scholarly journals Challenges as enablers for high quality linked data: Insights from the semantic publishing challenge

2016 ◽  
Author(s):  
Anastasia Dimou ◽  
Sahar Vahdati ◽  
Angelo Di Iorio ◽  
Christoph Lange ◽  
Ruben Verborgh ◽  
...  
Keyword(s):  
Best Practices ◽  
Semantic Web ◽  
Linked Data ◽  
Lessons Learned ◽  
Data Publishing ◽  
High Quality ◽  
Scholarly Publications ◽  
Input Dataset ◽  
Definition Of ◽  
Semantic Publishing

While most challenges organized so far in the Semantic Web domain are focused on comparing tools with respect to different criteria such as their features and competencies, or exploiting semantically enriched data, the Semantic Web Evaluation Challenges series, co-located with the ESWC Semantic Web Conference, aims to compare them based on their output, namely the produced dataset. The Semantic Publishing Challenge is one of these challenges. Its goal is to involve participants in extracting data from heterogeneous sources on scholarly publications, and producing Linked Data that can be exploited by the community itself. This paper reviews lessons learned from both (i) the overall organization of the Semantic Publishing Challenge, regarding the definition of the tasks, building the input dataset and forming the evaluation, and (ii) the results produced by the participants, regarding the proposed approaches, the used tools, the preferred vocabularies and the results produced in the three editions of 2014, 2015 and 2016. We compared these lessons to other Semantic Web Evaluation challenges. In this paper, we (i) distill best practices for organizing such challenges that could be applied to similar events, and (ii) report observations on Linked Data publishing derived from the submitted solutions. We conclude that higher quality may be achieved when Linked Data is produced as a result of a challenge, because the competition becomes an incentive, while solutions become better with respect to Linked Data publishing best practices when they are evaluated against the rules of the challenge.

scholarly journals Challenges as enablers for high quality Linked Data: insights from the Semantic Publishing Challenge

2017 ◽  
Vol 3 ◽  
pp. e105 ◽  
Author(s):  
Anastasia Dimou ◽  
Sahar Vahdati ◽  
Angelo Di Iorio ◽  
Christoph Lange ◽  
Ruben Verborgh ◽  
...  
Keyword(s):  
Best Practices ◽  
Semantic Web ◽  
Linked Data ◽  
Lessons Learned ◽  
Data Publishing ◽  
High Quality ◽  
Scholarly Publications ◽  
Input Dataset ◽  
Definition Of ◽  
Semantic Publishing

While most challenges organized so far in the Semantic Web domain are focused on comparing tools with respect to different criteria such as their features and competencies, or exploiting semantically enriched data, the Semantic Web Evaluation Challenges series, co-located with the ESWC Semantic Web Conference, aims to compare them based on their output, namely the produced dataset. The Semantic Publishing Challenge is one of these challenges. Its goal is to involve participants in extracting data from heterogeneous sources on scholarly publications, and producing Linked Data that can be exploited by the community itself. This paper reviews lessons learned from both (i) the overall organization of the Semantic Publishing Challenge, regarding the definition of the tasks, building the input dataset and forming the evaluation, and (ii) the results produced by the participants, regarding the proposed approaches, the used tools, the preferred vocabularies and the results produced in the three editions of 2014, 2015 and 2016. We compared these lessons to other Semantic Web Evaluation Challenges. In this paper, we (i) distill best practices for organizing such challenges that could be applied to similar events, and (ii) report observations on Linked Data publishing derived from the submitted solutions. We conclude that higher quality may be achieved when Linked Data is produced as a result of a challenge, because the competition becomes an incentive, while solutions become better with respect to Linked Data publishing best practices when they are evaluated against the rules of the  challenge.

scholarly journals Challenges as enablers for high quality linked data: Insights from the semantic publishing challenge

2016 ◽  
Author(s):  
Anastasia Dimou ◽  
Sahar Vahdati ◽  
Angelo Di Iorio ◽  
Christoph Lange ◽  
Ruben Verborgh ◽  
...  
Keyword(s):  
Best Practices ◽  
Semantic Web ◽  
Linked Data ◽  
Lessons Learned ◽  
Data Publishing ◽  
High Quality ◽  
Scholarly Publications ◽  
Input Dataset ◽  
Definition Of ◽  
Semantic Publishing

While most challenges organized so far in the Semantic Web domain are focused on comparing tools with respect to different criteria such as their features and competencies, or exploiting semantically enriched data, the Semantic Web Evaluation Challenges series, co-located with the ESWC Semantic Web Conference, aims to compare them based on their output, namely the produced dataset. The Semantic Publishing Challenge is one of these challenges. Its goal is to involve participants in extracting data from heterogeneous sources on scholarly publications, and producing Linked Data that can be exploited by the community itself. This paper reviews lessons learned from both (i) the overall organization of the Semantic Publishing Challenge, regarding the definition of the tasks, building the input dataset and forming the evaluation, and (ii) the results produced by the participants, regarding the proposed approaches, the used tools, the preferred vocabularies and the results produced in the three editions of 2014, 2015 and 2016. We compared these lessons to other Semantic Web Evaluation challenges. In this paper, we (i) distill best practices for organizing such challenges that could be applied to similar events, and (ii) report observations on Linked Data publishing derived from the submitted solutions. We conclude that higher quality may be achieved when Linked Data is produced as a result of a challenge, because the competition becomes an incentive, while solutions become better with respect to Linked Data publishing best practices when they are evaluated against the rules of the challenge.

