Loupe - A tool for exploring and inspecting Linked Data
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http://loupe.linkeddata.es/ - Loupe is an online tool for inspecting the structure of datasets to understand the data, formulate effective queries, or detect quality issues. Starting from the high-level statistics, Loupe allows users to zoom into details down to the corresponding triples. Loupe includes 2+ billion triples from datasets including DBpedia (17 languages), Wikidata, Linked Brainz, Bio2RDF, etc.
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Thesis PDF version: https://oa.upm.es/62935/
In the era of digital transformation, where most decision-making and artificial intelligence (AI) applications are becoming data-driven, data is becoming an essential asset. Linked Data, published in structured, machine-readable formats, with explicit semantics using Semantic Web standards, and with links to other data, is even more useful. The Linked (Open) Data cloud is growing with millions of new triples each year. Nevertheless, as we discuss in this thesis, such vast amounts of data bring several new challenges in ensuring the quality of Linked Data. The main goal of this thesis is to propose novel and scalable methods for automatic quality assessment and repair of Linked Data. The motivation for it is to significantly reduce the manual effort required by current quality assessment and repair, and to propose novel methods suitable for large-scale Linked Data sources such as DBpedia or Wikidata. The main hypothesis of this work is that data profiling metrics and automatic RDF Shape induction can be used to develop scalable and automatic quality assessment and repair methods. In this context, the following main contributions are delivered in this thesis: • LDQM, a Linked Data Quality Model for representing Linked Data quality in a standard manner and LD Sniffer, a tool based on LDQM for validating accessibility of Linked Data. LDQM contains 15 quality characteristics, 89 base measures, 23 derived measures, and 124 quality indicators. • Loupe, a framework for Linked Data profiling that includes the Loupe Extended Dataset Description Model and a suite of Linked Data profiling tools. The model consists of 84 Linked Data profiling metrics useful for quality assessment and repair tasks. Loupe tools have been used to evaluate 26 thousand datasets containing 34 billions of triples and Loupe contributed to the winning system of ISWC Semantic Web Challenge 2017. The Loupe Web portal has been visited more than 40,000 times by ~3000 unique visitors from 87 countries. • An automatic RDF Shape induction method that follows a data-driven approach to induce integrity constraints using data profiling metrics as features. The proposed method achieved an F1 of 98.81% in deriving maximum cardinality constraints, an F1 of 97.30% in deriving minimum cardinality constraints, and an F1 of 95.94% in deriving range constraints. • Four methods for automatic quality assessment and repair using RDF Shapes and data profiling metrics. They are motivated by several practical use cases that cover both Linked Data generation process and output and also cover both public and enterprise data. The four methods include (a) a method for detecting inconsistent mappings, (b) a method for detecting and eliminating noisy triples produced by open information extraction tools, (c) a method to repair links in RDF data, and (d) a method to complete type information in Linked Data ...A Framework for Linked Data Quality based on Data Profiling and RDF Shape Ind...
A Framework for Linked Data Quality based on Data Profiling and RDF Shape Ind...Nandana Mihindukulasooriya
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Thesis PDF version: https://oa.upm.es/62935/
In the era of digital transformation, where most decision-making and artificial intelligence (AI) applications are becoming data-driven, data is becoming an essential asset. Linked Data, published in structured, machine-readable formats, with explicit semantics using Semantic Web standards, and with links to other data, is even more useful. The Linked (Open) Data cloud is growing with millions of new triples each year. Nevertheless, as we discuss in this thesis, such vast amounts of data bring several new challenges in ensuring the quality of Linked Data. The main goal of this thesis is to propose novel and scalable methods for automatic quality assessment and repair of Linked Data. The motivation for it is to significantly reduce the manual effort required by current quality assessment and repair, and to propose novel methods suitable for large-scale Linked Data sources such as DBpedia or Wikidata. The main hypothesis of this work is that data profiling metrics and automatic RDF Shape induction can be used to develop scalable and automatic quality assessment and repair methods. In this context, the following main contributions are delivered in this thesis: • LDQM, a Linked Data Quality Model for representing Linked Data quality in a standard manner and LD Sniffer, a tool based on LDQM for validating accessibility of Linked Data. LDQM contains 15 quality characteristics, 89 base measures, 23 derived measures, and 124 quality indicators. • Loupe, a framework for Linked Data profiling that includes the Loupe Extended Dataset Description Model and a suite of Linked Data profiling tools. The model consists of 84 Linked Data profiling metrics useful for quality assessment and repair tasks. Loupe tools have been used to evaluate 26 thousand datasets containing 34 billions of triples and Loupe contributed to the winning system of ISWC Semantic Web Challenge 2017. The Loupe Web portal has been visited more than 40,000 times by ~3000 unique visitors from 87 countries. • An automatic RDF Shape induction method that follows a data-driven approach to induce integrity constraints using data profiling metrics as features. The proposed method achieved an F1 of 98.81% in deriving maximum cardinality constraints, an F1 of 97.30% in deriving minimum cardinality constraints, and an F1 of 95.94% in deriving range constraints. • Four methods for automatic quality assessment and repair using RDF Shapes and data profiling metrics. They are motivated by several practical use cases that cover both Linked Data generation process and output and also cover both public and enterprise data. The four methods include (a) a method for detecting inconsistent mappings, (b) a method for detecting and eliminating noisy triples produced by open information extraction tools, (c) a method to repair links in RDF data, and (d) a method to complete type information in Linked Data ...A Framework for Linked Data Quality based on Data Profiling and RDF Shape Ind...
