Improving the Quality of Linked Data Using Statistical Distributions

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.

Download Full-text

Improving the Quality of Linked Data Using Statistical Distributions

International Journal on Semantic Web and Information Systems ◽

10.4018/ijswis.2014040104 ◽

2014 ◽

Vol 10 (2) ◽

pp. 63-86 ◽

Cited By ~ 74

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

Download Full-text

An Unsupervised Approach for Determining Link Specifications

International Journal of Information Technology and Web Engineering ◽

10.4018/ijitwe.2018100106 ◽

2018 ◽

Vol 13 (4) ◽

pp. 104-123

Author(s):

Khayra Bencherif ◽

Mimoun Malki ◽

Djamel Amar Bensaber

Keyword(s):

Linked Data ◽

Open Data ◽

Real Data ◽

Knowledge Bases ◽

Structured Data ◽

Data Sets ◽

Novel Approach ◽

Link Discovery ◽

Unsupervised Approach

This article describes how the Linked Open Data Cloud project allows data providers to publish structured data on the web according to the Linked Data principles. In this context, several link discovery frameworks have been developed for connecting entities contained in knowledge bases. In order to achieve a high effectiveness for the link discovery task, a suitable link configuration is required to specify the similarity conditions. Unfortunately, such configurations are specified manually; which makes the link discovery task tedious and more difficult for the users. In this article, the authors address this drawback by proposing a novel approach for the automatic determination of link specifications. The proposed approach is based on a neural network model to combine a set of existing metrics into a compound one. The authors evaluate the effectiveness of the proposed approach in three experiments using real data sets from the LOD Cloud. In addition, the proposed approach is compared against link specifications approaches to show that it outperforms them in most experiments.

Download Full-text

SEMANTIC LINKING SPATIAL RDF DATA TO THE WEB DATA SOURCES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-639-2018 ◽

2018 ◽

Vol XLII-4 ◽

pp. 639-645 ◽

Cited By ~ 1

Author(s):

D. Ulutaş Karakol ◽

G. Kara ◽

C. Yılmaz ◽

Ç. Cömert

Keyword(s):

Semantic Web ◽

Spatial Data ◽

Linked Data ◽

Relational Databases ◽

Data Sources ◽

Data Conversion ◽

Web Data ◽

Link Discovery ◽

Rdf Data ◽

The Web

<p><strong>Abstract.</strong> Large amounts of spatial data are hold in relational databases. Spatial data in the relational databases must be converted to RDF for semantic web applications. Spatial data is an important key factor for creating spatial RDF data. Linked Data is the most preferred way by users to publish and share data in the relational databases on the Web. In order to define the semantics of the data, links are provided to vocabularies (ontologies or other external web resources) that are common conceptualizations for a domain. Linking data of resource vocabulary with globally published concepts of domain resources combines different data sources and datasets, makes data more understandable, discoverable and usable, improves data interoperability and integration, provides automatic reasoning and prevents data duplication. The need to convert relational data to RDF is coming in sight due to semantic expressiveness of Semantic Web Technologies. One of the important key factors of Semantic Web is ontologies. Ontology means “explicit specification of a conceptualization”. The semantics of spatial data relies on ontologies. Linking of spatial data from relational databases to the web data sources is not an easy task for sharing machine-readable interlinked data on the Web. Tim Berners-Lee, the inventor of the World Wide Web and the advocate of Semantic Web and Linked Data, layed down the Linked Data design principles. Based on these rules, firstly, spatial data in the relational databases must be converted to RDF with the use of supporting tools. Secondly, spatial RDF data must be linked to upper level-domain ontologies and related web data sources. Thirdly, external data sources (ontologies and web data sources) must be determined and spatial RDF data must be linked related data sources. Finally, spatial linked data must be published on the web. The main contribution of this study is to determine requirements for finding RDF links and put forward the deficiencies for creating or publishing linked spatial data. To achieve this objective, this study researches existing approaches, conversion tools and web data sources for relational data conversion to the spatial RDF. In this paper, we have investigated current state of spatial RDF data, standards, open source platforms (particularly D2RQ, Geometry2RDF, TripleGeo, GeoTriples, Ontop, etc.) and the Web Data Sources. Moreover, the process of spatial data conversion to the RDF and how to link it to the web data sources is described. The implementation of linking spatial RDF data to the web data sources is demonstrated with an example use case. Road data has been linked to the one of the related popular web data sources, DBPedia. SILK, a tool for discovering relationships between data items within different Linked Data sources, is used as a link discovery framework. Also, we evaluated other link discovery tools e.g. LIMES, Silk and results are compared to carry out matching/linking task. As a result, linked road data is shared and represented as an information resource on the web and enriched with definitions of related different resources. By this way, road datasets are also linked by the related classes, individuals, spatial relations and properties they cover such as, construction date, road length, coordinates, etc.</p>

