1. Amit Sheth, CTO, Semagix Inc http://www.semagix.com
Semantics Enabled Industrial and Scientific Applications: Research, Technology and Deployed Applications Keynote - the First Online Metadata and Semantics Research Conference Part I: Industrial Applications November 23, 2005
Amit Sheth, CTO, Semagix Inc
4/5/2019
2005 SEMAGIX All rights reserved.

2. Part II: Health-care Semantic Web Application
Outline
4/5/2019
I will drive the talk with applications. In the process, we will review underlying processes, technologies and research challenges.
Part I: Industrial Semantic Technology Applications in Risk and Compliance
Part II: Health-care Semantic Web Application
Part III: Bioinformatics Semantic Web applications
Part I relates to applications developed for Semagix’s customers using a technology that commercialized research at University of Georgia’s LSDIS lab.
Many slides have notes which provide additional material and pointers to related documents/papers and talks for further information.
2004 SEMAGIX All rights reserved.

3. Things to Consider About the Semantic (Web) Technologies
4/5/2019
Build Ontology
Build Schema (model level representation
Populate with Knowledgebase (people, location, organizations, events)
Automatic Semantic Annotation (Extract Semantic Metadata)
Any type of document, multiple sources of documents
Metadata can be stored with or sparely from documents
Applications: search (ranked list of documents of interest (semantic search), integrate/portal, summarize/explain, analyze, make decisions
Reasoning techniques: graph analysis, inferencing
Types of content/documents
Use of standards
Scalability
Performance
opscenter
2004 SEMAGIX All rights reserved.

4. Ontology-driven Information System Lifecycle
Semantic (Web) Technology State of the Art
4/5/2019
Ontology-driven Information System Lifecycle
Building a scalable and high performance system with support for:
Ontology creation and maintenance
Ontology-driven Semantic Metadata Extraction/Annotation
Utilizing semantic metadata and ontology
Semantic search/querying/browsing
Information and application integration - normalization
Analysis/Mining/Discovery – relationships
Schema
Creation
Ontology API
Analytic
Application
Creation
Ontology Population MB KB
Semantic Web in a Nutshell:
- Ontology as the centerpiece
- Metadata that associate meaning to content
Computing (complex querying, inferencing, other reasoning) that support semantic applications
Further discussion on this lifecycle can be found in
BSBQ Application Creation
Metadata Extraction
2004 SEMAGIX All rights reserved.

5. Types of Ontologies (or things close to ontology)
4/5/2019
Upper ontologies: modeling of time, space, process, etc
Broad-based or general purpose ontology/nomenclatures: Cyc, WordNet ;
Domain-specific or Industry specific ontologies
News: politics, sports, business, entertainment (also see TAP and SWETO)
Financial Market
Terrorism
Biology: Open Biomedical Ontologies , GlycO; PropeO
Clinical (See Open Clinical)
GO (nomenclature), NCI (schema), UMLS (knowledgebase), …
Application Specific and Task specific ontologies
Anti-money laundering, NeedToKnow, (Employee or Vendor Whetting)
Equity Research
Repertoire Management
CENTRAL ROLE OF ONTOLOGIES
Ontology represents agreement, represents common terminology/nomenclature
Ontology is populated with extensive domain knowledge or known facts/assertions
Key enabler of semantic metadata extraction from all forms of content:
unstructured text (and 150 file formats)
semi-structured (HTML, XML) and
structured data
Ontology is in turn the center price that enables
resolution of semantic heterogeneity
semantic integration
semantically correlating/associating objects and documents
Large number of ontologies have been developed and many are in use
Fundamentally different approaches in developing ontologies: schema vs populated; community efforts vs reusing knowledge sources
2004 SEMAGIX All rights reserved.

6. More sophisticated semantic technologies exploit ontologies and
Evolution of Meta Data
4/5/2019
More sophisticated semantic technologies exploit ontologies and
Provide scalability and flexibility
Handle all types of data (unstructured, semi-structured, structured)
Create SmartData – enhancing raw data with context and relationships
Accommodate SmartQuerying – flexible, intelligent querying
Enable powerful enterprise decision making
See (Semantic Meta Data For Enterprise Information Integration)
Large scale metadata extraction and semantic annotation is possible. IBM WebFountain [Dill et al 2003] demonstrates the ability to annotate on a Web scale (i.e., over 2.5 billion pages), while Semagix Freedom related technology [Hammond et al 2002] demonstrates capabilities that work for a few million documents per day per server. However, the general trade-off of depth versus scale applies. Storage and manipulation of metadata for millions to hundreds of millions of content items requires database techniques with the challenge of improving performance and scale in presence of more complex structures
2004 SEMAGIX All rights reserved.

7. Semagix Semantic Enhancement Engine
Automatic Semantic Matadata Extraction
from unstructured data
4/5/2019
Semagix Semantic Enhancement Engine
See Hammond, Sheth, Kochut 2002: Semantic Enhancement Engine:
[Hammond, Sheth, Kochut 2002]
2004 SEMAGIX All rights reserved.

8. Semantic Annotation/ Metadata Extraction + Enhancement
4/5/2019
Semantic Annotation/ Metadata Extraction Enhancement
Large scale metadata extraction and semantic annotation is possible. IBM WebFountain [Dill et al 2003] demonstrates the ability to annotate on a Web scale (i.e., over 2.5 billion pages), while Semagix Freedom related technology [Hammond et al 2002] demonstrates capabilities that work for a few million documents per day per server. However, the general trade-off of depth versus scale applies. Storage and manipulation of metadata for millions to hundreds of millions of content items requires database techniques with the challenge of improving performance and scale in presence of more complex structures.
2004 SEMAGIX All rights reserved.

