Adding Semantics to Enterprise Search Workshop

Adding Semantics to Enterprise Search Workshop
Tom Reamy Chief Knowledge Architect KAPS Group Program Chair – Text Analytics World Knowledge Architecture Professional Services

Agenda Questions / Discussions
Introduction – What is Wrong with Enterprise Search? Solution: Adding Semantics to Enterprise Search Infrastructure Solution– Taxonomy, Metadata, Information Technology Hybrid Solutions – Text Analytics Development – Taxonomy, Categorization, Faceted Metadata Search and Search-based Applications Integration with Search and ECM Platform for Information Applications Questions / Discussions

Introduction: KAPS Group
Knowledge Architecture Professional Services – Network of Consultants Applied Theory – Faceted taxonomies, complexity theory, natural categories, emotion taxonomies Services: Strategy – IM & KM - Text Analytics, Social Media, Integration Taxonomy/Text Analytics development, consulting, customization Text Analytics Quick Start – Audit, Evaluation, Pilot Social Media: Text based applications – design & development Partners – Smart Logic, Expert Systems, SAS, SAP, IBM, FAST, Concept Searching, Attensity, Clarabridge, Lexalytics Clients: Genentech, Novartis, Northwestern Mutual Life, Financial Times, Hyatt, Home Depot, Harvard Business Library, British Parliament, Battelle, Amdocs, FDA, GAO, World Bank, etc. Presentations, Articles, White Papers –

Enterprise Search Workshop: Introduction
What is Wrong with Enterprise Search? Everything! It is the wrong technology Index vs. Section Headings & summaries It is the wrong approach Technology is not the answer Need semantics, context, articulated infrastructure Leads to the Enterprise Search Dance Every 2-5 years, buy a new search engine And repeat the same mistakes 2-5 years later the complaints start again

Great Answer to the Wrong Question
Enterprise Search Workshop: Introduction: What is Wrong with Enterprise Search? The Google Solution? Great Answer to the Wrong Question Outside the enterprise Google works great Link Algorithm – most popular answer is popular Secret Sauce – 1,000’s of editors & analysis doing millions of “Best Bets” (and selling to the highest bidder – more best bets) Inside the enterprise - just another Alta Vista Link Algorithm doesn’t work Looking for THE document, not the most popular

The “Automatic” Solution? Variety of claimants
Enterprise Search Workshop: Introduction: What is Wrong with Enterprise Search? The “Automatic” Solution? Variety of claimants Autonomy et al – just point us at content and magic happens NLP, Latent Semantic Indexing, Training sets Semantic Web – trillions of triples Applications still mostly missing – how are triples structured Nothing is automatic – where resources are put – programming or library science or? One question – how well does “Find Similar” work No easy answer – Why search still is not working

The Right Answer – look beyond search Need a Different Technology:
Enterprise Search Workshop: Introduction: What is Wrong with Enterprise Search? The Right Answer – look beyond search Need a Different Technology: Semantics, language, meaning Aboutness of documents Beyond Technology: Context(s): Purpose, business function of information Self-Knowledge is the highest form of knowledge Beyond IT Library, business groups, Data wizards – predictive analytics What is new in search?

Publishing process, multiple users & info needs
Enterprise Search Workshop: Information Environment Elements of a Solution: Semantic Infrastructure Semantic Layer = Taxonomies, Metadata, Vocabularies + Text Analytics – adding cognitive science, structure to unstructured Modeling users/audiences Technology Layer Search, Content Management, SharePoint, Intranets Publishing process, multiple users & info needs SharePoint – taxonomies but Folksonomies – still a bad idea Infrastructure – Not an Application Business / Library / KM / EA – not IT Building on the Foundation Info Apps (Search-based Applications)

Enterprise Search Workshop Semantic Infrastructure: People
Communities / Tribes Different languages Different Cultures Different models of knowledge Two needs – support silos and inter-silo communication Types of Communities Formal and informal Variety of subject matters – vaccines, research, sales Variety of communication channels and information behaviors Individual People – tacit knowledge / information behaviors Consumers and Producers of information – In Depth Map major types

