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Investing in the future Methods for Sharing Online Resources December 2004 ESRC Society Today The Background Theory.

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Presentation on theme: "Investing in the future Methods for Sharing Online Resources December 2004 ESRC Society Today The Background Theory."— Presentation transcript:

1 Investing in the future Methods for Sharing Online Resources December 2004 ESRC Society Today The Background Theory

2 Methods for Sharing Online Resources… How does a child recognise what a dog looks like? What exactly is content? Waving a magic wand… Putting theory into practice. Serendipity Effect ESRC World

3 Todays Content Reality… (nightmare)

4 Unstructured vs Structured 80% Unstructured 20% Structured OracleDB2 MS SQL

5 Automatic Automatic Data Agnostic Data Agnostic Language Independent Language Independent Fast Fast Scalable Scalable Accurate Accurate Dynamic & Realtime Dynamic & Realtime Includes Voice & Video Includes Voice & Video Fully XML compatible Fully XML compatible + and includes Legacy Methods = A Unique Combination of Technologies Autonomy granted single source supplier status by US Government for its unique technology. Also used by UK Government

6 Aggregate content, tag & categorize Hypertext links to similar content Personalization from forms/questionnaires geodemographic profiling Searching for information ing information to relevant recipients Reformatting for multi- channel delivery, e.g. PDF to XML Answering customer inquiries via a help desk Manual Processes Process Automation Aggregation Automatic Categorization Hyperlinking Profiling Personalization Collaboration Delivery Retrieval Routing Alerting Understanding + Automation Removes Manual Processes Integration Through Understanding Information Theory and Bayesian Inference Notes News Feeds Internet Database Files Document Management XML Audio/ Media

7 Taxonomies - Fully hierarchical and relational, dynamic, Taxonomies - Fully hierarchical and relational, dynamic, individualized, trainable & editable by example or legacy methods Requires very few documents for initial training Requires very few documents for initial training Fully dynamic views of categorized content Fully dynamic views of categorized content Manual supervision if available if required Manual supervision if available if required Example Functions… AutomaticCategorization Retrieval Hyperlinking Personalization Alerting Profiling Clustering Clustering - Ability to take information, or people and cluster them automatically into related groups Clustering - Ability to take information, or people and cluster them automatically into related groups Profiling - User profiles can be automatically matched and connected for collaboration creating centers of expertise Profiling - User profiles can be automatically matched and connected for collaboration creating centers of expertise Personalization - Fully granular automatic and individual personalization configurable by users or administrator Personalization - Fully granular automatic and individual personalization configurable by users or administrator Alerting - Accurate scalable proactive alerting. Avoiding problems of Keyword systems. Implicit or explicit Alerting - Accurate scalable proactive alerting. Avoiding problems of Keyword systems. Implicit or explicit alert subject setting Hyperlinking - Fully Automatic hyperlink generation across data types Hyperlinking - Fully Automatic hyperlink generation across data types Retrieval - Natural language, concept matching, full legacy Boolean, metadata and XML, distributed and federated, refine by example, combinational, cross-lingual, user feedback, results weighting, parametric Retrieval - Natural language, concept matching, full legacy Boolean, metadata and XML, distributed and federated, refine by example, combinational, cross-lingual, user feedback, results weighting, parametric

8 Legacy Compatibility Module - LCM Legacy Systems Legacy IndexesLegacy Indexes Legacy TopicsLegacy Topics Legacy ProfilingLegacy Profiling Legacy Collaboration SystemsLegacy Collaboration Systems IDOL Putting information into contextPutting information into context Conceptual IndexesConceptual Indexes Conceptual ProfilesConceptual Profiles Contextual CategoriesContextual Categories Collaboration & Expertise NetworksCollaboration & Expertise Networks Additional benefits of being able to integrate with a whole host of Document Management Systems and Legacy Retrieval and Collaboration Systems in order to leverage the existing user-document relationships that reside within the knowledge base. Legacy Compatibilit y Module

9 Supported Repositories… Oracle 9iOracle 9i Oracle DatabaseOracle Database Lotus NotesLotus Notes Lotus QuickplaceLotus Quickplace DocumentumDocumentum ATG DynamoATG Dynamo IntershopIntershop Exchange ServerExchange Server FileNetFileNet iManage ServeriManage Server Microsoft SQLMicrosoft SQL SybaseSybase DB2DB2 ODBC DatabasesODBC Databases Microsoft SharePointMicrosoft SharePoint OpenText LiveLinkOpenText LiveLink PCDocsPCDocs Siebel 2000Siebel 2000 VignetteVignette

10 Meta Data… All document Meta-Data supportedAll document Meta-Data supported e.g: Price, Colour, Image, Size, Author, Summary, Type, Security, Meta-tagse.g: Price, Colour, Image, Size, Author, Summary, Type, Security, Meta-tags Strings, Numbers, Dates, Bits supportedStrings, Numbers, Dates, Bits supported Conceptual search + mixed Conceptual / Meta searchConceptual search + mixed Conceptual / Meta search Full Meta-data Boolean searchFull Meta-data Boolean search Meta-data weightingMeta-data weighting Biasing / Filtering by Meta-DataBiasing / Filtering by Meta-Data Advanced Compound SortingAdvanced Compound Sorting Boolean Meta-Data CategorizationBoolean Meta-Data Categorization Powerful per document free field structurePowerful per document free field structure FULL META DATA SUPPORT

11 XML Support… Just another file format: Read XML nativelyRead XML natively Products can output XMLProducts can output XML Advanced XML field mappings / operationsAdvanced XML field mappings / operations ALL Autonomy operations available on XMLALL Autonomy operations available on XML FULL XML SUPPORT

12 Retrieval Methods… dog AND pet AND labrador 1. Legacy Methods 2. Bayesian Inference 3. Information Theory

13 Statistics Generation from The Corpus The DRE, using Bayesian Inference and Shannons Information Theory, builds Bags of statistics from a corpus of documents DRE = Dynamic Reasoning Engine

14 The DRE Identifies Key Concepts…

15

16

17 And Stores Statistics on Document…

18 To Form a Conceptual Understanding…

19 Natural Language Response… Tell me about the golden labrador...

20 Natural Language Response

21 Conceptual Relations…

22 Serendipity effect… Autonomy shines when finding interesting or unanticipated matches between texts or digital assets … important and needed to drive collaboration… or knowledge sharing activities … Forrester …"Autonomy can understand and analyse huge amounts of information….. (it can) categorise the ideas that they contain and build a sophisticated idea of what it is looking at without human help…..Autonomy has developed a program that reads, analyses and acts upon text, a breakthrough in artificial intelligence" Sunday Times

23 ESRC World… Search belittles Autonomys capability as an enabling technology for personalization, knowledge management, and collaboration - an automated Intelligent Data Operating Layer for unstructured content… AMR July 2003 Potential Content Resources SOSIG UK Data Archive Selected MIMAS targets IBSS ESRC Investment Websites 3 rd Party Materials Commissioned Content


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