Publishing the Data of the Smithsonian American Art Museum to the Linked Data Cloud

2014 ◽  
Vol 8 (supplement) ◽  
pp. 152-166 ◽  
Author(s):  
Pedro Szekely ◽  
Craig A. Knoblock ◽  
Fengyu Yang ◽  
Eleanor E. Fink ◽  
Shubham Gupta ◽  
...  
Keyword(s):  
Semantic Web ◽  
Linked Data ◽  
American Art ◽  
Art Museum ◽  
High Quality ◽  
The People ◽  
Museum Data ◽  
The World ◽  
Mapping Process ◽  
High Standards

Museums around the world have built databases with metadata about millions of objects, their history, the people who created them, and the entities they represent. This data is stored in proprietary databases and is not readily available for use. Recently, museums embraced the Semantic Web as a means to make this data available to the world, but the experience so far shows that publishing museum data to the linked data cloud is difficult: the databases are large and complex, the information is richly structured and varies from museum to museum, and it is difficult to link the data to other datasets. This paper describes the process of publishing the data of the Smithsonian American Art Museum (SAAM). We describe the database-to-RDF mapping process, discuss our experience linking the SAAM dataset to hub datasets such as DBpedia and the Getty Vocabularies, and present our experience in allowing SAAM personnel to review the information to verify that it meets the high standards of the Smithsonian. Using our tools, we helped SAAM publish high-quality linked data of their complete holdings: 41,000 objects and 8,000 artists.

The Role of Ontology Engineering in Linked Data Publishing and Management

2014 ◽  
Vol 10 (3) ◽  
pp. 74-91 ◽  
Author(s):  
Markus Luczak-Rösch ◽  
Elena Simperl ◽  
Steffen Stadtmüller ◽  
Tobias Käfer
Keyword(s):  
Linked Data ◽  
Quantitative Methods ◽  
Ontology Engineering ◽  
Data Publishing ◽  
Ontology Development ◽  
Engineering Practice ◽  
Structured Interviews ◽  
Qualitative And Quantitative Methods ◽  
Definition Of

In this article the authors evaluate the adoption and applicability of established ontology engineering results by the Linked Data providers' community. The evaluation relies on a combination of qualitative and quantitative methods; in particular, the authors conducted an analytical survey containing structured interviews with data publishers in order to give an account of the current ontology engineering practice in Linked Data provisioning, and compared and expanded our findings with statistics on ontology development and usage provided by the Billion Triple Challenges datasets from 2012 (using the vocab.cc platform) and from 2014 and other related tools. The findings of the evaluation allow data practitioners and ontologists to yield a better understanding of the conceptual part of the LOD Cloud; and form the basis for the definition of purposeful, empirically grounded guidelines and best practices for developing, managing and using ontologies in the new application scenarios that arise in the context of Linked Data.

The Role of Ontology Engineering in Linked Data Publishing and Management

2018 ◽  
pp. 1255-1273
Author(s):  
Markus Luczak-Rösch ◽  
Elena Simperl ◽  
Steffen Stadtmüller ◽  
Tobias Käfer
Keyword(s):  
Linked Data ◽  
Quantitative Methods ◽  
Ontology Engineering ◽  
Data Publishing ◽  
Ontology Development ◽  
Engineering Practice ◽  
Structured Interviews ◽  
Qualitative And Quantitative Methods ◽  
Definition Of

In this article the authors evaluate the adoption and applicability of established ontology engineering results by the Linked Data providers' community. The evaluation relies on a combination of qualitative and quantitative methods; in particular, the authors conducted an analytical survey containing structured interviews with data publishers in order to give an account of the current ontology engineering practice in Linked Data provisioning, and compared and expanded our findings with statistics on ontology development and usage provided by the Billion Triple Challenges datasets from 2012 (using the vocab.cc platform) and from 2014 and other related tools. The findings of the evaluation allow data practitioners and ontologists to yield a better understanding of the conceptual part of the LOD Cloud; and form the basis for the definition of purposeful, empirically grounded guidelines and best practices for developing, managing and using ontologies in the new application scenarios that arise in the context of Linked Data.

scholarly journals Knowledge Management and Semantic Technology in the Health Care Revolution: Health 3.0 Model

2013 ◽  
Vol 680 ◽  
pp. 633-638 ◽  
Author(s):  
Te Fu Chen ◽  
Chieh Heng Ko ◽  
Fei Chun Cheng
Keyword(s):  
Health Care ◽  
Knowledge Management ◽  
Semantic Web ◽  
Linked Data ◽  
Insurance Companies ◽  
Semantic Technologies ◽  
Key Factors ◽  
Health 2.0 ◽  
Academic Researchers ◽  
Definition Of

Currently, the exploration, improvement, and application of knowledge management and semantic technologies to health care are in a revolution from Health 2.0 to Health 3.0. However, what accurately are knowledge management and semantic technologies and how can they improve a healthcare system? The study aims to review what constitute a Health 3.0 system, and identify key factors in the health care system. First, the study analyzes semantic web, definition of Health 2.0 and Health 3.0, new models for linked data: (1) semantic web and linked data graphs (2) semantic web and healthcare information challenges, OWL and linked knowledge, from linked data to linked knowledge, consistent knowledge representation, and Health 3.0 system. Secondly, the research analyzes two case studies of Health 3.0, and summarizes six key factors that constitute a Health 3.0 system. Finally, the study recommends the application of knowledge management and semantic technologies to Health 3.0 health care model requires the cooperation among emergency care, insurance companies, hospitals, pharmacies, government, specialists, academic researchers, and customer (patients).

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