Enterprise Search Workshop People: Central Team
Central Team supported by software and offering services Creating, acquiring, evaluating taxonomies, metadata standards, vocabularies, categorization taxonomies Input into technology decisions and design – content management, portals, search Socializing the benefits of metadata, creating a content culture Evaluating metadata quality, facilitating author metadata Analyzing the results of using metadata, how communities are using Research metadata theory, user centric metadata Facilitate knowledge capture in projects, meetings

Enterprise Search Workshop People: Location of Team
KM/KA Dept. – Cross Organizational, Interdisciplinary Balance of dedicated and virtual, partners Library, Training, IT, HR, Corporate Communication Balance of central and distributed Industry variation Pharmaceutical – dedicated department, major place in the organization Insurance – Small central group with partners Beans – a librarian and part time functions Which design – knowledge architecture audit

Enterprise Search Workshop Resources: Technology
Text Mining Both a structure technology – taxonomy development And an application Search Based Applications Portals, collaboration, business intelligence, CRM Semantics add intelligence to individual applications Semantics add ability to communicate between applications Creation – content management, innovation, communities of practice (CoPs) When, who, how, and how much structure to add Workflow with meaning, distributed subject matter experts (SMEs) and centralized teams

Enterprise Search Workshop Business Processes
Platform for variety of information behaviors & needs Research, administration, technical support, etc. Types of content, questions Subject Matter Experts – Info Structure Amateurs Web Analytics – Feedback for maintenance & refine Enhance Basic Processes – Integrated Workflow Enhance Both Efficiency and Quality Enhance support processes – education, training Develop new processes and capabilities External Content – Text mining, smarter categorization

Enterprise Search Workshop Knowledge Structures
List of Keywords (Folksonomies) Controlled Vocabularies, Glossaries Thesaurus Browse Taxonomies (Classification) Formal Taxonomies Faceted Classifications Semantic Networks / Ontologies Categorization Taxonomies Topic Maps Knowledge Maps

Enterprise Search Workshop A Framework of Knowledge Structures
Level 1 – keywords, glossaries, acronym lists, search logs Resources, inputs into upper levels Level 2 – Thesaurus, Taxonomies Semantic Resource – foundation for applications, metadata Level 3 – Facets, Ontologies, semantic networks, topic maps, Categorization Taxonomies Applications Level 4 – Knowledge maps Strategic Resource

Enterprise Search Workshop Enterprise Taxonomies: Wrong Approach
Very difficult to develop - $100,000’s Even more difficult to apply Teams of Librarians or Authors/SME’s Cost versus Quality Problems with maintenance Cost rises in proportion with granularity Difficulty of representing user perspective Social media requires a framework – doesn’t create one Wisdom of Crowds OR Tyranny of the majority, madness of crowds

Enterprise Search Workshop Information Environment Metadata - Tagging
How do you bridge the gap – taxonomy to documents? Tagging documents with taxonomy nodes is tough And expensive – central or distributed Library staff –experts in categorization not subject matter Too limited, narrow bottleneck Often don’t understand business processes and business uses Authors – Experts in the subject matter, terrible at categorization Intra and Inter inconsistency, “intertwingleness” Choosing tags from taxonomy – complex task Folksonomy – almost as complex, wildly inconsistent Resistance – not their job, cognitively difficult = non-compliance Text Analytics is the answer(s)!

Enterprise Search Workshop: Information Environment Mind the Gap – Manual-Automatic-Hybrid
All require human effort – issue of where and how effective Manual - human effort is tagging (difficult, inconsistent) Small, high value document collections, trained taggers Automatic - human effort is prior to tagging – auto-categorization rules and/or NLP algorithm effort Hybrid Model – before (like automatic) and after Build on expertise – librarians on categorization, SME’s on subject terms Facets – Requires a lot of Metadata - Entity Extraction feeds facets – more automatic, feedback by design Manual - Hybrid – Automatic is a spectrum – depends on context

Enterprise Search Workshop Content Structures: New Approach
Simple Subject Taxonomy structure Easy to develop and maintain Combined with categorization capabilities Added power and intelligence Combined with Faceted Metadata Dynamic selection of simple categories Allow multiple user perspectives Can’t predict all the ways people think Monkey, Banana, Panda Combined with ontologies and semantic data Multiple applications – Text mining to Search Combine search and browse

Enterprise Search Workshop Benefits - Why Semantic Infrastructure
Unstructured content = 90% or more of all content Only way to get value is adding structure Only way to add useful structure is deep research into information environment What is the justification for this approach? How many new search engines do you need to buy and do the dance in another 5 years? Not as expensive or time consuming as it seems (just unfamiliar to IT)

Enterprise Search Workshop Benefits- Infrastructure vs. Projects
Strategic foundation vs. Short Term Integrated solution – CM and Search and Applications Better results Avoid duplication Semantics Small comparative cost Needed to get full value from all the above ROI – asking the wrong question What is ROI for having an HR department? What is ROI for organizing your company?