Download Full-text

Use of Linked Data principles for semantic management of scanned documents

Transinformação ◽

10.1590/2318-08892016000200010 ◽

2016 ◽

Vol 28 (2) ◽

pp. 241-251 ◽

Cited By ~ 1

Author(s):

Luciane Lena Pessanha Monteiro ◽

Mark Douglas de Azevedo Jacyntho

Keyword(s):

Decision Making ◽

Knowledge Base ◽

Web Application ◽

Linked Data ◽

World Wide ◽

Decision Making Process ◽

Whole Process ◽

The World ◽

Scanned Documents ◽

The Web

The study addresses the use of the Semantic Web and Linked Data principles proposed by the World Wide Web Consortium for the development of Web application for semantic management of scanned documents. The main goal is to record scanned documents describing them in a way the machine is able to understand and process them, filtering content and assisting us in searching for such documents when a decision-making process is in course. To this end, machine-understandable metadata, created through the use of reference Linked Data ontologies, are associated to documents, creating a knowledge base. To further enrich the process, (semi)automatic mashup of these metadata with data from the new Web of Linked Data is carried out, considerably increasing the scope of the knowledge base and enabling to extract new data related to the content of stored documents from the Web and combine them, without the user making any effort or perceiving the complexity of the whole process.

Download Full-text

Data mining for building knowledge bases: techniques, architectures and applications

The Knowledge Engineering Review ◽

10.1017/s0269888916000047 ◽

2016 ◽

Vol 31 (2) ◽

pp. 97-123 ◽

Cited By ~ 4

Author(s):

Alfred Krzywicki ◽

Wayne Wobcke ◽

Michael Bain ◽

John Calvo Martinez ◽

Paul Compton

Keyword(s):

Data Mining ◽

Knowledge Base ◽

Question Answering ◽

Knowledge Bases ◽

Event Extraction ◽

Data Sources ◽

Small Scale ◽

Knowledge Mining ◽

Practical Applications ◽

Unstructured Text

AbstractData mining techniques for extracting knowledge from text have been applied extensively to applications including question answering, document summarisation, event extraction and trend monitoring. However, current methods have mainly been tested on small-scale customised data sets for specific purposes. The availability of large volumes of data and high-velocity data streams (such as social media feeds) motivates the need to automatically extract knowledge from such data sources and to generalise existing approaches to more practical applications. Recently, several architectures have been proposed for what we callknowledge mining: integrating data mining for knowledge extraction from unstructured text (possibly making use of a knowledge base), and at the same time, consistently incorporating this new information into the knowledge base. After describing a number of existing knowledge mining systems, we review the state-of-the-art literature on both current text mining methods (emphasising stream mining) and techniques for the construction and maintenance of knowledge bases. In particular, we focus on mining entities and relations from unstructured text data sources, entity disambiguation, entity linking and question answering. We conclude by highlighting general trends in knowledge mining research and identifying problems that require further research to enable more extensive use of knowledge bases.

Download Full-text

Web Services as XML Data Sources in Enterprise Information Integration

Enterprise Information Systems ◽

10.4018/978-1-61692-852-0.ch405 ◽

2011 ◽

pp. 972-985

Author(s):

Ákos Hajnal ◽

Tamás Kifor ◽

Gergely Lukácsy ◽

László Z. Varga

Keyword(s):

Web Services ◽

Web Service ◽

Information Integration ◽

Digital Libraries ◽

Relational Databases ◽

Query Language ◽

Data Sources ◽

Enterprise Information ◽

Xml Data ◽

The Web

More and more systems provide data through web service interfaces and these data have to be integrated with the legacy relational databases of the enterprise. The integration is usually done with enterprise information integration systems which provide a uniform query language to all information sources, therefore the XML data sources of Web services having a procedural access interface have to be matched with relational data sources having a database interface. In this chapter the authors provide a solution to this problem by describing the Web service wrapper component of the SINTAGMA Enterprise Information Integration system. They demonstrate Web services as XML data sources in enterprise information integration by showing how the web service wrapper component integrates XML data of Web services in the application domain of digital libraries.

