8 (2) ◽

pp. 187-203 ◽

Cited By ~ 2

Author(s):

Lihua Lu ◽

Hengzhen Zhang ◽

Xiao-Zhi Gao

Keyword(s):

Data Integration ◽

Data Model ◽

Quality Criteria ◽

Heterogeneous Data ◽

Data Sources ◽

Content Type ◽

Inconsistent Data ◽

Data Inconsistency ◽

Multi Attribute Decision Making ◽

Data Source

Purpose – Data integration is to combine data residing at different sources and to provide the users with a unified interface of these data. An important issue on data integration is the existence of conflicts among the different data sources. Data sources may conflict with each other at data level, which is defined as data inconsistency. The purpose of this paper is to aim at this problem and propose a solution for data inconsistency in data integration. Design/methodology/approach – A relational data model extended with data source quality criteria is first defined. Then based on the proposed data model, a data inconsistency solution strategy is provided. To accomplish the strategy, fuzzy multi-attribute decision-making (MADM) approach based on data source quality criteria is applied to obtain the results. Finally, users feedbacks strategies are proposed to optimize the result of fuzzy MADM approach as the final data inconsistent solution. Findings – To evaluate the proposed method, the data obtained from the sensors are extracted. Some experiments are designed and performed to explain the effectiveness of the proposed strategy. The results substantiate that the solution has a better performance than the other methods on correctness, time cost and stability indicators. Practical implications – Since the inconsistent data collected from the sensors are pervasive, the proposed method can solve this problem and correct the wrong choice to some extent. Originality/value – In this paper, for the first time the authors study the effect of users feedbacks on integration results aiming at the inconsistent data.

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Integrating distributed data sources with OGSA–DAI DQP and V iews

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2010.0166 ◽

2010 ◽

Vol 368 (1926) ◽

pp. 4133-4145 ◽

Cited By ~ 18

Author(s):

Bartosz Dobrzelecki ◽

Amrey Krause ◽

Alastair C. Hume ◽

Alistair Grant ◽

Mario Antonioletti ◽

...

Keyword(s):

Data Integration ◽

Query Processing ◽

Data Access ◽

Data Sources ◽

Distributed Data ◽

Grid Services ◽

Distributed Query Processing ◽

Processing Resource ◽

Distributed Query ◽

Open Grid Services Architecture

OGSA-DAI (Open Grid Services Architecture Data Access and Integration) is a framework for building distributed data access and integration systems. Until recently, it lacked the built-in functionality that would allow easy creation of federations of distributed data sources. The latest release of the OGSA-DAI framework introduced the OGSA-DAI DQP (Distributed Query Processing) resource. The new resource encapsulates a distributed query processor, that is able to orchestrate distributed data sources when answering declarative user queries. The query processor has many extensibility points, making it easy to customize. We have also introduced a new OGSA-DAI V iews resource that provides a flexible method for defining views over relational data. The interoperability of the two new resources, together with the flexibility of the OGSA-DAI framework, allows the building of highly customized data integration solutions.

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GRALD: an Approach for Goal and Risk Analysis in the Development of Information Systems for the Web of Data

Journal of Software Engineering Research and Development ◽

10.5753/jserd.2019.18 ◽

2019 ◽

Vol 7 ◽

pp. 4

Author(s):

Marcio Louzada De Freitas ◽

Renata Silva Souza Guizzardi ◽

Vítor Estêvão Silva Souza

Keyword(s):

Information Systems ◽

Linked Data ◽

Conceptual Modeling ◽

Risk Identification ◽

Domain Ontology ◽

Intrinsic Activity ◽

Published Data ◽

Functional Requirements ◽

Web Of Data ◽

The Web

The publication of Linked Data on the Web regarding several application domains leads to new problems related to Requirements Engineering, which needs to take into account aspects related to new ways of developing systems and delivering information integrated with the Web of Data. Tasks such as (functional and non-functional) requirements elicitation and ontology-based conceptual modeling can be applied to the development of systems that publish Linked Data, in order to obtain a better shared conceptualization (i.e., a domain ontology) of the published data. The use of vocabularies is an intrinsic activity when publishing or consuming Linked Data and their choice can be supported by the elicited requirements and domain ontology. However, it is important to assess the risk when choosing external vocabularies, as their use can lead to problems, such as misinterpretation of meanings due to poor documentation, connection timeouts due to infrastructure problems, etc. Thus, risk identification, modeling and analysis techniques can be employed, in order to identify risks and their impacts on stakeholder goals. In this work, we propose GRALD: Goals and Risks Analysis for Linked Data, an approach for modeling goals and risks for information systems for the Web of Data.

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DATA INTEGRATION MODEL DESIGN FOR SUPPORTING DATA CENTER PATIENT SERVICES DISTRIBUTED INSURANCE PURCHASE WITH VIEW BASED DATA INTEGRATION

Computer Engineering Science and System Journal ◽

10.24114/cess.v3i2.8895 ◽

2018 ◽

Vol 3 (2) ◽

pp. 162

Author(s):

Slamet Sudaryanto Nurhendratno ◽

Sudaryanto Sudaryanto

Keyword(s):

Data Integration ◽

Data Center ◽

Data Exchange ◽

Heterogeneous Data ◽

Data Sources ◽

Query Rewriting ◽

Schema Mapping ◽

Data Interoperability ◽

Integration Model ◽

Data Source

Data integration is an important step in integrating information from multiple sources. The problem is how to find and combine data from scattered data sources that are heterogeneous and have semantically informant interconnections optimally. The heterogeneity of data sources is the result of a number of factors, including storing databases in different formats, using different software and hardware for database storage systems, designing in different data semantic models (Katsis & Papakonstantiou, 2009, Ziegler & Dittrich , 2004). Nowadays there are two approaches in doing data integration that is Global as View (GAV) and Local as View (LAV), but both have different advantages and limitations so that proper analysis is needed in its application. Some of the major factors to be considered in making efficient and effective data integration of heterogeneous data sources are the understanding of the type and structure of the source data (source schema). Another factor to consider is also the view type of integration result (target schema). The results of the integration can be displayed into one type of global view or a variety of other views. So in integrating data whose source is structured the approach will be different from the integration of the data if the data source is not structured or semi-structured. Scheme mapping is a specific declaration that describes the relationship between the source scheme and the target scheme. In the scheme mapping is expressed in in some logical formulas that can help applications in data interoperability, data exchange and data integration. In this paper, in the case of establishing a patient referral center data center, it requires integration of data whose source is derived from a number of different health facilities, it is necessary to design a schema mapping system (to support optimization). Data Center as the target orientation schema (target schema) from various reference service units as a source schema (source schema) has the characterization and nature of data that is structured and independence. So that the source of data can be integrated tersetruktur of the data source into an integrated view (as a data center) with an equivalent query rewriting (equivalent). The data center as a global schema serves as a schema target requires a "mediator" that serves "guides" to maintain global schemes and map (mapping) between global and local schemes. Data center as from Global As View (GAV) here tends to be single and unified view so to be effective in its integration process with various sources of schema which is needed integration facilities "integration". The "Pemadu" facility is a declarative mapping language that allows to specifically link each of the various schema sources to the data center. So that type of query rewriting equivalent is suitable to be applied in the context of query optimization and maintenance of physical data independence.Keywords: Global as View (GAV), Local as View (LAV), source schema ,mapping schema

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