DLUBM: A Benchmark for Distributed Linked Data Knowledge Base Systems

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.

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Access control requirements for environmental information knowledge base systems

Proceedings of the 5th Jerusalem Conference on Information Technology, 1990. 'Next Decade in Information Technology' ◽

10.1109/jcit.1990.128318 ◽

2002 ◽

Author(s):

G. Steinke

Keyword(s):

Access Control ◽

Knowledge Base ◽

Environmental Information ◽

Knowledge Base Systems

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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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