Towards a Semantic Web application: Ontology-driven ortholog clustering analysis
Semantic (Web) Technology In Action: Ontology Driven Systems for Search, Integration and Analysis
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Transcript of Semantic (Web) Technology In Action: Ontology Driven Systems for Search, Integration and Analysis
Semantic (Web) Technology In Action: Ontology Driven Systems for
Search, Integration and Analysis
Written by: Amith Sheth & Cartic Ramakrishnan (IEEE bulletin, Dec 2003)
Presented by: Didit
Outline Introduction Semantic Web Semantic Technology Application
Nowadays Ontology Scalability Aspect in Ontologies Semiformal, Less Expensive Ontology Semagix Freedom: an Example of
Semantic technology Conclusion
Introduction
Semantic web enable the web to understand and satisfy the request of people
Considered as the next phase of web infrastructure development
How viable the web semantic is?
Outline Introduction Semantic Web Semantic Technology Application
Nowadays Ontology Scalability Aspect in Ontologies Semiformal, Less Expensive Ontology Semagix Freedom: an Example of
Semantic technology Conclusion
Semantic Web
Provide standard based interoperability of application
Semantic web vision Representing knowledge Acquiring knowledge Utilizing knowledge
Scalability is the key challenge
Semantic Web
Closely supported by web service and web process
Skepticism related to lofty goal of the semantic
web related to scalability issue
Semantic Web
Scalability issue related to Large ontology creation and
maintenance Large semantic annotation Inference mechanism
Database technology play role in web semantic Involving in complex query processing
Outline Introduction Semantic Web Semantic Technology Application
Nowadays Ontology Scalability Aspect in Ontologies Semiformal, Less Expensive Ontology Semagix Freedom: an Example of
Semantic technology Conclusion
Semantic Technology Application Nowadays Semantic search engine, Taalee (Semagic)
[Townley 2000] Provide domain specific search and contextual browsing Extracting audio, video, and text content from 250
sources Semantic integration, Equity Analyst Workbench
[sheth et al 2002] Provide text content from site aggregated from 90
international source Provide an integrated access to heterogeneous content. Content are classified into small taxonomy and domain
specific metadata are automatically extracted
Semantic Technology Application Nowadays
Analytics and knowledge discovery, Passenger Threat Assessment [sheth et al 2004] Knowledge base is populated from
many public, licensed and proprietary knowledge source
Periodic metadata extraction from heterogeneous data is performed
Semantic Technology Application Nowadays Web semantic: review from existing
application Application validate the importance of
ontology in the current approach Ontology population is critical Named entity and semantic ambiguity
resolution is important for data quality problem
Semi-formal ontologies (based on limited expressive power) are most practical and useful
Large scale metadata extraction and semantic annotation is possible
Semantic Technology Application Nowadays Web semantic: review from existing
application (continue) Support for heterogeneous data is important
factor Ability to query both ontology and metadata
to retrieve heterogeneous content is highly valuable
A majority of semantic web application development rely on ontology creation, semantic annotation, and querying
Outline Introduction Semantic Web Semantic Technology Application
Nowadays Ontology Scalability Aspect in Ontologies Semiformal, Less Expensive Ontology Semagix Freedom: an Example of
Semantic technology Conclusion
Ontology Ontologies come in
bewildering variety Informal Semiformal (do not use
formal semantic, the ontology populated in incomplete knowledge)
Formal Semiformal ontology
demonstrated very high practical value in term of development effort
Outline Introduction Semantic Web Semantic Technology Application
Nowadays Ontology Scalability Aspect in Ontologies Semiformal, Less Expensive Ontology Semagix Freedom: an Example of
Semantic technology Conclusion
Scalability Aspect in Ontologies Avalibility of large and useful ontologies
Must resemble as a database schema and capture some aspect of real world semantic
Can be done through: Social process (a suggestion and revision) Automatically extract the ontology schema
from content Automatic population of the knowledge base
(with respect to human design ontology schema)
Scalability Aspect in Ontologies
Semantic metadata extraction of massive content Annotating heterogeneous content is
very challenging
The key to face this challenge is Large scale availability of domain
specific ontologies Scalable technique to annotate content
Scalability Aspect in Ontologies
Inference mechanism that scale Should can deal with massive number
of assertion Deal with decidability and complexity
issue by limiting expressiveness of knowledge representation
Outline Introduction Semantic Web Semantic Technology Application
Nowadays Ontology Scalability Aspect in Ontologies Semiformal, Less Expensive Ontology Semagix Freedom: an Example of
Semantic technology Conclusion
Semiformal, Less Expensive Ontology Semiformal ontology more abundant
and useful than formal ontology Can be built to a scale that is useful in
real world application Can accommodate partial and possibly
incomplete information The more semantic consistency
required by standard, the sharper the tradeoff between complexity and scale
Outline Introduction Semantic Web Semantic Technology Application
Nowadays Ontology Scalability Aspect in Ontologies Semiformal, Less Expensive Ontology Semagix Freedom: an Example of
Semantic technology Conclusion
Semagix Freedom: an Example of Semantic technology
Provide modeling tool to design ontology schema based on the application requirement
Exploits task and domain ontologies that are populated with relevant facts
Perform automatic classification of content Ontology driven metadata extraction Support complex query processing (semantic
search, semantic integration, and analytic knowledge discovery)
Semagix Freedom: an Example of Semantic technology
Semagix Freedom: an Example of Semantic technology
Knowledge agent maintain ontology automatically by traverse trusted source periodically
Content agents have similar duty as knowledge agents
Semagix Freedom: an Example of Semantic technology Semantic enhancement server enhanced
incoming content identifying relevant document feature Perform entity disambiguation Tag metadata with relevant knowledge Produce semantically annotated content
Support two form content processing Automatic classification (utilize classifier committee
based on statistical, learning, and knowledge based classifier)
Metadata extraction (involve named entity identification and semantic disambiguation)
Semagix Freedom: an Example of Semantic technology
Metabase store both semantic and syntactic metadata related to content
Semantic query server provide index of metabase so that retrieval of metada can be done quickly
Semagix Freedom: an Example of Semantic technology
Performance capability Typical size of an ontology schema:
10s of classes, 10s of relationship, few hundred properties type
Average size of ontology population: over a million of named entities
Number of instance that can be extracted in a day: up to a million per server per day
Semagix Freedom: an Example of Semantic technology
Performance capability Number of text document that can be
processed for metada extraction: hundreds of thousand to a million per server per day
Performance for search engine type keyword queries: over 10 million queries per hour
Query processing requirement in an analytical application: approx 20 complex queries, taking total of 1/3 second for computation
Outline Introduction Semantic Web Semantic Technology Application
Nowadays Ontology Scalability Aspect in Ontologies Semiformal, Less Expensive Ontology Semagix Freedom: an Example of
Semantic technology Conclusion
Conclusion Formal ontologies even though supported
by deductive inference mechanism may not be the primary mean of addressing major challenge in semantic web vision
Semantic web vision is not one of solving AI problem instead it can make critical contribution to the
semantic web (on issue in supporting heterogeneous data, semantic ambiguity, complex query processing, semiformal ontology, and ability to scale large amount of information)