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)