MONTAGE: CREATING SELF-POPULATING DOMAIN ONTOLOGIES FROM LINKED OPEN DATA

2013 ◽  
Vol 07 (04) ◽  
pp. 427-453 ◽  
Author(s):  
SHIMA DASTGHEIB ◽  
ARSHAM MESBAH ◽  
KRYS KOCHUT
Keyword(s):  
Cancer Treatment ◽  
Open Data ◽  
Linked Open Data ◽  
Data Sources ◽  
Data Sets ◽  
Domain Expert ◽  
Domain Specific ◽  
Knowledge Based ◽  
Ontology Design ◽  
Amount Of Knowledge

Domain-specific ontologies have become integral components of numerous semantic- and knowledge-based applications. However, creating such ontologies and populating them with correct individuals is a difficult and time-consuming process. Recently, a vast amount of knowledge has become available as part of the Linked Open Data (LOD) project, which includes data sets in multiple areas. In this paper, we present mOntage, a novel ontology design and population framework, which allows a domain expert to easily define a domain ontology schema and specify the ontology's classes and properties in terms of the subsets of the existing LOD data sources. The classes and properties of the ontology being created can be defined either directly, in terms of existing LOD-available classes and properties, or can be newly constructed by the domain expert. The definitions, called maps, are encoded as part of the ontology itself, effectively converting it into a self-populating ontology. Finally, a dedicated software system automatically populates the ontology with instances obtained from the selected LOD sources by executing suitable SPARQL queries. We illustrate our framework by creating Cancer Treatment ontology in the area of biomedicine.

scholarly journals From linked open data to molecular interaction: studying selectivity trends for ligands of the human serotonin and dopamine transporter

MedChemComm ◽  
2016 ◽  
Vol 7 (9) ◽  
pp. 1819-1831 ◽  
Author(s):  
Barbara Zdrazil ◽  
Eva Hellsberg ◽  
Michael Viereck ◽  
Gerhard F. Ecker
Keyword(s):  
Dopamine Transporter ◽  
Molecular Interaction ◽  
Open Data ◽  
Linked Open Data ◽  
Data Sources ◽  
Data Sets ◽  
Sar Data ◽  
Compound Series

Retrieval of consistent SAR data sets is a challenging task. Combining integrated open data sources with workflow tools allows studying selectivity trends of compound series.

Using linked open data to enhance the discoverability, functionality and impact of Emblematica Online

2017 ◽  
Vol 35 (1) ◽  
pp. 159-178
Author(s):  
Timothy W. Cole ◽  
Myung-Ja K. Han ◽  
Maria Janina Sarol ◽  
Monika Biel ◽  
David Maus
Keyword(s):  
Open Data ◽  
Resource Discovery ◽  
Linked Open Data ◽  
Content Type ◽  
Domain Specific ◽  
Special Collections ◽  
Online Portal ◽  
Emblem Books ◽  
Description Framework ◽  
Considerable Work

Purpose Early Modern emblem books are primary sources for scholars studying the European Renaissance. Linked Open Data (LOD) is an approach for organizing and modeling information in a data-centric manner compatible with the emerging Semantic Web. The purpose of this paper is to examine ways in which LOD methods can be applied to facilitate emblem resource discovery, better reveal the structure and connectedness of digitized emblem resources, and enhance scholar interactions with digitized emblem resources. Design/methodology/approach This research encompasses an analysis of the existing XML-based Spine (emblem-specific) metadata schema; the design of a new, domain-specific, Resource Description Framework compatible ontology; the mapping and transformation of metadata from Spine to both the new ontology and (separately) to the pre-existing Schema.org ontology; and the (experimental) modification of the Emblematica Online portal as a proof of concept to illustrate enhancements supported by LOD. Findings LOD is viable as an approach for facilitating discovery and enhancing the value to scholars of digitized emblem books; however, metadata must first be enriched with additional uniform resource identifiers and the workflow upgrades required to normalize and transform existing emblem metadata are substantial and still to be fully worked out. Practical implications The research described demonstrates the feasibility of transforming existing, special collections metadata to LOD. Although considerable work and further study will be required, preliminary findings suggest potential benefits of LOD for both users and libraries. Originality/value This research is unique in the context of emblem studies and adds to the emerging body of work examining the application of LOD best practices to library special collections.

scholarly journals Geospatial input data for the PALM model system 6.0: model requirements, data sources, and processing

2020 ◽  
Author(s):  
Wieke Heldens ◽  
Cornelia Burmeister ◽  
Farah Kanani-Sühring ◽  
Björn Maronga ◽  
Dirk Pavlik ◽  
...  
Keyword(s):  
Input Data ◽  
Open Data ◽  
Local Environment ◽  
Model System ◽  
Urban Environments ◽  
Mitigation Strategies ◽  
Data Sources ◽  
Data Sets ◽  
Simulation Scenario ◽  
German Cities

