Auto-Categorization- In-A-Box What s It All About?

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1 Canadian Metadata Forum 2005 Metadata: A Reality Check Auto-Categorization- In-A-Box What s It All About? Linda Farmer, Second Knowledge Solutions Sean Murphy, Deloitte Susan Thorne, Public Works & Government Services Canada Clark Breyman, Interwoven Second Knowledge Solutions:http:// k2s.ca 1

2 Workshop Agenda 1. Technology, Value & Issues Linda Farmer, Second Knowledge Solutions 2. Government of Canada Case Study: Effective Metadata & Content Management Sean Murphy, Deloitte Susan Thorne, Public Works & Government Services Canada 3. Auto-Categorization Under the Hood Clark Breyman, Interwoven 4. Panel Discussion & Audience Questions Second Knowledge Solutions:http:// k2s.ca 2

3 Canadian Metadata Forum, 2005 Auto-Categorization: Technology, Value & Issues Linda Farmer Second Knowledge Solutions Second Knowledge Solutions:http:// k2s.ca 3

4 Relationship to Metadata Unstructured Content Extract Concepts, Terms AUTO- CATEGORIZATION TOOL Taxonomy/ thesaurus Metadata Schema General: Lifecycle: Meta-Metadata: Technical: Educational: Rights: Relation: Annotation: Classification: Semantic Framework Second Knowledge Solutions:http:// k2s.ca 4

5 Information Processing Limits Convert text sources to useable format Read documents one at a time for summarization & classification Categorize texts within a taxonomy-like structure Perform quality control check Lack of speed Lack of consistency Volume required not achievable Time-to-market becomes difficult Second Knowledge Solutions:http:// k2s.ca 5

6 Search Technology Limits SEARCH ENGINE Crawls for keywords Ignores stopwords Puts keywords into indexing database with occurrences & locations Applies Boolean logic for searching Stems words, ignores plurals Relies primarily on keyword matching No relationships between keywords Keyhole view of content No context Largely indiscriminate retrieval of information Second Knowledge Solutions:http:// k2s.ca 6

7 What s Needed All high volume, information-dependant industries are desperate for better content management and retrieval tools Tools that organize content, provide structure and serve up relevant information Second Knowledge Solutions:http:// k2s.ca 7

8 Taxonomy & Classification Technology Taxonomies for giving semantic structure to content Auto-categorization tools Facilitate creation & maintenance of taxonomies Classify/categorize content Second Knowledge Solutions:http:// k2s.ca 8

9 Auto-Categorization Tools Lifeline for the enterprise swimming in unstructured information 1. Develop taxonomy structure 2. Classify existing collections of unstructured content 3. Apply metadata to content Second Knowledge Solutions:http:// k2s.ca 9

10 Auto-categorization Components Taxonomy Editor Workbench Automatic Metadata Extractor & Tagger Taxonomy Generation & Mapping CATEGORIZATION ENGINE Metadata Validation Web-browser/ Visualization Tool APIs for integration with other applications Portals, CM, CRM, search, SQL DBs, E-marketplace Second Knowledge Solutions:http:// k2s.ca 10

11 Categorization Engine Unstructured Networked Sources Extraction of concepts, phrases, categories for taxonomy creation CATEGORIZATION ENGINE Black Box Rule-Based Statistical Analysis Semantic & Linguistic Clustering (Concept extraction) Second Knowledge Solutions:http:// k2s.ca 11

12 Rule-Based Approach Precisely defines the criteria by which a document belongs to a specific category Matches terms in thesaurus to words in content Rules can also employ metadata values Experts organize concepts into categories using If- Then rules If word= shrub, then assign to category= bush If word= Bush and within 4 words of President, then assign to category= nil If doc. type= , then assign to category= Internal Communication Second Knowledge Solutions:http:// k2s.ca 12

13 Rule-Based Approach Upside Rules are powerful and flexible Most straightforward and user-controllable Can support complex operation & decision trees Very accurate Downside Supports classification only Rules must be carefully articulated and made as unambiguous as possible Expensive human domain experts need to write and maintain rules Best for focused and stable subject domains Second Knowledge Solutions:http:// k2s.ca 13

