scientific publication retrieval in linked data
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DESCRIPTION
Presentation held by Lim Ying Sean, Arun Anand Sadanandan, Dickson Lukose and Klaus Tochtermann at the Agricultural Ontology Service (AOS) Workshop 2012 in Kutching, Sarawak, Malaysia from September 3 - 4, 2012TRANSCRIPT
Lim Ying Sean1, Arun Anand Sadanandan1, Dickson Lukose1 and
Klaus Tochtermann2
Scientific Publication
Retrieval in Linked Data
1 MIMOS Bhd, Kuala Lumpur, Malaysia, 2 Leibniz Information Centre for Economics (ZBW), Kiel, Germany
Overview
• Introduction
• Linked Datasets
• Prototype Implementation
• Future Work
• Conclusion
Introduction
• Linked Data allows libraries to create and deliver library
data that is sharable, extensible and easily re-usable.
• Through rich linkages with complementary data from
trusted sources, libraries can increase the value of their
library data beyond the sum of their sources taken
individually.
Introduction
• Some examples of Linked Data available.
4
Introduction
• Our goal is to identify and retrieve related
scientific publications from different Linked
Datasets published, from a single user interface.
• Scientific publications in Linked Data consist of 3
elements:
– Metadata
– Thesauri
– Name authority file
Linked Datasets
• Agrovoc
• OpenAgris
• Standard Thesaurus Wirtschaft (STW) - a thesaurus for
economics.
• EconStor - an open access server for free publication of academic
literature in economics.
Subset of OpenAGRIS Data Model
Thesaurus
Metadata
FOAF Vocabulary
Prototype Implementation
How does our prototype work?
(OpenAgris)
(EconStor) (STW)
Prototype Screenshot
Prototype Screenshot
OpenAgris EconStor
Prototype Screenshot
Future Work
• In order to improve the quality of retrieved
publications, there are some future
research works are required:
– Measure the relevancy of the related
publications.
– Enhance user experience when searching for
related publications.
Conclusion
• We have illustrated our work in consuming linked
datasets in the area of publications, and in particular we
described the process followed to retrieve related
publications from different linked datasets.
• The approach we adopted depends on thesaurus
alignment to retrieve related publications.
Pseudocode 1 Input: User input query Output: List of publication from local dataset Procedure: 1. Identify concepts from user query, Cn={C1,C2,….,Cn}; 2. Initialize the current concept pointer i; 3. Initialize an array of concepts Wn = {}; 4. while (i<n) do 5. if Ci has skos:narrower to another concept, S then 6. load concept S into the array, Wn= {S1}; 7. else 8. load concept Ci into the array, Wn = {S1,Ci…..}; 9. end if 10. Increase the current concept pointer i; 11. end while 12. Issues a SPARQL to local dataset to identify publications that consist of
concept Wn.
Pseudocode 2 Input: List of concept Output: List of publication from Linked Data Procedure: 1. Receive an array of concept An={C1,C2,…Cn}; 2. Initialize a 2 dimensions array S[src][j]; 3. Initialize the current concept pointer i; 4. while (i<n) do 5. if Ci has skos:exactMatch to another concept, SCONCEPT then 6. Identify the source for SCONCEPT, src; 7. S[src][j] SCONCEPT; 8. end if 9. Increase the current concept pointer i; 10. end while 11. Issues a SPARQL to Linked Data S[src] to identify publication that consist of
concept Sn, Sn∈ S[src][j].