Abstract. The PALM model system 6.0 is designed to simulate micro- and mesoscale flow dynamics in realistic urban environments. The simulation results can be very valuable for various urban applications, for example to develop and improve mitigation strategies related to heat stress or air pollution. For the accurate modelling of urban environments, realistic boundary conditions need to be considered for the atmosphere, the local environment, and the soil. The local environment with its geospatial components is described in the static driver of the model and follows a standardized, hereafter called PALM input data standard. The main input parameters describe surface type, buildings and vegetation. Depending on the desired simulation scenario and the available data, the local environment can be described at different levels of detail. To compile a complete static driver describing a whole city, various data sources are used, including remote sensing, municipal data collections and open data such as OpenStreetMap. This manuscript shows how input data sets for three German cities can be derived. Based on these data sets, the static driver for PALM can be generated. As the collection and preparation of input data sets is tedious, prospective research aims at the development of a semi-automated processing chain to support users in formatting their geospatial 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.

scholarly journals Terminology and the construction of ontology

Terminology ◽  
2005 ◽  
Vol 11 (1) ◽  
pp. 55-81 ◽  
Author(s):  
Lee Gillam ◽  
Mariam Tariq ◽  
Khurshid Ahmad
Keyword(s):  
Knowledge Management ◽  
Information Retrieval ◽  
Linguistic Analysis ◽  
Statistical Techniques ◽  
Knowledge Based Systems ◽  
Arbitrary Domain ◽  
Domain Specific ◽  
Knowledge Based ◽  
Ontology Design ◽  
Conceptual Hierarchies

This paper discusses a method for corpus-driven ontology design: extracting conceptual hierarchies from arbitrary domain-specific collections of texts. These hierarchies can form the basis for a concept-oriented (onomasiological) terminology collection, and hence may be used as the basis for developing knowledge-based systems using ontology editors. This reference to ontology is explored in the context of collections of terms. The method presented is a hybrid of statistical and linguistic techniques, employing statistical techniques initially to elicit a conceptual hierarchy, which is then augmented through linguistic analysis. The result of such an extraction may be useful in information retrieval, knowledge management, or in the discipline of terminology science itself.

A Semantic Knowledge-Based Framework for Information Extraction and Exploration

2021 ◽  
Vol 13 (2) ◽  
pp. 85-109
Author(s):  
Abduladem Aljamel ◽  
Taha Osman ◽  
Dhavalkumar Thakker
Keyword(s):  
Open Data ◽  
Semantic Knowledge ◽  
Unstructured Data ◽  
Specific Information ◽  
Use Case ◽  
Binary Relations ◽  
Semantic Framework ◽  
Domain Specific ◽  
Knowledge Based ◽  
Financial Domain

The availability of online documents that describe domain-specific information provides an opportunity in employing a knowledge-based approach in extracting information from web data. This research proposes a novel comprehensive semantic knowledge-based framework that helps to transform unstructured data to be easily exploited by data scientists. The resultant sematic knowledgebase is reasoned to infer new facts and classify events that might be of importance to end users. The target use case for the framework implementation was the financial domain, which represents an important class of dynamic applications that require the modelling of non-binary relations. Such complex relations are becoming increasingly common in the era of linked open data. This research in modelling and reasoning upon such relations is a further contribution of the proposed semantic framework, where non-binary relations are semantically modelled by adapting the semantic reasoning axioms to fit the intermediate resources in the N-ary relations requirements.

Medieval Music in Linked Open Data: A Case Study on Linking Medieval Motets

2016 ◽  
Vol 10 (1) ◽  
pp. 36-50
Author(s):  
Tamsyn Rose-Steel ◽  
Ece Turnator
Keyword(s):  
Open Data ◽  
Lessons Learned ◽  
Linked Open Data ◽  
Data Sets ◽  
Complex Data ◽  
13Th Century ◽  
Medieval Studies ◽  
Digital Platforms ◽  
Concept System ◽  
Medieval Music

In Fall 2013, the Council on Library and Information Resources (CLIR), funded by the Andrew W. Mellon Foundation, engaged five postdoctoral fellows placed in five different institutions to explore issues related to data curation for medieval studies. In May 2015, these fellows convened a two-day workshop on the sharing and publishing of Linked Open Data (LOD). Funded by a CLIR/Mellon microgrant, the workshop brought together librarians, technologists, and scholars to brainstorm on the challenges posed to medievalists in sharing data on digital platforms. 2 The workshop offered a forum in which to discuss the complexity of medieval data and the challenges of sharing and publishing it. It enabled participants to appreciate LOD's potential to express complicated data sets in our area of study and aid the navigation of those data sets, as well as understand how LOD can facilitate scholars to share and publish research outcomes more effectively. In this article, we take the lessons learned from the workshop and apply them to a set of complex data: 13th-century French motets, short pieces of music usually consisting of three lines and incorporating manifold connections and references. Following an outline of LOD, a detailed explanation of the motet and the manner of its composition will set the scene for elucidating the levels of complexity to be found in motet metadata, and hence why the LOD model can aid us in negotiating the data. We will then demonstrate an effective application of LOD by proposing a proof-of-concept system for organizing a select set of motets.

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