14 Statistical Analysis Approach Word frequency Relative placement of words, groupings Distance between words in a document Pattern analysis Co-occurrence of terms to find clumps or clusters of closely related documents Bayesian Probability Neural Networks Support Vector Machines Assigns them a category according to a training set of documents Second Knowledge Solutions:http:// k2s.ca 14

15 Statistical Analysis Approach Training Set Requirements Collect & manually create subsets of documents representative of each topic or node of the taxonomy Sample content is analyzed and taxonomy is further refined and rules of classification established Rules used to automate the analysis of new documents and their classification into the taxonomy Second Knowledge Solutions:http:// k2s.ca 15

16 Statistical Analysis Approach Upside Supports first draft compilation of taxonomy & subsequent classification of content into taxonomy Common method used for concept extraction due to computational nature and its fit with computers Downside Classification totally dependant on breadth & precision of manually defined training set Setting up and maintaining training set of documents is very time consuming & expensive Does not adapt well to changes in taxonomy Best used in tandem with linguistic processing Second Knowledge Solutions:http:// k2s.ca 16

17 Semantic & Linguistic Clustering Language dependant Documents clustered or grouped depending on meaning of words using thesauri, parts-of-speech analyzers, rule-based & probabilistic grammar, etc. Analyzes structure of sentences Morphological level Analysis of words - prefixes, suffixes, roots Lexical level Word-level analysis incl. part of speech Syntactical level Analyzes structure & relationships between words in a sentence Semantic level Determine possible meanings of a sentence Enhanced by statistical analysis. Second Knowledge Solutions:http:// k2s.ca 17

18 Semantic & Linguistic Clustering Upside Supports both taxonomy creation & classification Downside High degree of sophistication required to develop tool No training set of documents required Supports automatic summarization of documents Second Knowledge Solutions:http:// k2s.ca 18

19 Auto-CategorizationVendors Information Extraction Content Management Entrieva (Semio) Nstein Technologies Clear Forest Teragram Schemalogic Autonomy Documentum Interwoven Convera IBM/Lotus Inxight Intellisophic Verity Stellent Stratify Mohomine Second Knowledge Solutions:http:// k2s.ca 19

20 Auto-Categorization Products Interwoven MetaTagger Nstein Global Intelligent Information Management Linguistic DNA Inxight SmartDiscovery Analysis Server Teragram Categorizer Entity Extractor Convera RetrievalWare Knowledge Discovery Solution ExcaliburWeb Search Documentum Content Intelligence Services SchemaLogic SchemaServer Integrator Verity Collaborative Classifier Verity Extractor Entrieva SemioTagger Semio Skyliner Knowledge Engineering Workbench Second Knowledge Solutions:http:// k2s.ca 20

21 Which One to Choose? Condundrum..there is no universally accepted standard for evaluating the various algorithms or software configurations in regard to speed, accuracy, and scalability of taxonomy technology products. Delphi Group White Paper, 2004 Option..test the different solutions against a significant portion of your unstructured data, letting your users verify that the documents are categorized quickly and accurately and on a scale that meets your needs. Delphi Group White Paper, 2004 Second Knowledge Solutions:http:// k2s.ca 21

22 Categorization Process Training Set/Topic Rules Taxonomy/ Thesauri Categories Relationships Taxonomy Editor Unstructured Content Categorization Engine Semantic Processing Extracted Concepts Categories Applications Content Management Portals Website CRM Search engine APIs DB Metadata XML tags Second Knowledge Solutions:http:// k2s.ca 22

23 Key Features of Tools Pre-defined taxonomy templates Multiple language support Confidence ratings for assignment of a document to a particular category Search/discovery tools Entity extraction (people, places, company names, products.etc.) to automatically generate metadata Extraction of key sentences to generate text summaries/profiles Clustering/tagging on-the-fly Multiple taxonomy management Workflow management Second Knowledge Solutions:http:// k2s.ca 23

24 Value of Auto-Categorization Tools 1. Speed: Extremely large quantities of documents can be processed very quickly 2. Superior results: Generates highly accurate, highly granular categorization creating well indexed corpus of content 3. Increased scalability: Easily handles increases in users & documents without need for new products, infrastructure changes 4. Control & flexibility: Control over the way documents are categorized and ability to create multiple views into the content Second Knowledge Solutions:http:// k2s.ca 24