Enterprise Search Workshop Costs and Benefits
IDC study – quantify cost of bad search Three areas: Time spent searching Recreation of documents Bad decisions / poor quality work Costs 50% search time is bad search = $2,500 year per person Recreation of documents = $5,000 year per person Bad quality (harder) = $15,000 year per person Per 1,000 people = $ 22.5 million a year 30% improvement = $6.75 million a year Add own stories – especially cost of bad information Human measure - # of FTE’s, savings passed on to customers, etc.

Enterprise Search Workshop Benefits - Selling the Benefits
CTO, CFO, CEO Doesn’t understand – wrong language Semantics is extra – harder work will overcome Not business critical Not tangible – accounting bias Does not believe the numbers Believes he/she can do it Need stories and figures that will connect Need to understand their world – every case is different Need to educate them – Semantics is tough and needed

Enterprise Search Workshop Benefits of Text Analytics
Why Text Analytics? Enterprise search has failed to live up to its potential Enterprise Content management has failed to live up to its potential Taxonomy has failed to live up to its potential Adding metadata, especially keywords has not worked What is missing? Intelligence – human level categorization, conceptualization Infrastructure – Integrated solutions not technology, software Text Analytics can be the foundation that (finally) drives success – search, content management, and much more

Development

Enterprise Search Workshop Introduction: Text Analytics
History – academic research, focus on NLP Inxight –out of Zerox Parc Moved TA from academic and NLP to auto-categorization, entity extraction, and Search-Meta Data Explosion of companies – many based on Inxight extraction with some analytical-visualization front ends Half from 2008 are gone - Lucky ones got bought Focus on enterprise text analytics – shift to sentiment analysis - easier to do, obvious pay off (customers, not employees) Backlash – Real business value? Enterprise search down – 10 years of effort for what? Need Text Analytics to work Text Analytics is slowly growing – time for a jump?

Enterprise Search Workshop Current State of Text Analytics
Current Market: 2012 – exceed $1 Bil for text analytics (10% of total Analytics) Growing 20% a year Search is 33% of total market Other major areas: Sentiment and Social Media Analysis, Customer Intelligence Business Intelligence, Range of text based applications Fragmented market place – full platform, low level, specialty Embedded in content management, search, No clear leader. Big Data – Big Text is bigger, text into data, data for text Watson – ensemble methods, pun module

Enterprise Search Workshop Current State of Text Analytics: Vendor Space
Taxonomy Management – SchemaLogic, Pool Party From Taxonomy to Text Analytics Data Harmony, Multi-Tes Extraction and Analytics Linguamatics (Pharma), Temis, whole range of companies Business Intelligence – Clear Forest, Inxight Sentiment Analysis – Attensity, Lexalytics, Clarabridge Open Source – GATE Stand alone text analytics platforms – IBM, SAS, SAP, Smart Logic, Expert System, Basis, Open Text, Megaputer, Temis, Concept Searching Embedded in Content Management, Search Autonomy, FAST, Endeca, Exalead, etc.

