applications of semantic technology in the real world today

04/11/23

2005 SEMAGIX All rights reserved.

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Applications of Semantic Technology in the Real World TodayAmit Sheth, CTO, Semagix Inc

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Things to Consider About the Semantic (Web) Technologies

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

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SchemaCreation

Ontology Population

Metadata Extraction

BSBQ Application

Creation

AnalyticApplication

Creation

Ontology API

MB KB

Ontology-driven Information System Lifecycle

Building a scalable and high performance system with support for:

Ontology creation and maintenanceOntology-driven Semantic Metadata

Extraction/AnnotationUtilizing semantic metadata and ontology

Semantic search/querying/browsing

Information and application integration - normalization

Analysis/Mining/Discovery – relationships

Semantic Technology Solves These Challenges

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Upper ontologies: modeling of time, space, process, etc

Broad-based or general purpose ontology/nomenclatures: Cyc, CIRCA ontology (Applied Semantics), SWETO, WordNet ;

Domain-specific or Industry specific ontologies

News: politics, sports, business, entertainment

Financial Market

Terrorism

PharmaO

GlycO (Glycomics); PropeO (Proteomics)

GO (nomenclature), NCI (schema), UMLS (knowledgebase), …

Application Specific and Task specific ontologies

Anti-money laundering, NeedToKnow, (Employee or Vendor Whetting)

Equity Research

Repertoire Management

Fundamentally different approaches in developing ontologies: schema vs populated; community efforts vs reusing knowledge sources

Types of Ontologies (or things close to ontology)

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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

Evolution of Meta Data

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Real-World Applications(case studies)

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Global Bank

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

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Watch list Organization

Company

Hamas

WorldCom

FBI Watchlist

Ahmed Yaseer

appears on Watchlist

member of organization

works for Company

Ahmed Yaseer:

• Appears on Watchlist ‘FBI’

• Works for Company ‘WorldCom’

• Member of organization ‘Hamas’

The Process

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Global Investment Bank

Example of Fraud Prevention application used in financial services

User will be able to navigate the ontology using a number of different interfaces

World Wide Web content

Public Records

BLOGS,RSS

Un-structure text, Semi-structured Data

Watch ListsLaw

Enforcement Regulators

Semi-structured Government Data

Scores the entity based on the content and entity relationships

EstablishingNew Account

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Law Enforcement Agency

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

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Complex querying and characteristic modelling across information sources

Application of bespoke and pre-configured ‘profiles’ for detailed investigation

Profile Creation

Complex Querying

Summary of Results

Investigation

Profile Creation

Complex Querying

Summary of Results

Investigation

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User configurable scoring profiles

Profile based on direct matching with case characteristics

Profiling based on link analysis through indirect relationships with other cases and information

Profile Creation

Complex Querying

Summary of Results

Investigation

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Free text searching across aggregated information sources

Gisondi, white ford expedition, main street, assault, traffic offences

Profile Creation

Complex Querying

Summary of Results

Investigation

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Unified view of direct and indirect results that best match the complex query and the profile

Profile Creation

Complex Querying

Summary of Results

Investigation

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Knowledge Annotation of known entities from within free text

Direct and indirect relationship scoring driven by risk weightings

Aggregated knowledge from disparate sources

Profile Creation

Complex Querying

Summary of Results

Investigation

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Scoring of key characteristics to drive relevance to original profile and query

Identification of investigation path

Visualisation of results

Profile Creation

Complex Querying

Summary of Results

Investigation

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3D navigation of relationships and knowledge around a query

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Key Characteristics of the Key Cases

Scalable end-to-end platform driven by domain specific ontologies

Expressive representation with named relationshipsPopulated ontologies with millions of instancesSophisticated entity disambiguation of knowledge extracted from multiple knowledge sourcesSelf-maintaining ontologies – updated as needed

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Key Characteristics of the Key Cases

Unified 360-degree view of entities across heterogeneous information sources

Domain specific semantic metadata extraction and enhanced annotation of heterogeneous documents and heterogeneous contentSemantic linking from internal and 3rd party content/sitesFull visibility of sources and trustworthiness

Comprehensive & high performance analytical processingRelationship linking of informationCustom scoring of relationships within information

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Help create/maintain populated ontologies that capture domain terms (do these map to classification level?)

Automatic annotation of data; possibly value added metadata enhancement (could be consistent with any metadata standard)

Providing insight into the documents (show annotated data, link concepts in documents with ontologies or context for search)

Show conceptual similarity between documents

Rule-based or pattern-based processing

Discovering links

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QUESTIONS?

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Semagix Product Architecture R

AW

DA

TA

XML

SMARTCentral

Th

in A

gile

Ap

plic

atio

ns

Model Integrate Enhance Deliver

SMART Services

SMARTWorks

Freedom

SMARTSearch

SMARTExplore

SMARTConnect

SMARTNotify

SMARTView

SMART Services

Smart Data

Ontology

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Technical Capabilities

Unified Ontology Representation Language Expressiveness

Ontology Quality and Freshness

Populated Ontology Size

Data: Type and Amount

Metadata Extraction: type

Computation: query expressiveness (over metadata and ontology), rules, ranking

Visualization