Improving the Quality of Art Market Data Using Linked Open Data and Machine Learning

2017 ◽  
pp. 418-428 ◽  
Author(s):  
Dominik Filipiak ◽  
Agata Filipowska
Keyword(s):  
Machine Learning ◽  
Open Data ◽  
Art Market ◽  
Linked Open Data ◽  
Market Data

scholarly journals Evaluating the quality of linked open data in digital libraries

2020 ◽  
pp. 016555152093095
Author(s):  
Gustavo Candela ◽  
Pilar Escobar ◽  
Rafael C Carrasco ◽  
Manuel Marco-Such
Keyword(s):  
Digital Libraries ◽  
Open Data ◽  
Quality Measures ◽  
Linked Open Data ◽  
Data Sets ◽  
Design And Implementation ◽  
Bibliographic Data ◽  
Description Framework ◽  
Resource Description

Cultural heritage institutions have recently started to share their metadata as Linked Open Data (LOD) in order to disseminate and enrich them. The publication of large bibliographic data sets as LOD is a challenge that requires the design and implementation of custom methods for the transformation, management, querying and enrichment of the data. In this report, the methodology defined by previous research for the evaluation of the quality of LOD is analysed and adapted to the specific case of Resource Description Framework (RDF) triples containing standard bibliographic information. The specified quality measures are reported in the case of four highly relevant libraries.

SemQuire - Assessing the Data Quality of Linked Open Data Sources Based on DQV

2018 ◽  
pp. 163-175 ◽  
Author(s):  
André Langer ◽  
Valentin Siegert ◽  
Christoph Göpfert ◽  
Martin Gaedke
Keyword(s):  
Data Quality ◽  
Open Data ◽  
Linked Open Data ◽  
Data Sources

scholarly journals The Potential of Metadata for Linked Open Data and its Value for Users and Publishers

2012 ◽  
Vol 4 (2) ◽  
pp. 222-244 ◽  
Author(s):  
Anneke Zuiderwijk ◽  
Keith Jeffery ◽  
Marijn Janssen
Keyword(s):  
Open Data ◽  
Linked Open Data ◽  
Quality Of Data ◽  
Research Information ◽  
Public And Private ◽  
Advantages And Disadvantages ◽  
Private Organizations ◽  
Information Format ◽  
Effective Use

Public and private organizations increasingly release their data to gain benefits such as transparency and economic growth. The use of these open data can be supported and stimulated by providing considerable metadata (data about the data), including discovery, contextual and detailed metadata. In this paper we argue that metadata are key enablers for the effective use of Linked Open Data (LOD). We illustrate the potential of metadata by 1) presenting an overview of advantages and disadvantages of metadata derived from literature, 2) presenting metadata requirements for LOD architectures derived from literature, workshops and a questionnaire, 3) describing a LOD metadata architecture that meets the requirements and 4) showing examples of the application of this architecture in the ENGAGE project. The paper shows that using metadata with the appropriate metadata architecture can yield considerable benefits for LOD publication and use, including improving find ability, accessibility, storing, preservation, analysing, comparing, reproducing, finding inconsistencies, correct interpretation, visualizing, linking data, assessing and ranking the quality of data and avoiding unnecessary duplication of data. The Common European Research Information Format (CERIF) can be used to build the metadata architecture and achieve the advantages.

scholarly journals Heritage Connector: A Machine Learning Framework for Building Linked Open Data from Museum Collections

2021 ◽  
Author(s):  
Kalyan Dutia ◽  
John Stack
Keyword(s):  
Machine Learning ◽  
Named Entity Recognition ◽  
Open Data ◽  
Linked Open Data ◽  
Entity Recognition ◽  
Digital Museum ◽  
Named Entity ◽  
Learning Framework ◽  
Almost All ◽  
Small Cloud

As with almost all data, museum collection catalogues are largely unstructured, variable in consistency and overwhelmingly composed of thin records. The form of these catalogues means that the potential for new forms of research, access and scholarly enquiry that range across multiple collections and related datasets remains dormant. In the project Heritage Connector: Transforming text into data to extract meaning and make connections, we are applying a battery of digital techniques to connect similar, identical and related items within and across collections and other publications. In this paper we describe a framework to create a Linked Open Data knowledge graph (KG) from digital museum catalogues, connect entities within this graph to Wikidata, and create new connections in this graph from text. We focus on the use of machine learning to create these links at scale with a small amount of labelled data, on a mid-range laptop or a small cloud virtual machine. We publish open-source software providing tools to perform the tasks of KG creation, entity matching and named entity recognition under these constraints.

scholarly journals A Shape Expression approach for assessing the quality of Linked Open Data in libraries

Semantic Web ◽  
2021 ◽  
pp. 1-21
Author(s):  
Gustavo Candela ◽  
Pilar Escobar ◽  
María Dolores Sáez ◽  
Manuel Marco-Such
Keyword(s):  
Semantic Web ◽  
Cultural Heritage ◽  
Open Data ◽  
Linked Open Data ◽  
Use Cases ◽  
Crucial Aspect ◽  
Semantic Web Technologies ◽  
Web Technologies

Cultural heritage institutions are exploring Semantic Web technologies to publish and enrich their catalogues. Several initiatives, such as Labs, are based on the creative and innovative reuse of the materials published by cultural heritage institutions. In this way, quality has become a crucial aspect to identify and reuse a dataset for research. In this article, we propose a methodology to create Shape Expressions definitions in order to validate LOD datasets published by libraries. The methodology was then applied to four use cases based on datasets published by relevant institutions. It intends to encourage institutions to use ShEx to validate LOD datasets as well as to promote the reuse of LOD, made openly available by libraries.

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