Enterprise Search Workshop What is Text Analytics?
Text Mining – NLP, statistical, predictive, machine learning Semantic Technology – ontology, fact extraction Extraction – entities – known and unknown, concepts, events Catalogs with variants, rule based Sentiment Analysis – Objects/ Products and phrases Statistics, catalogs, rules – Positive and Negative Auto-categorization Training sets, Terms, Semantic Networks Rules: Boolean - AND, OR, NOT Advanced – DIST(#), ORDDIST#, PARAGRAPH, SENTENCE Disambiguation - Identification of objects, events, context Build rules based, not simply Bag of Individual Words

Case Study – Categorization & Sentiment

Case Study – Taxonomy Development

Enterprise Search Workshop Need for a Quick Start
Text Analytics is weird, a bit academic, and not very practical It involves language and thinking and really messy stuff On the other hand, it is really difficult to do right (Rocket Science) Organizations don’t know what text analytics is and what it is for TAW Survey shows - need two things: Strategic vision of text analytics in the enterprise Business value, problems solved, information overload Text Analytics as platform for information access Real life functioning program showing value and demonstrating an understanding of what it is and does Quick Start – Strategic Vision – Software Evaluation – POC / Pilot

Enterprise Search Workshop Text Analytics Vision & Strategy
Strategic Questions – why, what value from the text analytics, how are you going to use it Platform or Applications? What are the basic capabilities of Text Analytics? What can Text Analytics do for Search? After 10 years of failure – get search to work? What can you do with smart search based applications? RM, PII, Social ROI for effective search – difficulty of believing Problems with metadata, taxonomy

Enterprise Search Workshop Quick Start Step One- Knowledge Audit
Ideas – Content and Content Structure Map of Content – Tribal language silos Structure – articulate and integrate Taxonomic resources People – Producers & Consumers Communities, Users, Central Team Activities – Business processes and procedures Semantics, information needs and behaviors Information Governance Policy Technology CMS, Search, portals, text analytics Applications – BI, CI, Semantic Web, Text Mining

Enterprise Search Workshop Quick Start Step One- Knowledge Audit
Info Problems – what, how severe Formal Process – Knowledge Audit Contextual & Information interviews, content analysis, surveys, focus groups, ethnographic studies, Text Mining Informal for smaller organizations, specific application Category modeling – Cognitive Science – how people think Panda, Monkey, Banana Natural level categories mapped to communities, activities Novice prefer higher levels Balance of informative and distinctiveness Strategic Vision – Text Analytics and Information/Knowledge Environment

Quick Start Step Two - Software Evaluation Varieties of Taxonomy/ Text Analytics Software
Software is more important to text analytics No spreadsheets for semantics Taxonomy Management - extraction Full Platform SAS, SAP, Smart Logic, Concept Searching, Expert System, IBM, Linguamatics, GATE Embedded – Search or Content Management FAST, Autonomy, Endeca, Vivisimo, NLP, etc. Interwoven, Documentum, etc. Specialty / Ontology (other semantic) Sentiment Analysis – Attensity, Lexalytics, Clarabridge, Lots Ontology – extraction, plus ontology

Quick Start Step Two - Software Evaluation Different Kind of software evaluation
Traditional Software Evaluation - Start Filter One- Ask Experts - reputation, research – Gartner, etc. Market strength of vendor, platforms, etc. Feature scorecard – minimum, must have, filter to top 6 Filter Two – Technology Filter – match to your overall scope and capabilities – Filter not a focus Filter Three – In-Depth Demo – 3-6 vendors Reduce to 1-3 vendors Vendors have different strengths in multiple environments Millions of short, badly typed documents, Build application Library 200 page PDF, enterprise & public search

Quick Start Step Two - Software Evaluation Design of the Text Analytics Selection Team
IT - Experience with software purchases, needs assess, budget Search/Categorization is unlike other software, deeper look Business -understand business, focus on business value They can get executive sponsorship, support, and budget But don’t understand information behavior, semantic focus Library, KM - Understand information structure Experts in search experience and categorization But don’t understand business or technology Interdisciplinary Team, headed by Information Professionals Much more likely to make a good decision Create the foundation for implementation

Quick Start Step Three – Proof of Concept / Pilot Project
POC use cases – basic features needed for initial projects Design - Real life scenarios, categorization with your content Preparation: Preliminary analysis of content and users information needs Training & test sets of content, search terms & scenarios Train taxonomist(s) on software(s) Develop taxonomy if none available Four week POC – 2 rounds of develop, test, refine / Not OOB Need SME’s as test evaluators – also to do an initial categorization of content Majority of time is on auto-categorization