Download Full-text

Web knowledge

Agency and the Semantic Web ◽

10.1093/oso/9780199292486.003.0008 ◽

2006 ◽

Author(s):

Christopher Walton

Keyword(s):

Semantic Web ◽

Knowledge Representation ◽

Knowledge Base ◽

Web Applications ◽

Semantic Network ◽

Knowledge Bases ◽

Web Standards ◽

Description Framework ◽

The Web

In the introductory chapter of this book, we discussed the means by which knowledge can be made available on the Web. That is, the representation of the knowledge in a form by which it can be automatically processed by a computer. To recap, we identified two essential steps that were deemed necessary to achieve this task: 1. We discussed the need to agree on a suitable structure for the knowledge that we wish to represent. This is achieved through the construction of a semantic network, which defines the main concepts of the knowledge, and the relationships between these concepts. We presented an example network that contained the main concepts to differentiate between kinds of cameras. Our network is a conceptualization, or an abstract view of a small part of the world. A conceptualization is defined formally in an ontology, which is in essence a vocabulary for knowledge representation. 2. We discussed the construction of a knowledge base, which is a store of knowledge about a domain in machine-processable form; essentially a database of knowledge. A knowledge base is constructed through the classification of a body of information according to an ontology. The result will be a store of facts and rules that describe the domain. Our example described the classification of different camera features to form a knowledge base. The knowledge base is expressed formally in the language of the ontology over which it is defined. In this chapter we elaborate on these two steps to show how we can define ontologies and knowledge bases specifically for the Web. This will enable us to construct Semantic Web applications that make use of this knowledge. The chapter is devoted to a detailed explanation of the syntax and pragmatics of the RDF, RDFS, and OWL Semantic Web standards. The resource description framework (RDF) is an established standard for knowledge representation on the Web. Taken together with the associated RDF Schema (RDFS) standard, we have a language for representing simple ontologies and knowledge bases on the Web.

Download Full-text

Linked Data - The Story So Far

International Journal on Semantic Web and Information Systems ◽

10.4018/jswis.2009081901 ◽

2009 ◽

Vol 5 (3) ◽

pp. 1-22 ◽

Cited By ~ 1986

Author(s):

Christian Bizer ◽

Tom Heath ◽

Tim Berners-Lee

Keyword(s):

Best Practices ◽

Research Agenda ◽

Linked Data ◽

Structured Data ◽

Data Space ◽

Web Of Data ◽

Technological Developments ◽

Technical Principles ◽

Global Data ◽

The Web

The term “Linked Data” refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions of assertions— the Web of Data. In this article, the authors present the concept and technical principles of Linked Data, and situate these within the broader context of related technological developments. They describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked Data community as it moves forward.

Download Full-text

Mapeamento de Problemas de Qualidade no Linked Data

Journal on Advances in Theoretical and Applied Informatics ◽

10.26729/jadi.v1i1.1043 ◽

2015 ◽

Vol 1 (1) ◽

pp. 38

Author(s):

Jessica Oliveira De Souza ◽

Jose Eduardo Santarem Segundo

Keyword(s):

Semantic Web ◽

User Experience ◽

Web Application ◽

Linked Data ◽

Data Sets ◽

Quality Of Data ◽

Related Quality ◽

Primary Means ◽

Basic Semantic

Since the Semantic Web was created in order to improve the current web user experience, the Linked Data is the primary means in which semantic web application is theoretically full, respecting appropriate criteria and requirements. Therefore, the quality of data and information stored on the linked data sets is essential to meet the basic semantic web objectives. Hence, this article aims to describe and present specific dimensions and their related quality issues.

Download Full-text

An Approach to Knowledge Base Completion by a Committee-Based Knowledge Graph Embedding

Applied Sciences ◽

10.3390/app10082651 ◽

2020 ◽

Vol 10 (8) ◽

pp. 2651

Author(s):

Su Jeong Choi ◽

Hyun-Je Song ◽

Seong-Bae Park

Keyword(s):

Knowledge Base ◽

Language Processing ◽

Graph Embedding ◽

Knowledge Bases ◽

Knowledge Graph ◽

Data Sets ◽

Complete Knowledge ◽

Proposed Model ◽

Ranking Task ◽

Low Dimensional

Knowledge bases such as Freebase, YAGO, DBPedia, and Nell contain a number of facts with various entities and relations. Since they store many facts, they are regarded as core resources for many natural language processing tasks. Nevertheless, they are not normally complete and have many missing facts. Such missing facts keep them from being used in diverse applications in spite of their usefulness. Therefore, it is significant to complete knowledge bases. Knowledge graph embedding is one of the promising approaches to completing a knowledge base and thus many variants of knowledge graph embedding have been proposed. It maps all entities and relations in knowledge base onto a low dimensional vector space. Then, candidate facts that are plausible in the space are determined as missing facts. However, any single knowledge graph embedding is insufficient to complete a knowledge base. As a solution to this problem, this paper defines knowledge base completion as a ranking task and proposes a committee-based knowledge graph embedding model for improving the performance of knowledge base completion. Since each knowledge graph embedding has its own idiosyncrasy, we make up a committee of various knowledge graph embeddings to reflect various perspectives. After ranking all candidate facts according to their plausibility computed by the committee, the top-k facts are chosen as missing facts. Our experimental results on two data sets show that the proposed model achieves higher performance than any single knowledge graph embedding and shows robust performances regardless of k. These results prove that the proposed model considers various perspectives in measuring the plausibility of candidate facts.

Download Full-text