1. Enterprise Social Networks A New Semantic Foundation
Tom Reamy Chief Knowledge Architect
KAPS Group
Program Chair – Text Analytics World
Knowledge Architecture Professional Services

2. Agenda
Introduction
Integration of Social Media & Enterprise Networks
It’s a Different World
New Approaches
Text Analytics
New Applications – and Opportunities
Conclusion

3. Introduction: KAPS Group
Knowledge Architecture Professional Services – Network of Consultants
Applied Theory – Faceted & emotion taxonomies, natural categories Services:
Strategy – IM & KM - Text Analytics, Social Media, Integration
Taxonomy/Text Analytics, Social Media development, consulting
Text Analytics Quick Start – Audit, Evaluation, Pilot
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, Dept. of Transportation, etc.
Program Chair – Text Analytics World
Presentations, Articles, White Papers –
Current – Book – Text Analytics: How to Conquer Information Overload, Get Real Value from Social Media, and Add Smart Text to Big Data

4. Scale – orders of magnitude – 100’s of millions, Billions
Integration of Social and Enterprise Content Social Media: It’s a Very Different World
Scale – orders of magnitude – 100’s of millions, Billions
Speed – million a day
Size –140 characters to a few sentences
Quality – misspellings, lack of structure, incoherence
Conversations – not stand alone docs
Can’t tell what a “document” is about without reference to previous threads
Purpose – communicate - social grooming, rant
Not exchange of ideas, policies, etc.
Simple Content Complexity – single thoughts, simplicity of emotion

5. Case Study – Categorization & Sentiment

6. Content Integration Beyond Information Management
Emotion taxonomies
Joy, Sadness, Fear, Anger, Surprise, Disgust
New Complex – pride, shame, embarrassment, love, awe
New situational/transient – confusion, concentration, skepticism
Actions and Things
Ontologies – relationships, not simple hierarchical
Graph databases
Neocortex Model
Model local “languages”
Broad inter-silo models
Beyond Search – Need Text Analytics
Analysis of phrases, multiple contexts – conditionals, oblique
Analysis of conversations – dynamic of exchange, private language

7. Enterprise Social Networks Text Analytics – Auto-Categorization
Auto-categorization (badly named)
Training sets – Bayesian, Vector space
Terms – literal strings, stemming, dictionary of related terms
Rules – simple – position in text (Title, body, url)
Semantic Network – Predefined relationships, sets of rules
Boolean– Full search syntax – AND, OR, NOT
Advanced – DIST (#), PARAGRAPH, SENTENCE
This is the most difficult to develop
Build on a Taxonomy(Simple)
Combine with Extraction
If any of list of entities and other words

8. Case Study – Categorization & Sentiment

10. Enterprise Social Networks: Text Mining 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

11. Enterprise Social Networks Basic Level Categories
Mid-level in a taxonomy / hierarchy, Short and easy words
Maximum distinctness and expressiveness
First level named and understood by children
Level at which most of our knowledge is organized
Levels: Superordinate – Basic – Subordinate
Mammal – Dog – Golden Retriever
Experts chunk series of actions, ideas, etc.
Novice – high level only
Expert – special language – based on deep connections

12. Enterprise Social Networks Expertise – application areas
Social Media - Community of Practice
Characterize the level of expertise in the community
Evaluate other communities expertise level
Personalize information presentation by expertise
Expertise location
Generate automatic expertise characterization based on authored documents
Expertise of people in a social network
Terrorists and bomb-making

13. Enterprise Social Networks Conclusions
Social Media is a Different World
Content, Scale, Questions
Text Analytics provides a new foundation for KM
Semantics, expertise location, collaboration, CoP’s
Basic Level Categories are fundamental to thought
What is basic level is context dependent
Cognitive Science – New Models of Knowledge
Cognitive Computing
Voice of the Enterprise can do for KM what Sentiment Analysis is doing for eCommerce and Voice of the Customer
Adds semantics / meaning to KM programs and software

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

15. Taxonomy and Social Media: Applications New Range of Applications
Real Sentiment Analysis - Limited value of Positive and Negative
Degrees of intensity, complexity of emotions and documents
Contextual rules – “I would have loved X except for the battery”
Expertise Analysis
Experts think & write differently – process, chunks
Categorization rules for documents, authors, communities
Behavior Prediction–TA and Predictive Analytics, Social Analytics
Crowd Sourcing – technical support to Wiki’s
Political – conservative and liberal minds/texts
Disgust, shame, cooperation, openness