25 Issues of Implementation 1. When does an auto-categorizer become essential? 2. How do you evaluate the performance of an autocategorization tool? 3. What level of human involvement is desirable, required, or possible? 4. How do controlled vocabularies (CVs) contribute to performance of auto-categorizers? 5. How can categorizing tools help create CVs? Second Knowledge Solutions:http:// k2s.ca 25

26 Linda Farmer Second Knowledge Solutions Second Knowledge Solutions:http:// k2s.ca 26

27 Auto-Categorization Under the Hood Clark Breyman Director of Product Management, Interwoven Interwoven Confidential

28 Agenda Overview Basic Assumptions Under-the-Hood: Content Analysis Stages Contextual Recognition Classification (K-NN) Supporting Technologies Entity Extraction Collection Profiling Future Directions Slide 2 Interwoven Confidential

29 Basic Assumptions The Objective: A Scalable Content Architecture The Method: Drive Content Presentation, Storage and Compliance from Metadata Prerequisites: Metadata Standards: Defined Schemas and Taxonomies Supporting Automation: Minimize Manual Document Review & Metadata Assignment Slide 3 Interwoven Confidential

30 Metadata-Driven Presentation, Storage & Compliance Content Contribution from Users Content Contribution from Systems Metadata Capture User Interface Content Repository Automated Tagging Categorization, Metadata Extraction, Metadata Validation Content and Metadata Distribution Workflow Content Published to Portal/Web Server for Dynamic Content Delivery Metadata Published to Portal/Database for Dynamic Content Delivery Content and Metadata Published to Search Indexes Slide 4 Interwoven Confidential

31 Automated Tagging Process Input Content (Native Format) Output Metadata Record (XML) Original File Extracted Text MetaTagger Server Content Processor Transconverter Pre- Processor Group Field Processors Post- Processor Group Final Processor Group XML Metadata Record Additional Content Processors Slide 5 Interwoven Confidential

32 Field Processor Types Categorization Categorization by Recognition Categorize by matching words and phrases Resolves ambiguous categories with contextual clues (e.g. financial bank vs river bank) Categorization by Example Categorize using by comparison with expertly classified training documents. Metadata Validation and Mapping Combine and Standardize Metadata using Business Rules Convert Between Taxonomies Slide 6 Interwoven Confidential

33 Implementing Taxonomies Basic Information for All Categories UID (Universal Identifier) Label Language Definition Parents Child A unique code that identifies a category, enabling label and other attributes to be changed and localized as necessary The display name for a category The language of the localized category attributes. A plain-language description of the category and where it should be applied. Parent (more general) categories Child (more specific) categories Related Additional related but distinct categories that may apply. Slide 7 Interwoven Confidential

34 Adding Auto-Categorization: Contextual Recognition Auto-Categorization Information for Contextual Recognizers Input Content Alternate Terms Clue Terms Weak Test Documents Slide 8 Words and Phrases that indicate that a category applies. Words and Phrases used to resolve ambiguity. Force label and alternate terms to be considered ambiguous. Documents that should match a particular category. Parse Segment Text into Words, Identify Word Stems Recognize Count Matching Terms (Label, Alternate, Clues) Identify Candidate Categories Determine Ambiguity Resolve Eliminate Ambiguity Using Clues Threshold Eliminate Categories that Match too Few Times Output Categories Interwoven Confidential

35 Adding Auto-Categorization: Classification (K-NN) Auto-Categorization Information for Example-Based Classifiers Input Content Example Documents Test Documents Documents that define by example where a category applies. Documents that should match a particular category. Parse Segment Text into Words, Identify Word Stems Classify Compare Vocabulary with Example Documents Identify K Most Similar Documents Add Similar Document Categories Compute Score Based on Similarity Threshold Eliminate Categories that Match too Weakly Slide 9 Output Categories Interwoven Confidential

36 Supporting Technologies Entity Extraction Extract Names, Addresses and other Linguistic Patterns for content cataloging. Content Profiling Identify Similar Groups in Document Collections (Clustering) Identifying Co-Occurring Terms Summarization Generate Summaries & Key Phrases Slide 10 Interwoven Confidential

