As more and more organizations move from recognizing that unstructured data exists, and remains untapped, the field of semantic technology and text analysis capabilities is

1. Building
intelligence
through
semantics
Machine
Learning
Sentiment
Analysis
Text
Text
Analytics
Analytic
s
Ontology
Building
Context
Analysis

2. About Veda
•
A semantic technology service provider leveraging its capabilities to provide
standardized and bespoke solutions
Awards and
references
•
One of 5 companies worldwide named as Semantic Application Specialists by
Gartner (Who’s Who of Text Analytics, September 2012)
Formation
and
background
•
•
Started as a JV with the Fraunhofer Institute, Germany
Earlier part of 3i Infotech, a large listed IT form. Acquired by current promoters as
part of a management buy out
•
Headquartered in Bangalore, India’s software capital, with ready access to critical
talent
•
Currently a 20 member team, also having a sales presence in Chicago, USA. Key
members of technology team each have over a decade’s worth of experience in
semantic technology
Who we are
Location
Team
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3. Enterprise’ Information Distribution
~30%
Unstructured Data:
• Consists of textual
information like
contracts, emails,
presentations
• 70% of organizations’
information remains
in an unstructured
form hence it is not
utilized at all.
~70%
Structured Data:
• Consists of information
from ERP, CRM systems,
XML data
• It is organized and
manageable
• Currently only 30% of
organizations’
information is analysed
for decision making
Are we using only structured data for decision making? What are the critical misses
that are made as a result?
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4. What is hidden in unstructured data
Examples of unstructured data
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Customer complaints
Employee feedback
Brand perception
Financial data from reports
Competitive news
Information
Facts
Events etc.
And many many more….
What it contains
• Insights
• Opportunities
• Risks
• Just the things needed
for good decision
making!
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5. Semantics – making sense of unstructured data
• Semantics is the study of meaning. It focuses on the
relation between signifiers, like words, phrases, signs,
and symbols, and what they stand for their denotation.
[Wikipedia]
• SEMANTICS = MEANING
• It is about describing things
• In linguistics, semantics is the subfield that is devoted to
the study of meaning as inherent at the levels of words,
phrases, sentences, and larger units of discourse.
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6. Industry Overview - Need for Semantic Technology
Information
overload
Heterogeneous
Distributed
Unorganized
High data
volumes
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•
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Increasing numbers
Increasing Sources
Unmanageable
Inefficient
retrieval
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Keyword search is inefficient
Lack of Classification and relevance
Focus on “Search” rather than “Find”
The definition of ‘Data’,
which had been artificially
restricted to only
numerical data, can now
extend to text and other
unstructured data as
well…
…Providing more insights
and richness for decision
making

7. Top 9 Technology Trends Likely to Impact Information
Management in 2013
Technology Trend
Big Data
Modern information infrastructure
Semantic technologies
The logical data warehouse
NoSQL DBMSs
In-memory computing
Chief data officer and other information-centric roles
Information stewardship applications
Information valuation / infonomics
Source: Gartner
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8. Broadly, text based offerings can be clubbed under two main
heads
Statistical text mining
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Natural language processing
Looks for documents based on statistical
techniques.
Helps identify high frequency terms or
expressions
Identifies other terms being used in
conjunction with them
Assigns match probability to documents
based on mathematical techniques to
facilitate searches and knowledge
management
Accuracy could be improved further by
using machine learning principles
•
Primary applications: Text mining and
document matching (eg VoC analysis,
Email analysis, E Discovery, etc)
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Parses a sentence to identify nature of
words in it
More relevant for sentence level analysis
as opposed to document level analysis
Principles of English, as opposed to
statistical techniques, take precedence in
analysis
Accuracy dependent on strengths of
algorithms written
Primary applications: Named Entity
Extraction (knowledge management),
Sentiment analysis (VoC analysis, E mail
monitoring, etc)

