scholarly journals An analysis of the semantic annotation task on the linked data cloud

2019 ◽  
Vol 13 (4) ◽  
pp. 317
Author(s):  
Michel Gagnon ◽  
Amal Zouaq ◽  
Francisco Aranha ◽  
Faezeh Ensan ◽  
Ludovic Jean Louis
Keyword(s):  
Linked Data ◽  
Semantic Annotation ◽  
Annotation Task

A WIKIPEDIA-BASED FRAMEWORK FOR COLLABORATIVE SEMANTIC ANNOTATION

2011 ◽  
Vol 20 (05) ◽  
pp. 847-886 ◽  
Author(s):  
N. FERNÁNDEZ ◽  
J. A. FISTEUS ◽  
D. FUENTES ◽  
L. SÁNCHEZ ◽  
V. LUQUE
Keyword(s):  
Semantic Web ◽  
Data Processing ◽  
Web Search ◽  
Semantic Annotation ◽  
Formal Ontology ◽  
Manual Annotation ◽  
Annotation Task ◽  
Web Resources ◽  
User Actions ◽  
Common User

The semantic web aims at automating web data processing tasks that nowadays only humans are able to do. To make this vision a reality, the information on web resources should be described in a computer-meaningful way, in a process known as semantic annotation. In this paper, a manual, collaborative semantic annotation framework is described. It is designed to take advantage of the benefits of manual annotation systems (like the possibility of annotating formats difficult to annotate in an automatic manner) addressing at the same time some of their limitations (reduce the burden for non-expert annotators). The framework is inspired by two principles: use Wikipedia as a facade for a formal ontology and integrate the semantic annotation task with common user actions like web search. The tools in the framework have been implemented, and empirical results obtained in experiences carried out with these tools are reported.

Graph-based semantic annotation for enriching educational content with linked data

2014 ◽  
Vol 55 ◽  
pp. 29-42 ◽  
Author(s):  
Juan C. Vidal ◽  
Manuel Lama ◽  
Estefanía Otero-García ◽  
Alberto Bugarín
Keyword(s):  
Linked Data ◽  
Semantic Annotation ◽  
Educational Content

scholarly journals SCALEUS-FD: A FAIR Data Tool for Biomedical Applications

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Arnaldo Pereira ◽  
Rui Pedro Lopes ◽  
José Luís Oliveira
Keyword(s):  
Semantic Web ◽  
Linked Data ◽  
Biomedical Applications ◽  
Semantic Annotation ◽  
Heterogeneous Data ◽  
Full Potential ◽  
Web Tool ◽  
Data Stewardship ◽  
Data Ecosystem ◽  
Rest Api

The Semantic Web and Linked Data concepts and technologies have empowered the scientific community with solutions to take full advantage of the increasingly available distributed and heterogeneous data in distinct silos. Additionally, FAIR Data principles established guidelines for data to be Findable, Accessible, Interoperable, and Reusable, and they are gaining traction in data stewardship. However, to explore their full potential, we must be able to transform legacy solutions smoothly into the FAIR Data ecosystem. In this paper, we introduce SCALEUS-FD, a FAIR Data extension of a legacy semantic web tool successfully used for data integration and semantic annotation and enrichment. The core functionalities of the solution follow the Semantic Web and Linked Data principles, offering a FAIR REST API for machine-to-machine operations. We applied a set of metrics to evaluate its “FAIRness” and created an application scenario in the rare diseases domain.

A Query-based Approach for Semi-Automatic Annotation of Web Services

2011 ◽  
Vol 2 (2) ◽  
pp. 37-54 ◽  
Author(s):  
Mohammad Mourhaf AL Asswad ◽  
Sergio de Cesare ◽  
Mark Lycett
Keyword(s):  
Web Services ◽  
Semantic Web ◽  
Semantic Annotation ◽  
Semantic Web Services ◽  
Manual Annotation ◽  
Automatic Annotation ◽  
Query Execution ◽  
Annotation Task ◽  
Compound Nouns ◽  
Execution Engine

Semantic Web services (SWS) have attracted increasing attention due to their potential to automate discovery and composition of current syntactic Web services. An issue that prevents a wider adoption of SWS relates to the manual nature of the semantic annotation task. Manual annotation is a difficult, error-prone, and time-consuming process and automating the process is highly desirable. Though some approaches have been proposed to semi-automate the annotation task, they are difficult to use and cannot perform accurate annotation for the following reasons: (1) They require building application ontologies to represent candidate services and (2) they cannot perform accurate name-based matching when labels of candidate service elements and ontological entities contain Compound Nouns (CN). To overcome these two deficiencies, this paper proposes a query-based approach that can facilitate semi-automatic annotation of Web services. The proposed approach is easy to use because it does not require building application ontologies to represent services. Candidate service elements that need to be annotated are extracted from a WSDL file and used to generate query instances by filling a Standard Query Template. The resulting query instances are executed against a repository of ontologies using a novel query execution engine to find appropriate correspondences for candidate service elements. This query execution engine employs name-based and structural matching mechanisms that can perform effective and accurate similarity measurements between labels containing CNs. The proposed semi-automatic annotation approach is evaluated by employing it to annotate existing Web services using published domain ontologies. Precision and recall are used as evaluation metrics. The resulting precision and recall values demonstrate the effectiveness and applicability of the proposed approach.

A Query-Based Approach for Semi-Automatic Annotation of Web Services

2012 ◽  
pp. 125-143
Author(s):  
Mohammad Mourhaf AL Asswad ◽  
Sergio de Cesare ◽  
Mark Lycett
Keyword(s):  
Web Services ◽  
Semantic Web ◽  
Semantic Annotation ◽  
Semantic Web Services ◽  
Manual Annotation ◽  
Automatic Annotation ◽  
Query Execution ◽  
Annotation Task ◽  
Compound Nouns ◽  
Execution Engine

Semantic Web services (SWS) have attracted increasing attention due to their potential to automate discovery and composition of current syntactic Web services. An issue that prevents a wider adoption of SWS relates to the manual nature of the semantic annotation task. Manual annotation is a difficult, error-prone, and time-consuming process and automating the process is highly desirable. Though some approaches have been proposed to semi-automate the annotation task, they are difficult to use and cannot perform accurate annotation for the following reasons: (1) They require building application ontologies to represent candidate services and (2) they cannot perform accurate name-based matching when labels of candidate service elements and ontological entities contain Compound Nouns (CN). To overcome these two deficiencies, this paper proposes a query-based approach that can facilitate semi-automatic annotation of Web services. The proposed approach is easy to use because it does not require building application ontologies to represent services. Candidate service elements that need to be annotated are extracted from a WSDL file and used to generate query instances by filling a Standard Query Template. The resulting query instances are executed against a repository of ontologies using a novel query execution engine to find appropriate correspondences for candidate service elements. This query execution engine employs name-based and structural matching mechanisms that can perform effective and accurate similarity measurements between labels containing CNs. The proposed semi-automatic annotation approach is evaluated by employing it to annotate existing Web services using published domain ontologies. Precision and recall are used as evaluation metrics. The resulting precision and recall values demonstrate the effectiveness and applicability of the proposed approach.

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