Enterprise Search Workshop POC Design: Evaluation Criteria & Issues
Basic Test Design – categorize test set Score – by file name, human testers Categorization & Sentiment – Accuracy 80-90% Effort Level per accuracy level Combination of scores and report Operators (DIST, etc.) , relevancy scores, markup Development Environment – Usability, Integration Issues: Quality of content & initial human categorization Normalize among different test evaluators Quality of taxonomy – structure, overlapping categories

Quick Start for Text Analytics Proof of Concept -- Value of POC
Selection of best product(s) Identification and development of infrastructure elements – taxonomies, metadata – standards and publishing process Training by doing –SME’s learning categorization, Library/taxonomist learning business language Understand effort level for categorization, application Test suitability of existing taxonomies for range of applications Explore application issues – example – how accurate does categorization need to be for that application – 80-90% Develop resources – categorization taxonomies, entity extraction catalogs/rules

Enterprise Search Workshop POC and Early Development: Risks and Issues
CTO Problem –This is not a regular software process Semantics is messy not just complex 30% accuracy isn’t 30% done – could be 90% Variability of human categorization Categorization is iterative, not “the program works” Need realistic budget and flexible project plan Anyone can do categorization Librarians often overdo, SME’s often get lost (keywords) Meta-language issues – understanding the results Need to educate IT and business in their language

Analysis of taxonomy – suitable for categorization
Text Analytics Development: Categorization Process Start with Taxonomy and Content Starter Taxonomy If no taxonomy, develop (steal) initial high level Textbooks, glossaries, Intranet structure Organization Structure – facets, not taxonomy Analysis of taxonomy – suitable for categorization Structure – not too flat, not too large Orthogonal categories Content Selection Map of all anticipated content Selection of training sets – if possible Automated selection of training sets – taxonomy nodes as first categorization rules – apply and get content

Enterprise Search Workshop Text Analytics Development: Categorization Process
First Round of Categorization Rules Term building – from content – basic set of terms that appear often / important to content Add terms to rule, apply to broader set of content Repeat for more terms – get recall-precision “scores” Repeat, refine, repeat, refine, repeat Get SME feedback – formal process – scoring Get SME feedback – human judgments Test against more, new content Repeat until “done” – 90%?

Enterprise Search Workshop Text Analytics Development: Entity Extraction Process
Facet Design – from Knowledge Audit, K Map Find and Convert catalogs: Organization – internal resources People – corporate yellow pages, HR Include variants Scripts to convert catalogs – programming resource Build initial rules – follow categorization process Differences – scale, threshold – application dependent Recall – Precision – balance set by application Issue – disambiguation – Ford company, person, car

Enterprise Search Workshop Case Study - Background
Inxight Smart Discovery Multiple Taxonomies Healthcare – first target Travel, Media, Education, Business, Consumer Goods, Content – 800+ Internet news sources 5,000 stories a day Application – Newsletters Editors using categorized results Easier than full automation

Enterprise Search Workshop Case Study - Approach
Initial High Level Taxonomy Auto generation – very strange – not usable Editors High Level – sections of newsletters Editors & Taxonomy Pro’s - Broad categories & refine Develop Categorization Rules Multiple Test collections Good stories, bad stories – close misses - terms Recall and Precision Cycles Refine and test – taxonomists – many rounds Review – editors – 2-3 rounds Repeat – about 4 weeks

Enterprise Search Workshop Case Study – Issues & Lessons
Taxonomy Structure: Aggregate vs. independent nodes Children Nodes – subset – rare Trade-off of depth of taxonomy and complexity of rules No best answer – taxonomy structure, format of rules Need custom development Recall more important than precision – editors role Combination of SME and Taxonomy pros Combination of Features – Entity extraction, terms, Boolean, filters, facts Training sets and find similar are weakest Plan for ongoing refinement

Enterprise Search Workshop Enterprise Environment – Case Studies
A Tale of Two Taxonomies It was the best of times, it was the worst of times Basic Approach Initial meetings – project planning High level K map – content, people, technology Contextual and Information Interviews Content Analysis Draft Taxonomy – validation interviews, refine Integration and Governance Plans

Enterprise Search Workshop Enterprise Environment – Case One – Taxonomy, 7 facets
Taxonomy of Subjects / Disciplines: Science > Marine Science > Marine microbiology > Marine toxins Facets: Organization > Division > Group Clients > Federal > EPA Facilities > Division > Location > Building X Content Type – Knowledge Asset > Proposals Instruments > Environmental Testing > Ocean Analysis > Vehicle Methods > Social > Population Study Materials > Compounds > Chemicals