9. Industry Overview – usual application areas
Areas
Technique used
Social media analytics
Better advertising placement
CRM information capture and action
Sentiment Analysis using NLP
Coupled with vertical specific taxonomies
E Discovery
Auto classification
Forensic analysis
Statistical text mining
Named Entity Recognition (NER)
Machine learning
Pattern analysis
Predictive modelling
Statistical text mining
Named Entity Recognition
Coupled with structured data (e.g. frequency of
mails, department information, etc)
Knowledge
Management
Auto tagging and classification
Discovery (eg healthcare information
sharing)
NER (for named entities)
Statistical text mining
Custom ontologies / semantic networks
Vertical specific use
cases
Examples:
Financial services, Publishing, Pharma,
Healthcare, Legal, Insurance, etc
Various degrees of text mining, NLP and
sentiment analysis, and entity extraction
techniques
Marketing
Compliance
Risk analysis, Fraud
detection
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10. But purely from an R&D perspective, quality thresholds
have a very high standard deviation
NLP
eDiscovery
Ontology
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Attaching sentiment to attribute, and attribute to object
Handling basic keywords (e.g. I like something, vs. something is like another)
Vertical taxonomies that allow aggregation
Vertical specific sentiment words (e.g. executing a man vs. executing a
transaction, high fuel economy vs. high fuel consumption)
High variability in Recall and Precision rates
Tagging of concepts remains difficult
Summarization techniques based on basic lexical parsing
Limited use cases
Often seen as multi year projects as opposed to quick win areas

11. The reason for the quality difference is that at many times,
client context is not fully understood and the software is not
trained on such context
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What is the primary purpose for which the tool will be used for: finding trends, better search, forensics, fraud
prevention, building predictive models, etc
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Are certain terms so common that they must be ignored while doing an analysis
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Are there domain specific words that attain a different meaning than in other domains (eg ‘execution’ has a
different meaning in financial services than in the news domain)
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Should weightages assigned to certain kinds of documents / words be increased to improve relevance
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How will the results be presented – are they to be shown visually and not be connected to other enterprise
systems, or should they be an integrated part of the overall BI roadmap of an organization
Unlike traditional systems, text analytics has a large dependency on context. Consequently, in order to unleash
its full potential, the usual bifurcation between consultancy, software development and software
implementation must disappear in the case of text analytics. An off-the-shelf product approach will definitely
not help, and one must adopt a services model to better serve client needs!
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12. In addition, there is limited focus on client needs and
use cases
Technology
focused
•
Companies mostly founded and run by technology experts
Customer
language
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Focus on technology capability and terms as opposed to problems to be solved
Product
approach
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• Leave out value to be derived by examining enterprise specific data more closely, or
integrating it with structured data for greater insights

13. An example of our Natural Language Processing capabilities
“The car model looks like the old one”
“I loved the food, but the service was terrible”
“Did anyone like the car?”
“I really luuuuv it”
“The Tokyo office does not like the current prototype of the
product. Bob said we should talk to them to find out why they are
unhappy. Must close this ASAP to get the launch done by August
2013.”
IP protection:
• Patent being filed for clause based sentiment extraction process
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• Can tag sentiments to attributes,
and attributes to products
• Can handle difficult words, eg ‘like’
based on context – most engines
cannot
• Can handle anaphora resolution
(eg pronouns)
• Can handle Named Entity
Recognition with high recall and
precision

14. Our Discovery product demonstrates the NLP capability in a
powerful manner, making consumer feedback actionable
•
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Clickthrough allows deeper
dives into each category
•
Though price gets mainly
negative reviews, not too many
people seem to talk about it.
Perhaps a discount scheme
could help?
•
Actual sentences are displayed,
and things to which the
sentiments are attached are
highlighted
•
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In this example about a vehicle,
most people care about
comfort, and luckily, the
product gets mostly positive
reviews in this area
Sentiments are associated with
specific aspects of the product