37 Implementing Entity Discovery & Extraction Word Patterns to Identify Metadata Examples: Slide 11 Essential for Discovery Applications Social Networks Related Concepts Compliance Audit Character Patterns URLs Address Part Numbers Phone Numbers Term-Type Patterns Person Names Company Names Hybrid Patterns Street Addresses Events (e.g. Merger Announcement) <extract> <pattern>/http:\/\/[a-za-z0-9\.\/]+/</pattern> <action report="true"> specifier.url </action> </extract> <extract> <pattern>firstname LASTNAME</pattern> <action report="true"> name.person </action> </extract> <extract> <pattern>/[0-2]+/ INITCAP STREET</pattern> <action report="true"> specifier.address </action> </extract> Interwoven Confidential

38 Implementation Methodology Content Architecture Requirements Analysis What is the problem? How will the metadata be used? Metadata Automation Create Skeleton Field Models Select Model Types: Summarizer, Extractor... Identify Plug-in Processor Requirements Schema Definition Identify Required Metadata Fields Separate Taxonomies into Facets Integrate with Content Sources Taxonomy Development Identify Categories Define Category Relationships Integrate with Metadata Receivers Focus on the Business Goals Taxonomies are to USE not to HAVE Keep Navigational & Descriptive Elements Separate Leverage Existing Domain Resources Refine Category Assignment Logic Sample Documents, Word Lists, Databases... Content Mining Tools Slide 12 Interwoven Confidential

39 Future Directions Easier Model Development Interactive Collection Profiling & Discovery Collection-Driven Suggestions More Powerful Hybrid Models Category Type Support in Rules Engine Single-Point Authoring for Multiple Models Better Feedback & Tuning Mechanisms Per-Category Thresholds Trainable Feature Selection Category Drift Analysis Slide 13 Interwoven Confidential

40 Copyright 2005 Interwoven, Inc. All Rights Reserved This confidential publication is the property of Interwoven, Inc. No part of this publication may be reproduced, translated into another language or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written consent of Interwoven, Inc. Some or all of the information contained herein may be protected by patent numbers: US # 6,505,212, EP / ATRA / BELG / DENM / FINL / FRAN / GBRI / GREC / IREL / ITAL / LUXE / NETH / PORT / SPAI / SWED / SWIT # , US # 6,480,944, US# 5,845,270, US #5,384,867, US #5,430,812, US #5,754,704, US #5,347,600, AUS #735365, GB #GB , US #5,845,067, US #6,675,299, US #5,835,037, AUS #632333, BEL #480941, BRAZ #PI , CAN #2,062,965, DENM / EPC / FRAN / GRBI / ITAL / LUXE / NETH / SPAI / SWED / SWIT #480941, GERM # , JAPA # , NORW #301860, US #5,065,447, US #6,609,184, US #6,141,017, US #5,990,950, US #5,821,999, US #5,805,217, US #5,838,832, US #5,867,221, US #5,923,376, US #6,434,273, US #5,867,603, US #4,941,193, US #5,822,721, US #5,845,270, US #5,923,785, US #5,982,938, US #5,790,131, US #5,721,543, US #5,982,441, US #5,857,036, GERM # or other patents pending application for Interwoven, Inc. Misappropriation of the information contained in this publication may be a violation of applicable laws. Copyright 2005 Interwoven, Inc. All rights reserved. Interwoven, TeamSite, Content Networks, DataDeploy, DeskSite, imanage, LiveSite, FileSite, MediaBin, MetaCode, MetaFinder, MetaSource, MetaTagger, OpenDeploy, OpenTransform, Primera, TeamPortal, TeamXML, TeamXpress, VisualAnnotate, WorkKnowledge, WorkDocs, WorkPortal, WorkRoute, WorkTeam, the respective taglines, logos and service marks are trademarks of Interwoven, Inc., which may be registered in certain jurisdictions. All other trademarks are owned by their respective owners. All other trademarks are owned by their respective owners. Slide 14 Interwoven Confidential

