scholarly journals SPARQLeR: Extended Sparql for Semantic Association Discovery

2007 ◽  
pp. 145-159 ◽  
Author(s):  
Krys J. Kochut ◽  
Maciej Janik
Keyword(s):  
Semantic Association ◽  
Association Discovery

scholarly journals Semantic Association Analysis in Ontology-Based Information Retrieval

2009 ◽  
pp. 131-141
Author(s):  
Payam M. Barnaghi ◽  
Wei Wang ◽  
Jayan C. Kurian
Keyword(s):  
Semantic Web ◽  
Information Search ◽  
Data Representation ◽  
Heterogeneous Data ◽  
Ontology Development ◽  
Semantic Association ◽  
Information Search And Retrieval ◽  
Association Discovery ◽  
Meaningful Relationships ◽  
Search And Retrieval

The Semantic Web is an extension to the current Web in which information is provided in machine-processable format. It allows interoperable data representation and expression of meaningful relationships between the information resources. In other words, it is envisaged with the supremacy of deduction capabilities on the Web, that being one of the limitations of the current Web. In a Semantic Web framework, an ontology provides a knowledge sharing structure. The research on Semantic Web in the past few years has offered an opportunity for conventional information search and retrieval systems to migrate from keyword to semantics-based methods. The fundamental difference is that the Semantic Web is not a Web of interlinked documents; rather, it is a Web of relations between resources denoting real world objects, together with well-defined metadata attached to those resources. In this chapter, we first investigate various approaches towards ontology development, ontology population from heterogeneous data sources, semantic association discovery, semantic association ranking and presentation, and social network analysis, and then we present our methodology for an ontology-based information search and retrieval. In particular, we are interested in developing efficient algorithms to resolve the semantic association discovery and analysis issues.

Derived types in semantic association discovery

2009 ◽  
Vol 35 (2) ◽  
pp. 213-244 ◽  
Author(s):  
Janne Jämsen ◽  
Timo Niemi ◽  
Kalervo Järvelin
Keyword(s):  
Semantic Association ◽  
Association Discovery

Information query based on semantic association in grid environment

2009 ◽  
Vol 29 (6) ◽  
pp. 1517-1519
Author(s):  
Xiao-juan ZHANG ◽  
Hua LI
Keyword(s):  
Semantic Association ◽  
Grid Environment

EHAI: Enhanced Human Microbe-Disease Association Identification

2020 ◽  
Vol 21 (11) ◽  
pp. 1078-1084
Author(s):  
Ruizhi Fan ◽  
Chenhua Dong ◽  
Hu Song ◽  
Yixin Xu ◽  
Linsen Shi ◽  
...  
Keyword(s):  
Microbial Community ◽  
Human Health ◽  
Complex Diseases ◽  
Disease Association ◽  
Computational Results ◽  
Computational Approaches ◽  
Disease Diagnostics ◽  
Disease Associations ◽  
Association Discovery ◽  
Biological Tool

: Recently, an increasing number of biological and clinical reports have demonstrated that imbalance of microbial community has the ability to play important roles among several complex diseases concerning human health. Having a good knowledge of discovering potential of microbe-disease relationships, which provides the ability to having a better understanding of some issues, including disease pathology, further boosts disease diagnostics and prognostics, has been taken into account. Nevertheless, a few computational approaches can meet the need of huge scale of microbe-disease association discovery. In this work, we proposed the EHAI model, which is Enhanced Human microbe- disease Association Identification. EHAI employed the microbe-disease associations, and then Gaussian interaction profile kernel similarity has been utilized to enhance the basic microbe-disease association. Actually, some known microbe-disease associations and a large amount of associations are still unavailable among the datasets. The ‘super-microbe’ and ‘super-disease’ were employed to enhance the model. Computational results demonstrated that such super-classes have the ability to be helpful to the performance of EHAI. Therefore, it is anticipated that EHAI can be treated as an important biological tool in this field.

Pidgin Metadata Framework as a Mediator for Metadata Interoperability

Libri ◽  
2021 ◽  
Vol 71 (4) ◽  
pp. 375-387
Author(s):  
Seungmin Lee
Keyword(s):  
Structural Characteristics ◽  
Semantic Association ◽  
Semantic Relationships ◽  
Semantic Categories ◽  
External Function ◽  
Layer 2 ◽  
Description Framework ◽  
Internal Function ◽  
Resource Description ◽  
Metadata Interoperability

Abstract A pidgin metadata framework based on the concept of pidgin metadata is proposed to complement the limitations of existing approaches to metadata interoperability and to achieve more reliable metadata interoperability. The framework consists of three layers, with a hierarchical structure, and reflects the semantic and structural characteristics of various metadata. Layer 1 performs both an external function, serving as an anchor for semantic association between metadata elements, and an internal function, providing semantic categories that can encompass detailed elements. Layer 2 is an arbitrary layer composed of substantial elements from existing metadata and performs a function in which different metadata elements describing the same or similar aspects of information resources are associated with the semantic categories of Layer 1. Layer 3 implements the semantic relationships between Layer 1 and Layer 2 through the Resource Description Framework syntax. With this structure, the pidgin metadata framework can establish the criteria for semantic connection between different elements and fully reflect the complexity and heterogeneity among various metadata. Additionally, it is expected to provide a bibliographic environment that can achieve more reliable metadata interoperability than existing approaches by securing the communication between metadata.

Semantic Association Identification and Knowledge Discovery for National Security Applications

2005 ◽  
Vol 16 (1) ◽  
pp. 33-53 ◽  
Author(s):  
Amit Sheth ◽  
Boanerges Aleman-Meza ◽  
I. Budak Arpinar ◽  
Clemens Bertram ◽  
Yashodhan Warke ◽  
...  
Keyword(s):  
National Security ◽  
Knowledge Discovery ◽  
Semantic Association ◽  
Security Applications
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