15. Example of Natural Language Processing in Financial
Domain (continuing R&D)
 Extracts economic
factors that have
been impacted
 Recommendations
and predictions help
analyze complex
financial information
in quickest time.
 Helps in predictive
analytics
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16. Example of Natural Language Processing in Financial
Domain – highlighting outlook by driver (continuing R&D)
 Linguistic rules to extract financial / economic indicators
 Domain specific verbs and nouns to understand movement
Financial markets rebounded strongly in 2006's third quarter .
FINANCE ENT : Financial markets
ACTION : rebounded
TIME : 2006's third quarter
MOVEMENT : UP
By the end of the third quarter , crude oil had fallen over 20 %
from its[crude_oil] July peak , while a similar retreat in natural
gas prices produced the latest high-profile hedge fund debacle .
FINANCE ENT : crude oil
ACTION : had fallen
TIME : the end of the third quarter
QUANTITY : 20 %
MOVEMENT : DOWN
FINANCE ENT : natural gas prices
ACTION : produced the latest high-profile hedge fund debacle
MOVEMENT : DOWN
Prices of longer-dated bonds rallied too : the 10-year U. S.
Treasury bond yield fell over 60 basis points during the third
quarter .
FINANCE ENT : Prices of longer-dated bonds
ACTION : rallied
MOVEMENT : UP
FINANCE ENT : the 10-year U. S. Treasury bond yield
ACTION : fell over 60 basis points
TIME : the third quarter
QUANTITY : 60 basis points
MOVEMENT : DOWN

17. Example of Natural Language Processing in Financial
Domain -extracting Cause and Effect (continuing R&D)
As the fourth quarter begins , financial markets remain supported by
positive earnings and interest rate trends .
FINANCE ENT : financial markets
ACTION : remain supported
TIME : the fourth quarter
CAUSE : positive earnings and interest rate trends
EFFECT : financial markets remain supported
However , the pace of U. S. economic activity will slow further by
year-end as weakness in the housing and automotive sectors becomes
increasingly acute .
FINANCE ENT : the pace of U. S. economic activity
ACTION : will slow
TIME : year-end
MOVEMENT : DOWN
CAUSE : weakness in the housing and automotive sectors becomes
increasingly acute .
EFFECT : the pace of U. S. economic activity will slow year-end
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18. An example of our Enterprise capabilities
• Ontology modeling using RDF and OWL semantic web standards
• Document Matching / Similarity using statistical models and concept based approach for Patent Search,
Knowledge Management etc..
• Information Extraction using linguistic models for Fraud Detection, analysis of news stories etc..
• Demonstrated capability for patent search, legal cases, handling survey data
• Machine learning capability allows for precision to be attuned and increased for specific client situations
• Can disambiguate based on domain specific situations, e.g. execution may mean a different thing in a
news domain, vs. executing a transaction in financial services domain
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19. Veda Text Mining capability – key features
Preprocessing
Processing
Data input in various forms (eg txt, doc, etc)
Can accept data from public sources (eg Facebook, Twitter) apart from Enterprise sources
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Removal of junk text around emails
Removal of small Emails like “Thanks”
Removal of forwarded Emails attached to main Email from analysis
Spell checks and autocorrects
Language parsing for English
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Natural Language and Statistical Processing techniques
Extraction of key discussion items from the text, and what is being said in relation to them
Key themes from messages and semantic chaining. Can be combined with sentiment analysis as well.
Ability to handle high velocity and high volume data using Big Data infrastructure (Hadoop, Storm, etc.)
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Input
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Group discussion items into categories and sub categories, while identifying what is being said about
them:
• Automatic for synonyms, singular and plural, etc
• Ability to add / delete categories
• Ability to further analyse sub-categories
Categorization
UI, editing and •
•
export
•
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Simple, easy custom built UI with filtering and drill down capability
Machine learning approach where human insight guides further results
Output not only available in visual format, but exportable to other applications or databases

20. Veda Text Mining capability – screens of analysis in
progress
Clustering conversations into categories using
semantic analysis.
Example customized outputs
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21. Our Delivery Capabilities
Proof of Concept
Trial & Demonstration
Delivery Methodology
High-level client requirements
Detailed solution requirements
- Define the scope of work
- Delivery framework (core offering +
value added services)
- Documented External Interfaces
with Volume and associated
recurring cost (if any) information
- User Guide & Training
- Proof of concept
- Methodology (Agile, Waterfall
approach or client specified
approach)
- Timelines for each deliverable
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- Responsibility Matrix