Enterprise Search Workshop Enterprise Environment – Case One – Taxonomy, 7 facets
Project Owner – KM department – included RM, business process Involvement of library - critical Realistic budget, flexible project plan Successful interviews – build on context Overall information strategy – where taxonomy fits Good Draft taxonomy and extended refinement Software, process, team – train library staff Good selection and number of facets Developed broad categorization and one deep-Chemistry Final plans and hand off to client

Enterprise Search Workshop Enterprise Environment – Case Two – Taxonomy, 4 facets
Taxonomy of Subjects / Disciplines: Geology > Petrology Facets: Organization > Division > Group Process > Drill a Well > File Test Plan Assets > Platforms > Platform A Content Type > Communication > Presentations

Enterprise Environment – Case Two – Taxonomy, 4 facets Environment & Project Issues
Value of taxonomy understood, but not the complexity and scope Under budget, under staffed Location – not KM – tied to RM and software Solution looking for the right problem Importance of an internal library staff Difficulty of merging internal expertise and taxonomy Project mind set – not infrastructure Rushing to meet deadlines doesn’t work with semantics Importance of integration – with team, company Project plan more important than results

Enterprise Environment – Case Two – Taxonomy, 4 facets Research and Design Issues
Research Issues Not enough research – and wrong people Misunderstanding of research – wanted tinker toy connections Interview 1 leads to taxonomy node 2 Design Issues Not enough facets Wrong set of facets – business not information Ill-defined facets – too complex internal structure

Enterprise Environment – Case Two – Taxonomy, 4 facets Conclusion: Risk Factors
Political-Cultural-Semantic Environment Not simple resistance - more subtle – re-interpretation of specific conclusions and sequence of conclusions / Relative importance of specific recommendations Access to content and people Enthusiastic access Importance of a unified project team Working communication as well as weekly meetings

Applications

Enterprise Search Workshop Building on the Foundation
Text Analytics: Create the Platform – CM & Search New Electronic Publishing Process Use text analytics to tag, new hybrid workflow New Enterprise Search Build faceted navigation on metadata, extraction Enhance Information Access in the Enterprise - InfoApps Governance, Records Management, Doc duplication, Compliance Applications – Business Intelligence, CI, Behavior Prediction eDiscovery, litigation support, Fraud detection Productivity / Portals – spider and categorize, extract

Enterprise Search Workshop Information Platform: Content Management
Hybrid Model – Internal Content Management Publish Document -> Text Analytics analysis -> suggestions for categorization, entities, metadata - > present to author Cognitive task is simple -> react to a suggestion instead of select from head or a complex taxonomy Feedback – if author overrides -> suggestion for new category External Information - human effort is prior to tagging More automated, human input as specialized process – periodic evaluations Precision usually more important Target usually more general

Text Analytics and Search Multi-dimensional and Smart
Faceted Navigation has become the basic/ norm Facets require huge amounts of metadata Entity / noun phrase extraction is fundamental Automated with disambiguation (through categorization) Taxonomy – two roles – subject/topics and facet structure Complex facets and faceted taxonomies Clusters and Tag Clouds – discovery & exploration Auto-categorization – aboutness, subject facets This is still fundamental to search experience InfoApps only as good as fundamentals of search People – tagging, evaluating tags, fine tune rules and taxonomy

Integrated Facet Application Design Issues - General
What is the right combination of elements? Dominant dimension or equal facets Browse topics and filter by facet, search box How many facets do you need? Scale requires more automated solutions More sophisticated rules Issue of disambiguation: Same person, different name – Henry Ford, Mr. Ford, Henry X. Ford Same word, different entity – Ford and Ford Number of entities and thresholds per results set / document Usability, audience needs Relevance Ranking – number of entities, rank of facets

Enterprise Search Workshop Thinking Fast and Slow – Daniel Kahneman
System 1 and System 2 – Daniel Kahneman System 1 – fast and automatic – little conscious control Represents categories as prototypes – stereotypes Norms for immediate detection of anomalies – distinguish the surprising from the normal fast detection of simple differences, detect hostility in a voice, find best chess move (if a master) Priming / Anchoring – susceptible to systemic errors Temperature Example Biased to believe and confirm Focuses on existing evidence (ignores missing – WYSIATI) .

