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When Metadata is the Content From Articles to Knowledge SSP 2009 Annual Meeting Chris Beguel – Director of Sales – TEMIS Baltimore, MD – May 09

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When Metadata is the ContentFrom Articles to Knowledge

SSP  2009 Annual MeetingChris Beguel – Director of Sales – TEMISBaltimore, MD – May 09

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Copyright © 2009 TEMIS –All rights reserved 2

Where are we? Semantic Age!

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Copyright © 2009 TEMIS –All rights reserved 3

Term

Entity

Fact

Knowledge

From Words to Meaning…

Product Dosing Action Target State Event Action

Potential Adverse EffectDrug = TrimilaxDosing = 500mgSymptom = TirenessWhen = After administration

Drug Symptom Condition

Prop. Num. Abrev. Verb /3rd Pron. Adj. Prep. NounVerb

Trimilax 500 makes me feel after ingestionmg dizzy

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Copyright © 2009 TEMIS –All rights reserved 4

Metadata? Understand!

Title: Google gives drivers a handat the gas pumps

Source: InformationWeekAuthor: Antone GonsalvesDate: November 7, 2007

Metadata

Entities

Facts

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Copyright © 2009 TEMIS –All rights reserved 5

Metadata? Understand!

Linux

United States

Open­source …

Google

T­Mobile HTC

Qualcomm Motorola

Atlanta

Locations

National Association of Conveni…

Organizations

Lucy Sackett

Persons

Internet

Technologies

Gilbarco Veeder­Root

Companies

InformationWeek

Sackett

Gilbarco

Entities

Facts

Metadata

Product

New Service Google Service

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Copyright © 2009 TEMIS –All rights reserved 6

Metadata? Understand!

Launch

Gilbarco Google Service

Gilbarco New service

Announcement

Partnership

Gilbarco Google

Sackett InformationWeek

Function

Sackett Gilbarco

Alliance

Google HTC

Qualcomm

Motorola

T­Mobile

Entities

Facts

Metadata

Announcement

Who: GilbarcoWhom: unknownWhat: New ServiceWhen: unknown

Who: GilbarcoWhat: Google ServiceWhen: early next week

Launch

Who: SackettCompany: GilbarcoFunction: spoke woman

FunctionWho: GilbarcoWith whom: GoogleWhen; unknownState: Negative

Partnership

Who: GoogleWith whom: T­Mobile, HTC,Qualcom, MotorolaWhen: unknown

Alliance

Announcement

Who: SackettWhom: InformationWeekWhen: unknownWhat: unknown

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Copyright © 2009 TEMIS –All rights reserved 7

From Metadata to Knowledge!

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Copyright © 2009 TEMIS –All rights reserved 8

What is Text Mining?

v Text Mining is an information access technology…

v Text Mining generates Knowledge

v Text Mining serves information consumers & producers

Text Mining Back­End

DataRepository

Text Mining Front­End(Text Analytics)

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Copyright © 2009 TEMIS –All rights reserved 9

1. Enhanced Search Experience

Simple recognition of words…

From standard keyword search….

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Copyright © 2009 TEMIS –All rights reserved 10

•Make comprehensive and precise search•Get more relevant documents•Find what you don’t know!

1. Enhanced Search Experience

… to Entity & Fact search!

End­UserBenefits

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Copyright © 2009 TEMIS –All rights reserved 11

2. Faceted Navigation

From “narrow your search”….

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Copyright © 2009 TEMIS –All rights reserved 12

2. Faceted Navigation

•Get a quick vision of document content•Navigate within context­relevant information•Rapidly focus on targeted documents

End­UserBenefits

… to multi­dimensional faceted navigation

Self­adjustingfilters to refine the

search

Ability to combineseveral filters at once

(and/or)

Point & Clickfiltering

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Copyright © 2009 TEMIS –All rights reserved 13

From bug view ….

3. Data Analysis and Reporting

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Copyright © 2009 TEMIS –All rights reserved 14

3. Data Analysis and Reporting

… to bird­eye view!

•Visualize key Entities & Facts (pie/bar charts)•Detect Entities & Facts dependencies (matrix charts)•Zoom in & out by drilling anywhere

End­UserBenefits

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Copyright © 2009 TEMIS –All rights reserved 15

4. Information Discovery

From flat list of documents ….

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Copyright © 2009 TEMIS –All rights reserved 16

4. Information Discovery

… toinformation

network

Entities

Facts

SearchPanel

DiscoveryTools

Proofs

•Search in knowledge, not in documents•Get a graphical representation of knowledge•Discover information by navigating within Facts

End­UserBenefits

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Copyright © 2009 TEMIS –All rights reserved 17

Semantic Enrichment at the Core

ProductManagement

Web ContentManagement

Text MiningContent Enrichment

Related TopicsExtraction

SmartLinking

SentimentAnalysis

Trends Analysis& Charting

SimilarityDetection

ContentAnnotation

MetadataExtraction

TaxonomyManagement

AutomaticCategorization

Entity & FactsExtraction

Original ContentJournal Scans

Expert InterviewsEvent Reports

Visitors &customers

ContentEditors

Editorial& Content

Management

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Copyright © 2009 TEMIS –All rights reserved 18

Benefits to Information Producers

v Create more engaging, longer lasting user visits• Richer user experience with context sensitive information• Enhanced page views per visits• Exposing the “long tail” through suggestions and linking• Integrate more content at a fraction of the cost

v Establish your web properties as a communitygateway

• “70% of all searches do NOT start on Google/MSN/Yahoo”says Sue Feldman at IDC Research

• Smart search and navigation are critical to user’s experience

Increase stickiness of website to maximizead revenue or subscription utilization!

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Copyright © 2009 TEMIS –All rights reserved 19

Re­Packaging Content – Elsevier

v Objective• Develop a revolutionary database indexing the last 28 years

in chemistry patent• Provide an exceptional users’experience by using “smart

content”

v Results• ~20 Million Chemistry Patent documents• Searchable by chemical reactions, solvents, reactants directly

extracted from the documents• Released by Elsevier­MDL in Nov. 2004

v Currently• TEMIS distributes the Chemical Entities Relationships

Annotator in partnership with Elsevier

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Copyright © 2009 TEMIS –All rights reserved 20

Exposing the Long Tail – Springer

v Objective• Mapping of meaningful words and phrases

in journal articles to encyclopedia entries• Identification of related documents in a pool of over

three million journal articles

v Solution• Indexing of incoming journal articles to link journal

articles with the related encyclopedia entry• Creation of semantic fingerprint for each journal article

to allow search engine calculate degree of relationship• Integration with Springer’s search engine

v Benefits• Increased product sales by improving content linking

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Copyright © 2009 TEMIS –All rights reserved 21

Answering Burning Questions – EFL

v Objectives• Extract numerical data

from case law to enhanceinformation accessfor lawyers.

v Solution• Luxid® with custom annotators (address, activity,

compensation, age, turnover… )• Export numerical data as metadata to a search engine.

v Benefits• Productivity gain to extract and validate metadata• Allowing to treat huge amount of case law

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Questions?Thank you!

SSP  2009 Annual MeetingChris Beguel – Director of Sales – TEMISBaltimore, MD – May 09