22. Delivery Methodology
Client assignments
Program
Activities
Project
Delivery
Program
Mgmt
Program
Initiation
Project
Kick-off
Support
Activities
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Feature
Selection
Data Set
Creation
Business
Requirements
Infrastructure
Readiness
Program
HR
Mgmt
Analysis
and
Design
Operational
Readiness
Program
Benefits
Tracking
Change
Analysis
Project
Closure
Machine
Learning
Development
Support
Delivery
Test &
Verify
Training
Release
Post
Release
Support

23. 26
Taking the next step
*Implement for a
business
function/division/a single
geography
*Multiple features of SIS
implemented including
cross business solutions
leading to concrete
measurable gains
Phase 3
Veda will solve a business
challenge you choose to
demonstrate the power of a
semantics based solutions
in a quick turn around
(Typically within few days)
exercise
Phase 2
Phase 1
For bespoke development, we are prepared to start
small, to show clients clear value and RoI
Replicating the success of
the previous phase –
*Across Larger Sections of
the enterprise
*Wider Data consolidation
scope
*Multiple output delivery
channels
*Visible long term gains

24. But ultimately, we believe that clients will benefit
considerably by a unified Semantic Information System
Staging Area
Data Warehouse
Reporting
Data Mart
* Insights from Unstructured
data coupled with Analytics
from Structured Data assets (E.g.
BI, Big Data)
Dashboards
Databases
Structured data
Store into Cubes
Data Mart
Processed data
Databases
Alerts
Unstructured
data
(Server,SAN,SAS)
Internet
Public Web Data
Ready insights
Processed data
Online
Natural Language processing
Email Crawler
Ontologies
Files Crawler
Data
Semantic Analysis
Knowledge Base
Crawler
Unstruct ured Data
Categorized
Data
Veda Organising Processes
Web Crawler
Social Media
Auto Classification
Visual Segregation
Unstructured & Semi-Structured Data
Structured Data
Social Media
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Processed data
Veda Collection Processes
chatter
* Collecting unstructured
data from disparate sources
Databases
Formatted data
Structured Data
* Analyse all collected
unstructured data, Organize it
using rich knowledge
representation/domain
ontologies
Data
Structured Data
Data Mart
Marketing
Purchasing
Payroll
Sales
LOB Applications
Operations

25. Veda Approach – COP Framework
Our proprietary Collect – Organize- Present framework and tools allow us to undertake quick bespoke
development
• Connectors
Collect
— Collect information from variety of (heterogeneous) sources
• Information Extraction
— Using NLP and semantic analysis
• Semantic Net / Ontology Editor
— Smart knowledge representation of a domain
Organize
• Auto Classifier
— Classify data and tag it to industry specific concepts automatically
• Ontology Reasoning
— Analyze industry knowledge and infer from ontological knowledge
• Analytics
— Identify various patterns and insights from the data
Present
• Semantic Matching
— Provide most relevant information
• Semantic Search and Browsing
— Semantic explorer to retrieve contextual concept-based information
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26. Veda’s Value Proposition
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Technology
Deep understanding of the Semantics space
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Expertise in both NLP and ontologies / taxonomies, and in standards (RDF / OWL)
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In the semantic technology space for more than a decade
Team has provided services not only to clients, but to other semantic service providers
Tie up with academia
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Delivery
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Allows for cutting edge R&D
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Tie up with leading Indian university in the area
High quality talent pipeline
Live - Delivery and Support Turnaround
— The Veda Platform is the core that
— Is a solution accelerator giving a head start to all our assignments (tested and
certified components)
— Allows for lower costs
— Allows for incremental rollouts

