Product Variants Search and Retrieval Based on the Semantically Annotated Product Family Using Multi-Facet Domain Ontology

2011 ◽  
Vol 403-408 ◽  
pp. 4114-4118
Author(s):  
Bin Dan ◽  
Shuang Peng ◽  
Xu Mei Zhang ◽  
Kai Rao
Keyword(s):  
Product Family ◽  
Digital Camera ◽  
Domain Ontology ◽  
Product Variant ◽  
Search And Retrieval

For the purpose of solving product variant search and retrieval problems for non-professional customers, a search and retrieval framework is proposed based on the semantically annotated product family using multi-facet domain ontology. The method of developing multi-faceted domain ontology is discussed, and the annotation model is constructed，then the methodology of search and retrieval is presented in detail. Finally, an example of a digital camera family is employed to illustrate the proposed approach.

Aligning Relational Schema and OWL Ontologies with Hidden Markov Model

2016 ◽  
Vol 7 (2) ◽  
pp. 1-24 ◽  
Author(s):  
Arianna Pipitone ◽  
Roberto Pirrone
Keyword(s):  
Semantic Web ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Theoretical Background ◽  
Domain Ontology ◽  
Relational Schema ◽  
Instance Matching ◽  
Technical Details ◽  
Search And Retrieval

The problem of bridging the gap between relational schema and ontologies is actively investigated in the Semantic Web and business communities. The main motivations are the OBDA scenario, where a domain ontology allows to hidden the technical details of the db to end-users; and the persistent storage of ontologies in db for facilitating search and retrieval keeping the benefits of DBMSs such as security and integrity. In these cases, the ABox is usually stored into a db, and the TBox is maintained in an ontology; for this reason, schema alignment is a more significant problem than the instance matching one. The use of manual mappings is hard and expensive, especially for large representations. This paper proposes an innovative approach for solving this problem based on a HMM; it estimates the most likely sequence of symbols describing the structures in the relational schema corresponding to the axioms in the ontology to be aligned. The theoretical background nd the models are described in detail. Finally, the system is compared to the most widespread tools in literature.

Using a Digital Camera for Documentation

2002 ◽  
Vol 12 (4) ◽  
pp. 145-146
Author(s):  
Steven C. Chang
Keyword(s):  
Digital Camera

A testing paradigm for quantifying ICC profilers

2019 ◽  
Vol 2019 (1) ◽  
pp. 80-85
Author(s):  
Pooshpanjan Roy Biswas ◽  
Alessandro Beltrami ◽  
Joan Saez Gomez
Keyword(s):  
Digital Camera ◽  
Visual Assessment ◽  
Just Noticeable Difference ◽  
Color Management ◽  
Color Gamut ◽  
Gamut Mapping ◽  
Digital File ◽  
Mapping Process ◽  
Testing Paradigm

To reproduce colors in one system which differs from another system in terms of the color gamut, it is necessary to use a color gamut mapping process. This color gamut mapping is a method to translate a specific color from a medium (screen, digital camera, scanner, digital file, etc) into another system having a difference in gamut volume. There are different rendering intent options defined by the International Color Consortium [5] to use the different reproduction goals of the user [19]. Any rendering intent used to reproduce colors, includes profile engine decisions to do it, i.e. looking for color accuracy, vivid colors or pleasing reproduction of images. Using the same decisions on different profile engines, the final visual output can look different (more than one Just Noticeable Difference[16]) depending on the profile engine used and the color algorithms that they implement. Profile performance substantially depends on the profiler engine used to create them. Different profilers provide the user with varying levels of liberty to design a profile for their color management needs and preference. The motivation of this study is to rank the performance of various market leading profiler engines on the basis of different metrics designed specifically to report the performance of particular aspects of these profiles. The study helped us take valuable decisions regarding profile performance without any visual assessment to decide on the best profiler engine.

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