41 Effective Metadata & Content Management A Government of Canada Case Study. Metadata Forum September 27-28, 2005 Presenters: Susan Thorne, Public Works & Government Services Canada Sean Murphy, Deloitte 1 Government of Canada Metadata and Content Management Case Study 2005 Deloitte & Touche LLP

42 GoC Case Study (GoC CMS) Our Definition of CMS Content Management Solutions (CMS) are the technologies, standards, metadata, business processes and people that are required to create, manage and deliver content Content encompasses documents, structured and unstructured data and other materials generally delivered through the Internet to citizens (external users) and to internal users via Intranets and Extranets 2 Effective Metadata and Content Management 2005 Deloitte & Touche LLP

43 GoC Case Study (GoC CMS) The Problem Website1 Website2 Website3 Website4 Website5 Website6 System1 System2 System3 System4 Lots of websites, fed by different systems filled with content written by a variety of groups. 3 Effective Metadata and Content Management 2005 Deloitte & Touche LLP

44 GoC Case Study (GoC CMS) The Vision GoC CMS enables stakeholders to manage, share and publish web resources and its metadata in a standard and rational way. Content creation Web sites Web sites Collect, Create & Manage CMS Repository Deliver & Retrieve Web sites Web sites Content tagging based on standards Web sites Web sites Workflow Content approved 4 Effective Metadata and Content Management 2005 Deloitte & Touche LLP

45 GoC Case Study (GoC CMS) Background (1) Timeline GOL Gateways & Clusters blueprint to improve access to government resources Request for Proposal process begins - Gateways and Clusters Engagement Strategy - GOL CMS Prototype Delivered - Launch of GoC CMS Pilot - GOL CMS Project Closeout And Beyond Speech from the Throne - Canada site re-launch - Approval by TIMS for CMS - Deloitte awarded contract - GOL CMS Prototype Project - GoC CMS Prototype Delivered - Pilot planned 5 Effective Metadata and Content Management 2005 Deloitte & Touche LLP

46 GoC Case Study (GoC CMS) Background (2) Before GoC CMS Individual tools Need for an Enterprise Solution With GoC CMS Shared tools Multiple databases / repositories and business processes Individual administration tools Minimal sharing of information within and across departments Overlap in IM One-off investments Central repository Common & customizable set of business processes Shared tools and information across GoC GoC IM/IT standards Leveraged content Single lower cost investment Improved Operations through a Shared Solution! 6 Effective Metadata and Content Management 2005 Deloitte & Touche LLP

47 GoC Case Study (GoC CMS) The Components (1) Technology (COTS products) have been integrated to develop the GoC CMS Prototype. The key technology components include: Interwoven: Product set includes: TeamSite for content management, MetaTagger for taxonomy management and automated keyword generation, and deployment tools (Open Deploy, Data Deploy). Verity K2: Search engine that provides content searching and indexing capability across the solution that can be adjusted to support the ranking of metadata in a search result. Cognos Impromptu: Business intelligence software used to generate usage and audit reports. BMC Patrol: Server monitoring software. 7 Effective Metadata and Content Management 2005 Deloitte & Touche LLP

48 GoC Case Study (GoC CMS) The Components (2) GoC CMS enables stakeholders to manage, share and publish web resources and its metadata in a standard and rational way. It includes features for the following key process components: External Content Sources Collect & Create Migrate Import Create Add Resource Harvest Review Review Resource Manage Update Update Resource Publish Deliver Deploy Website s Central Hosting Website s External Hosting Using GoC CMS Tools Synchroniz e CMS Synch Synchronization to External CMS systems Holding Tank Working Area Staging Production Archive 8 Effective Metadata and Content Management 2005 Deloitte & Touche LLP

49 GoC Case Study (GoC CMS) The Components (3) Metadata standards are fundamental to the information management component of the CMS. Less about the technology, more about the standards which enable inter-operability across GoC and other levels of government We have moved beyond the what is metadata and why is it important phase Now we need to move beyond the phase of department-specific or application-specific metadata silos Effective enterprise service to Canadians requires interoperability between content authoring, technical systems and processes, content repositories and end-user information needs Facets of the GOC information holdings can be combined in virtual information and service views for client-centric or program-centric delivery Information portability and reusability (write-once, use multiple times processes) Connecting documents, publishing and archival systems 9 Effective Metadata and Content Management 2005 Deloitte & Touche LLP