27. Veda’s Value Proposition (contd)
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Expertise in Multiple Business Domains
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Experience
Healthy mix of business and technology expertise – can provide clear use cases for
Semantics and help establish clear RoI metrics
•
Core team members have had experience in Semantic technology since 2003, longer
than most other companies
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Technology team experienced in providing expertise in a wide variety of business
domains leading to speedy and effective solution implementations
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Located in India, with associated inherent advantages
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Lower cost options for clients with onshore – offshore model
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24 hour work cycle
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Large talent pool
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Location
Tie ups with companies focused on various other related technologies to offer
integrated offerings, eg full service offering / working with offshore vendor to make
outsourced processes more efficient using semantics
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28. Veda’s End-to-End Semantic Expertise
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Text Analytics
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Analyzing unstructured text, converting to structured data
Machine learning
— Statistical techniques resulting in increasing accuracy over time (with more inputs)
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Sentiment Analysis
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Semantic Information Retrieval
—
•
More artifacts searched/More accurate – e- Mails, Documents, Spreadsheets, Output from
existing structured data sources
Semantic Web Standards
—
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Identifying if the sentiment of a sentence is positive, negative or neutral (and the various shades
in between)
Standardized storage and output formats for easier information sharing

29. Past Experience
Client Profile
Project Description
A global publishing house in legal, tax,
finance and healthcare
 Context-based content research platform for tax & legal domain
 Automatic meta-tagging , ontology modeling and ontology driven
content reference system.
A prominent product manufacturer on
inference and reasoning engine
 Leveraged semantics for a supply chain process to integrate systems
with heterogeneous data sources and help in automatic decision
making in case of any disruptions in the cycle.
 Provided ontology modeling and application development services.
A reputed university and complex systems  Produced a method for organizing and potentially navigating the wide
research lab in Australia
range of web-pages associated with the Murray-Darling river system in
a seamless fashion
An analytics software manufacturer in
Australia
A premier worldwide online providers of
news, information, communication,
entertainment and shopping services
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 Assist investigation of fraud and terrorism – Establishing links between
entities
 Unstructured data analysis
 Developed a web analytics platform for analyzing click-stream data in
real-time.

30. Some sample use cases mapped to our current
technology demonstrators
Current situation
•
How Semantics will help
Mapping to current Veda
technology demonstrator
Saved in C drives or in DMS, separate excel
sheets maintained to check on timely
renewals, etc.
Tough to compare specific clauses across
contracts or find relevant clause as needed
•
Search for specific kind of contract
and specific clause will throw up (a)
master template (b) earlier
contracts entered into in the area (c)
extracts from the relevant clause
•
Patent search demonstrator uses
similar techniques, allowing the user
to also see probabilistic match of
documents
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Dig deep into embedded code to see what
departments and areas will get impacted
•
Ontology based relational steps
make it easy to see connected
departments, processes, etc. that
will be impacted
•
Tax caselaw and section ontology
created
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Mapping social sentiment and reviews
done manually or using dictionary based
social monitoring tools
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Some social marketing and social
listening already being done, though
not accurate. A better quality NLP
engine allows for more accurate
results (e.g. the word ‘like’).
•
Veda Discovery Engine which has
sentiment capabilities
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Obtaining right resumes using keyword
search remains time consuming
Employee suggestions in open ended
surveys not aggregatable
Qualitative comments in employee
evaluations not aggregated
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Identify key intervention areas at
aggregate levels
Map trends in overall ratings to key
strength and weakness areas
•
Veda Discovery for aggregation,
Veda Txt for identification of gist of
comments
Metatagging remains a manual process
and as a result, searches remain searches,
not findings
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Automatic metatagging (Persons,
Locations, Organizations, concepts,
etc.)
•
Veda Discovery – NER Engine, Veda
Legal demonstrator, Veda Msg (for
alerts)
Legal contracts
•
Process
changes
Marketing
HR
•
•
•
Knowledge
management
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•