Enterprise Search Workshop Thinking Fast and Slow
System 2 – Complex, effortful judgments and calculations System 2 is the only one that can follow rules, compare objects on several attributes, and make deliberate choices Understand complex sentences Check the validity of a complex logical argument Focus attention – can make people blind to all else – Invisible Gorilla Similar to traditional dichotomies – Tacit – Explicit, etc Basic Design – System 1 is basic to most experiences, and System 2 takes over when things get difficult – conscious control Text Analysis and Text Mining / Auto-Cat and TA Cat

Enterprise Search Workshop System 1 & 2 – and Text Analytics Approaches
“Automatic Categorization” – System 1 prototypes Limited value -- only works in simple environments Shallow categories with large differences Not open to conscious control System 2 – categories – complex, minute differences, deep categories Together: Choose one or other for some contexts Combine both – need to develop new kinds of categories and/or new ways to combine?

Enterprise Search Workshop Text Mining and Text Analytics
Text Analytics and Big Data enrich each other Data tells you what people did, TA tells you why Text Analytics – pre-processing for TM Discover additional structure in unstructured text Behavior Prediction – adding depth in individual documents New variables for Predictive Analytics, Social Media Analytics New dimensions – 90% of information, 50% using Twitter analysis Text Mining for TA– Semi-automated taxonomy development Apply data methods, predictive analytics to unstructured text New Models – Watson ensemble methods, reasoning apps Extraction – smarter extraction – sections of documents, Boolean, advanced rules – drug names, adverse events – major mention

Enterprise Search Workshop Integration of Text and Data Analytics
Expertise Location: Case Study: Data and Text Data Sources: HR Information: Geography, Title-Grade, years of experience, education, projects worked on, hours logged, etc. Text Sources: Document authored (major and minor authors) – data and/or text Documents associated (teams, themes) – categorized to a taxonomy Experience description – extract concepts, entities Self-reported expertise – requires normalization, quality control Complex judgments: Faceted application Ensemble methods – combine evaluations

Enterprise Search Workshop: Building on the Platform - Expertise Analysis
Expertise Characterization for individuals, communities, documents, and sets of documents Experts prefer lower, subordinate levels Novice & General – high and basic level Experts language structure is different Focus on procedures over content Applications: Business & Customer intelligence – add expertise to sentiment Deeper research into communities, customers Expertise location- Generate automatic expertise characterization based on documents

Enterprise Search Workshop New Approaches – Applied Watson
Key concept is that multiple approaches are required – and a way to combine them – confidence score Aim = 85% accuracy of 50% of questions (Ken Jennings – 92% of 62% Used a combination of structure and text search Massive parallelism, many experts, pervasive confidence estimation, integration of shallow and deep knowledge Key step – fast filtering to get to top 100 (System 1) Then – intense analysis to evaluate (System 2) – multiple scoring

Enterprise Search Workshop New Approaches – Applied Watson
Multiple sources – taxonomies, ontologies, etc. Special modules – temporal and spatial reasoning – anomalies Taxonomic, Geospatial, Temporal, Source Reliability, Gender, Name Consistency, Relational, Passage Support, Theory Consistency, etc. Merge answer scores before ranking 3 Years, 20 researchers of all types Got to 70% of 70% - in two hours More difficult answers / more complete questions

Enterprise Search Workshop: Applications Social Media: Beyond Simple Sentiment
Beyond Good and Evil (positive and negative) Social Media is approaching next stage (growing up) Where is the value? How get better results? Importance of Context – around positive and negative words Rhetorical reversals – “I was expecting to love it” Issues of sarcasm, (“Really Great Product”), slanguage Granularity of Application Early Categorization – Politics or Sports Limited value of Positive and Negative Degrees of intensity, complexity of emotions and documents Addition of focus on behaviors – why someone calls a support center – and likely outcomes