50 GoC Case Study (GoC CMS) The Components (4) Metadata Standards and Implementation Specifications CMS Metadata Sub-Group formed in April 2005 Reports to the GOL Metadata Working Group, led by TBS (Nancy Brodie) Also acts as a sub-group of the CMS Functional Working Group Role is to define, align and manage metadata frameworks and processes in support of the enterprise GOC CMS Includes departmental and cluster representatives Objectives CMS Metadata Element Set CMS-Metadata Application Profile (MAP) Align the Element Set with the Records Management Element Set by finding opportunities for interoperability and aligning metadata names 10 Effective Metadata and Content Management 2005 Deloitte & Touche LLP

51 Metadata Element Sets and Application Profiles The Components (5) Metadata Standards and Implementation Specifications (continued) Metadata Elements Set Name (dc.title, dc.coverage.spatial, dc.subject, gcms.caption, etc) Label (Title, Subject, Caption, etc) Definition (intended scope or purpose of the metadata element or subelement) Data type The CMS Element Set will be a standard once completed Is based upon Dublin Core, with GOC CMS extensions Metadata Application Profile How the metatag value is populated and used within a CMS Single or multiple values Optional or mandatory Schemes and vocabularies Relationship to other metadata elements Purpose and constraints 11 Effective Metadata and Content Management 2005 Deloitte & Touche LLP

52 GoC Case Study (GoC CMS) The Components (6) Metadata standards and specifications development process: complex, costly and time consuming Designing your metadata for flexibility and extensibility Working together with departments to define common metadata for the CMS Departments will be able to extend the common metadata set to meet departmentspecific requirements Design for flexibility your metadata requirements will evolve Engaging your communities Stakeholder community: If it doesn t come from them, they won t use it Extended community: Share your experiences and challenges; it s unlikely that no one else has been developing solutions dealing with the same problems Keeping the end-goal in mind To what end(s) do you expect to use the metadata element (facet browse, search filter, dynamic content feeds, etc. )? Make sure everyone involved in the development process understands how the metadata element/vocabulary is intended to be used Select representative sample of content and tag it to ensure that your element/vocabulary meets the requirements 12 Effective Metadata and Content Management 2005 Deloitte & Touche LLP

53 GoC Case Study (GoC CMS) The Components (7) Metadata and Taxonomy Services Shared Metadata Services Unit as critical/hub to provide consistent metadata services as part of the central administration services offered around the CMS. - Taxonomy creation, integration and management services - Metadata quality assessment services - Metadata tagging services - Standards and guidelines development support services 13 Effective Metadata and Content Management 2005 Deloitte & Touche LLP

54 GoC Case Study (GoC CMS) Secrets to Success Avoiding Scope Creep focus project on content management solutions Crafting the Right Team community leadership, collaborative effort and lots of meetings and discussions/compromise Knowing the Products they re a toolbox not an out-of-the-box packaged solution Clearly Defining the Requirements metadata standards and taxonomy management are requirements that take time to develop, make the investment upfront Governance - it s all about the people, process and structure Evolving to a Shared Solution 14 Effective Metadata and Content Management 2005 Deloitte & Touche LLP

55 Proof-of-Concept Solution Evolution An Auto-Classification Perspective Now Proof of Concept In the Future Common standards (CLF) Few / limited classification tools Manual processes Multiple repositories Common Repository Defined and applied common standards Applied common tools (MetaTagger) Common metadata model Metadata-enabled search Some autoclassification capability Workflow Use of metadata for information lifecycle management (archival, disposition) Keyword / subject driven navigation and search 15 Effective Metadata and Content Management 2005 Deloitte & Touche LLP

56 When all else fails Bring food 16 Effective Metadata and Content Management 2005 Deloitte & Touche LLP

57 Questions? 17 Effective Metadata and Content Management 2005 Deloitte & Touche LLP

58 Contact Information Sean Murphy Deloitte (613) Susan Thorne Public Works & Government Services Canada (819) Effective Metadata and Content Management 2005 Deloitte & Touche LLP

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