31. Sample use cases by industries
Domain
Publishing,
media
Allows automatic extraction of people, location, dates and events, being extended to
themes and concepts. Helps in automatic metatagging.
• Current tagging process is manual and time consuming. Technology provides clear RoI
by reducing this time and manual labour, providing consistent tagging, and allowing
easier search for future reference, rather than relying on keywords (eg Mahatma vs
Gandhi vs Mahatma Gandhi).
Oil and Gas
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Description
Can make Incident monitoring and reporting systems more robust, thereby reducing risk
of major accidents
• For incident reporting, a user need not fill in multiple structured data fields. Text
analytics can quickly match data to structured inputs.
• Witness reports, once converted to text, can be monitored across incidents for patters
that would otherwise have gone unnoticed.
Helps make process changes easier and allows all linked aspects to be seen at one go
• Helps determine what other processes and safety regulations are relevant if a sub
process is sought to be changed (could also include contractual information etc if
relevant)
Usually, companies have millions of oil well logs which can be classified by performing
named entity extraction and enrichment

32. Sample use cases by industries
Domain
Description
Financial services
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Contract matching (including addendums)
VoC analysis
• Churn prediction
• Highlights capability gaps
Promotion management
• Avoids duplication of creation of similar material across divisions / locations. Saving in man
hours and resources by leveraging all available material produced earlier
Risk analysis
• Manage and gather customer documents from various sources to look for areas of concern
“Know your customer” analysis
Competitor analysis
Financial news analysis for investment managers
Telecom
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•
•
Legal interception and pattern recognition
SMS analyses for recognizing spam to avoid penalties
VoC analysis
Airlines
•
Analysis of unstructured problem and safety logs to avoid incidents
•
•
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33. Sample use cases by industries
Domain
Description
Healthcare
•
Link and compare patient records to obtain insights on:
• Symptoms, medicines and discharge times to determine if some medication mixes may be
more beneficial than others across a wide set of patient records
• why some patients may be re-admitted
Pharma
•
•
R&D improvement by allowing scientists, who need to refer to papers but may not know exactly
what to look for, to see relevant topics (based on automatic metatagging, and linked ontology at
the backend)
Better knowledge management - automatically tag papers, saving scientist time and making
search consistent
Feedback analysis for product from distributors, doctors and end patients
•
Broker document analysis to deepen insight on insured risks to improve risk management
•
Insurance
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34. Sample functional use cases
Domain
Marketing
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•
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Voice of Customer analysis
New product ideas
Competitor analysis
Complaint monitoring
HR
•
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Drawing insights from employee suggestions
Analysing unstructured inputs in evaluations and improving training efficacy
Risk
•
Internal document monitoring for risk and compliance
Legal
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Description
•
Better contract management

35. Veda Solutions Currently Deployed
Veda for Business Process Workflow
• Configurable to any Business
requirement across Industries
• Sources of content can be structured
AND Unstructured
• Can be integrated to various Business Applications - ERP, Content Management, Portals, etc..
• Configurable User Interface with features such as:
– Saving of Search for later reference
– Tabbed Views
– No. of results to be displayed with sort order
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36. Veda Solutions Currently Deployed
Veda Social Media Analytics
 Registration & log in
 Inputs from Social Media
 Inputs from Blogs, Websites
 Hierarchy & Relevance Analysis
 Sentiment Analysis
 Rich Reporting
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37. Veda Solutions Currently Deployed
Veda Recruiter
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38. Veda Solutions Currently Deployed
Veda Patent Search
 Registration & log in
 Subscription
 Payment Gateway
 Keyword Search
 Semantic Search
 Rich Internet Application
 Saved Search
 Filters
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39. Veda Solutions Currently Deployed
Veda SMS Service
 Registration & log in
• Crunches judgment
text into high
relevance words that
can be sent through
an SMS for
immediate access
• Is combined with
website service
offering full access
for relevant cases
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 Subscription
 Payment Gateway
 Keyword Search
 Semantic Search
 Legal ontology (Indian)
 Filters

40. Contact details
Veda Semantics Pvt Ltd
www.vedasemantics.com
Contact person:
Rajat Kumar (CEO)
rajat@vedasemantics.com
# +91-9619308745
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