A Framework for Linked Data Quality based on Data Profiling and RDF Shape Ind...Nandana Mihindukulasooriya
The presentation for the paper "A Two-Fold Quality Assurance Approach for Dynamic Knowledge Bases : The 3cixty Use Case" presented at the 1st International Workshop on Completing and Debugging the Semantic Web at the 13th Extended Semantic Web Conference.A Two-Fold Quality Assurance Approach for Dynamic Knowledge Bases : The 3cixt...
A Two-Fold Quality Assurance Approach for Dynamic Knowledge Bases : The 3cixt...Nandana Mihindukulasooriya
DBpedia exposes data from Wikipedia as machine-readable Linked Data. The DBpedia data extraction process generates RDF data in two ways; (a) using the mappings that map the data from Wikipedia infoboxes to the DBpedia ontology and other vocabularies, and (b) using infobox-properties, i.e., properties that are not defined in the DBpedia
ontology but are auto-generated using the infobox attribute-value pairs. The work presented in this paper inspects the quality issues of the properties used in the Spanish DBpedia dataset according to conciseness, consistency, syntactic validity, and semantic accuracy quality dimensions.An analysis of the quality issues of the properties available in the Spanish ...
An analysis of the quality issues of the properties available in the Spanish ...Nandana Mihindukulasooriya
This presentation introduces LDP4j, an open source Java-based framework for the development of read-write Linked Data applications based on the W3C Linked Data Platform 1.0 (LDP) specification and available under the Apache 2.0 license. This was presented in the ISWC 2014 Developer Woskshop.
http://www.ldp4j.org/LDP4j: A framework for the development of interoperable read-write Linked Da...
LDP4j: A framework for the development of interoperable read-write Linked Da...Nandana Mihindukulasooriya
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The presentation for the paper "A Two-Fold Quality Assurance Approach for Dynamic Knowledge Bases : The 3cixty Use Case" presented at the 1st International Workshop on Completing and Debugging the Semantic Web at the 13th Extended Semantic Web Conference.A Two-Fold Quality Assurance Approach for Dynamic Knowledge Bases : The 3cixt...
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DBpedia exposes data from Wikipedia as machine-readable Linked Data. The DBpedia data extraction process generates RDF data in two ways; (a) using the mappings that map the data from Wikipedia infoboxes to the DBpedia ontology and other vocabularies, and (b) using infobox-properties, i.e., properties that are not defined in the DBpedia
ontology but are auto-generated using the infobox attribute-value pairs. The work presented in this paper inspects the quality issues of the properties used in the Spanish DBpedia dataset according to conciseness, consistency, syntactic validity, and semantic accuracy quality dimensions.An analysis of the quality issues of the properties available in the Spanish ...
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This presentation introduces LDP4j, an open source Java-based framework for the development of read-write Linked Data applications based on the W3C Linked Data Platform 1.0 (LDP) specification and available under the Apache 2.0 license. This was presented in the ISWC 2014 Developer Woskshop.
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Loupe - A tool for exploring and inspecting Linked Data
- 1. http://loupe.linkeddata.es/ Nandana Mihindukulasooriya, María Poveda-Villalón, Raúl García-Castro, and Asunción Gómez-Pérez 4V (TIN2013-46238-C4-2-R) Ontology Engineering Group Universidad Politécnica de Madrid, Madrid, Spain. Explore the vocabularies used and the abstract triple patterns in 2+ billion triples including all Dbpedia datasets, Wikidata, Linked Brainz, Bio2RDF, Linked MDB, ... Loupe helps to understand data, uncover patterns, formulate queries, and detect quality issues