Enterprise Search Workshop: Applications Social Media: Beyond Simple Sentiment
Two basic approaches [Limited accuracy, depth] Statistical Signature of Bag of Words Dictionary of positive & negative words Essential – need full categorization and concept extraction New Taxonomies – Appraisal Groups – Adjective and modifiers – “not very good” Supports more subtle distinctions than positive or negative Emotion taxonomies - Joy, Sadness, Fear, Anger, Surprise, Disgust New Complex – pride, shame, confusion, skepticism

Enterprise Search Workshop : Applications Behavior Prediction – Telecom Customer Service
Problem – distinguish customers likely to cancel from mere threats Basic Rule (START_20, (AND, (DIST_7,"[cancel]", "[cancel-what-cust]"), (NOT,(DIST_10, "[cancel]", (OR, "[one-line]", "[restore]", “[if]”))))) Examples: customer called to say he will cancell his account if the does not stop receiving a call from the ad agency. cci and is upset that he has the asl charge and wants it off or her is going to cancel his act More sophisticated analysis of text and context in text Combine text analytics with Predictive Analytics and traditional behavior monitoring for new applications

Enterprise Search Workshop : Applications Variety of New Applications
Essay Evaluation Software - Apply to expertise characterization Avoid gaming the system – multi-syllabic nonsense Model levels of chunking, procedure words over content Legal Review Significant trend – computer-assisted review (manual =too many) TA- categorize and filter to smaller, more relevant set Payoff is big – One firm with 1.6 M docs – saved $2M Financial Services Trend – using text analytics with predictive analytics – risk and fraud Combine unstructured text (why) and structured transaction data (what) Customer Relationship Management, Fraud Detection Stock Market Prediction – Twitter, impact articles

Enterprise Search Workshop : Applications Pronoun Analysis: Fraud Detection; Enron Emails
Patterns of “Function” words reveal wide range of insights Function words = pronouns, articles, prepositions, conjunctions, etc. Used at a high rate, short and hard to detect, very social, processed in the brain differently than content words Areas: sex, age, power-status, personality – individuals and groups Lying / Fraud detection: Documents with lies have Fewer and shorter words, fewer conjunctions, more positive emotion words More use of “if, any, those, he, she, they, you”, less “I” More social and causal words, more discrepancy words Current research – 76% accuracy in some contexts Text Analytics can improve accuracy and utilize new sources Data analytics (standard AML) can improve accuracy

Enterprise Search Workshop Conclusions
Enterprise Search is broken Search requires semantics (What is non-semantic search?) Adding Semantics requires an infrastructure approach People, Technology, Processes, Content & content structure Text Analytics can change the game – in conjunction with other infrastructure elements Semantic Search as a platform for SBA – payoff is enormous Want to learn more – come to Text Analytics World in San Francisco in March! Early Bird Registration –

Questions? Tom Reamy tomr@kapsgroup.com KAPS Group
Knowledge Architecture Professional Services

Resources Books Women, Fire, and Dangerous Things
George Lakoff Knowledge, Concepts, and Categories Koen Lamberts and David Shanks Formal Approaches in Categorization Ed. Emmanuel Pothos and Andy Wills The Mind Ed John Brockman Good introduction to a variety of cognitive science theories, issues, and new ideas Any cognitive science book written after 2009

Resources Conferences – Web Sites
Text Analytics World - All aspects of text analytics March 17-19, San Francisco Semtech

Resources Blogs Web Sites SAS- http://blogs.sas.com/text-mining/
Taxonomy Community of Practice: LindedIn – Text Analytics Summit Group Whitepaper – CM and Text Analytics - Whitepaper – Enterprise Content Categorization strategy and development –

Resources Articles Malt, B. C Category coherence in cross-cultural perspective. Cognitive Psychology 29, Rifkin, A Evidence for a basic level in event taxonomies. Memory & Cognition 13, Shaver, P., J. Schwarz, D. Kirson, D. O’Conner Emotion Knowledge: further explorations of prototype approach. Journal of Personality and Social Psychology 52, Tanaka, J. W. & M. E. Taylor Object categories and expertise: is the basic level in the eye of the beholder? Cognitive Psychology 23,

Resources LinkedIn Groups: Journals Text Analytics World
Text Analytics Group Data and Text Professionals Sentiment Analysis Metadata Management Semantic Technologies Journals Academic – Cognitive Science, Linguistics, NLP Applied – Scientific American Mind, New Scientist

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