9. Automatic Semantic Annotation
4/5/2019
Limited tagging
(mostly syntactic)
COMTEX Tagging
Content
‘Enhancement’
Rich Semantic
Metatagging
Value-added Semagix Semantic Tagging
Value-added
relevant metatags
added by Semagix
to existing
COMTEX tags:
Private companies
Type of company
Industry affiliation
Sector
Exchange
Company Execs
Competitors
© Semagix, Inc.
2004 SEMAGIX All rights reserved.

10. Semagix Freedom Architecture
for building ontology-driven information system
4/5/2019
Further details of this technology can be found in Managing Semantic Content for the Web
2004 SEMAGIX All rights reserved.

11. Global Bank
4/5/2019
Aim
Legislation (PATRIOT ACT) requires banks to identify ‘who’ they are doing business with
Problem
Volume of internal and external data needed to be accessed
Complex name matching and disambiguation criteria
Requirement to ‘risk score’ certain attributes of this data
Approach
Creation of a ‘risk ontology’ populated from trusted sources (OFAC etc); Sophisticated entity disambiguation
Semantic querying, Rules specification & processing
Solution
Rapid and accurate KYC checks
Risk scoring of relationships allowing for prioritisation of results
Full visibility of sources and trustworthiness
Additional details can be found in
2004 SEMAGIX All rights reserved.

12. The Process Ahmed Yaseer: Watch list Organization Hamas FBI Watchlist
4/5/2019
Watch list
Organization
Company
Hamas
WorldCom
FBI Watchlist
Ahmed Yaseer:
Appears on Watchlist ‘FBI’
Works for Company ‘WorldCom’
Member of organization ‘Hamas’
member of organization
appears on Watchlist
Ahmed Yaseer
works for Company
2004 SEMAGIX All rights reserved.

13. Global Investment Bank
4/5/2019
Law
Enforcement
Public Records
World Wide
Web content
BLOGS, RSS
Watch Lists
Regulators
Semi-structured Government Data
Un-structure text, Semi-structured Data
User will be able to navigate
the ontology using a number
of different interfaces
Establishing
New Account
Scores the entity
based on the
content and entity
relationships
(a) Serve global population of 500 users (B) Complete all source checks in 20 seconds or less © Integrate with enterprise single sign-on systems (d) Meet complex name matching and disambiguation criteria (e) Adhere to complex security requirements
Results: Rapid, accurate KYC checks; Automatic audit trails; Reduction in in false positives; Streamlines and enhances due diligence of potential high risk accounts
Example of
Fraud Prevention application used in financial services
2004 SEMAGIX All rights reserved.

14. Law Enforcement Agency
4/5/2019
Aim
Provision of an overarching intelligence system that provides a unified view of people and related information
Problem
Need to create unique entities from across multiple disparate, non-standardised databases; Requirement to disambiguate ‘dirty’ data
Need to extract insight from unstructured text
Approach
Multiple database extractors to disambiguate data and form relevant relationships
Modelling of behaviours/patterns within very large ontology (6Mn+ entities)
Solution
Merged and linked case data from multiple sources using effective identification, disambiguation, and link analysis
Dynamic annotation of documents
Single query across multiple datasets
360 view of an individual and relevant associations
Requirements: (a) Merge and link case data from multiple sources to a taxonomy using effective identification, disambiguation, and analysis; (b) Ability to use pre-defined/investigation-specific case studies for search and match © Positive and negative searching of cases (d) Ability to explore case data starting from any entity via link analysis
Results: Superior, faster identification of prolific offenders; Better prioritization of cases; Greater investigator productivity and effectiveness
2004 SEMAGIX All rights reserved.

15. Free text searching across aggregated information sources
Profile Creation
Complex Querying
Summary of Results
Investigation
4/5/2019
Gisondi, white ford expedition, main street, assault, traffic offences
Free text searching across aggregated information sources
2004 SEMAGIX All rights reserved.

16. Profile Creation
Complex Querying
Summary of Results
Investigation
4/5/2019
Unified view of direct and indirect results that best match the complex query and the profile
2004 SEMAGIX All rights reserved.

17. Direct and indirect relationship scoring driven by risk weightings
Profile Creation
Complex Querying
Summary of Results
Investigation
4/5/2019
Direct and indirect relationship scoring driven by risk weightings
Aggregated knowledge from disparate sources
Knowledge Annotation of known entities from within free text
2004 SEMAGIX All rights reserved.

18. Technical Capabilities
4/5/2019
Ontology-driven Information Systems
Ontology Quality and Freshness
trusted knowledge sources, weekly to daily update
Populated Ontology Size
millions of assertions; sometimes exceeding 10 million
Data: Type and Amount
structured, semi-structured, unstructured
Metadata Extraction
Automatic extraction, semantic metadata;
Computation: query expressiveness (over metadata and ontology), rules, ranking
Visualization
Scalability and Performances
main-memory vs database based
2004 SEMAGIX All rights reserved.

19. QUESTIONS? http://www.semagix.com
4/5/2019
A relevant article:
A relevant conference:
2005 SEMAGIX All rights reserved.

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2004 SEMAGIX All rights reserved.

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2004 SEMAGIX All rights reserved.

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2004 SEMAGIX All rights reserved.