Veda is an end-to-end semantic framework that originated from Fraunhofer Institute in Germany. It covers all aspects of a business solution including collecting structured and unstructured data, organizing it semantically, and presenting information through retrieval and analysis. Veda's product portfolio includes tools for extraction, classification, matching, and inference. It has been deployed in solutions for workflows, social media analytics, recruitment, and patent searching. Key differentiators of Veda include its end-to-end technology coverage, business application focus, implementation team, and commercial models.

1. Convert Data to Actionable
Intelligence

2. Veda Overview – Origin and Growth
1.Semantic Technology IP
2.Originated at Fraunhoffer Institute, Germany
3.Product enhanced over the last decade
4.End to end Semantic Framework
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3. Veda Overview - COP Model
1.Covers all aspects of a Business Solution
2.Has the ability to work on Structured and Unstructured Data
3.The framework enables rapid deployment of Semantic
Applications
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4. Veda Overview - Product Portfolio
Area Technology / Tools Description
Web Crawler Supports HTTP with authentication
Data Email Crawler Supports IMAP, POP3 protocols
Collect
Aggregation Database Crawler Supports relational databases
File Crawler Word Reader, PDF Reader, Text Reader, HTML Reader
Information Visual Entity Extractor Domain specific entity extraction platform
Extraction Term Extraction NLP based concept, phrase identification
Semantic Net API & S-net editor
OrganIze
Editor
Information Ontology Editor OWL editor, Rules
Organization AutoClassification Lazy (term-based) and machine learning
Ontology Data Maps data to the ontology
Mapping
Semantic Matching Context based retrieval using semantic net
Present
Information Inference Engine Supports OWL DL Lite
Retrieval and Latent Semantic Latent semantic analysis and information retrieval
Analysis Indexing
Semantic Rule Engine Rule engine with inferencing capabilities
Visual entity extraction technique
Patents Filed
Method of Context-based information retrieval
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5. Product Strategy
 Semantic
Search
 Standards
 Text Analysis
 Semantic  Automated
Search Reasoning
Evolution of Semantic Technology
 Standards  Semantic
 Text Analysis Content Mgmt.
 Semantic  Automated  Knowledge
Search Reasoning based Apps
 Semantic  Standards  Semantic  Semantic Social
Search  Text Analysis Content Mgmt. Computing
 Standards  Automated  Knowledge  Linked Data
 Text Analysis Reasoning based Apps  Predictive
 Automated  Semantic  Semantic Social Analytics
Reasoning Content Mgmt. Computing
 Semantic  Knowledge  Linked Data
Content Mgmt. based Apps  Predictive
 Knowledge  Semantic Social Analytics Legend
based Computing
applications  Linked Data Matured
 Semantic Social  Predictive Advanced
Computing Analytics
Progressive
 Linked Data
Inception
2011 2012 2013 2014
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6. Product Map vis-à-vis Opportunity Areas
Functional Areas Technology Components Availability in Content Social Media Semantic Advertising Text Consumer Semantic
Veda Management Platform Search Tools Analysis Insights Mobile and
Platform Web Apps
Content Web crawler Yes Y Y Y Y
Aggregators Email crawler Yes Y Y Y Y
File crawler Yes Y Y Y Y
Database crawler Yes Y Y Y
Adapter for Online Feeds (RSS, Atom) WIP Y Y Y Y Y
Adapter for Twitter Yes Y Y Y Y Y
Adapater for Facebook Yes Y Y Y Y Y
Adapter for LinkedIn Yes Y Y Y Y
Semantic Semantic Net Editor Yes Y Y Y Y Y Y
Network / Ontology Editor Yes Y Y Y Y Y Y
Ontology Semantic Rule Editor Yes Y Y Y Y Y Y
Semantic Storage Ontology storage (RDBMS) Yes Y Y Y Y Y
of Content Semantic Net Storage (RDBMS, XML) Yes Y Y Y Y Y
Semantic Semantic Net based classification Yes Y Y Y Y Y
Organization of Bayes Naïve Classifier Yes Y Y Y
Content Ontology Inference Engine (support for Yes Y Y Y Y
OWL 1.0)
Semantic Semantic Matching (Semantic Net) Yes Y Y Y
Retrieval Semantic Browsing Yes Y Y Y Y
Latent Semantic Indexing Yes Y Y
Semantic Net API Yes Y Y Y
OWL API Yes Y Y Y Y
Semantic Preprocessing - Identification of Yes Y Y Y Y Y Y Y
understanding of paragraphs and sentences
content and Preprocessing - Identification of POS Yes Y Y Y Y Y Y Y
natural language Preprocessing - Identification of term Yes Y Y Y Y Y Y Y
query Preprocessing - Word Stemming Yes Y Y Y Y Y Y Y
Preprocessing - Removal of stopwords Yes Y Y Y Y Y Y Y
Weighting schemes for terms Yes Y Y Y Y Y Y Y
Named Entity Recognition and WIP Y Y Y Y Y Y Y
Relationships (NLP based)
Named Entity Recognition (Visual Yes Y Y Y Y Y Y Y
Segregation)
Phrase Identification Yes Y Y Y Y Y Y Y
6 Sentiment Analysis WIP Y Y Y

7. 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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8. Veda Solutions Currently Deployed
Veda Social Media Analytics
 Registration & log in
 Inputs from Social Media
 Inputs from Blogs, Websites
 Heirarchy & Relevance Analysis
 Sentiment Analysis
 Rich Reporting
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9. Veda Solutions Currently Deployed
Veda Recruiter
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10. 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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11. Industry Examples of Applicability of
Veda
• Healthcare: Patient Treatment Process using
• Patient’s past records
• Recent treatment of similar ailments across the
Hospital / State / Country, etc.
• Medical Research information available in the
Internet
• Professionals: Tax / Legal / Patent Filing using
• Case Laws, Guidelines, Notifications
• Statutory Records, Past Filings
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12. Industry Examples of Applicability of
Veda
• Manufacturing: Purchase Process using
• Past Purchase documents
• Material documents / brochures
• Live Pricing information from websites of
Suppliers, Exchanges, e-Commerce sites
• Human Relations: Recruitment / Appraisal
Process using
• Past Employee Records
• Profiles from Social Networking sites
12 • Recent performance data

13. Key Differentiators
1. One of the few Semantic Product Companies
worldwide that has an end-to-end technology
coverage
2. One of the few Semantic companies worldwide
adopting a Business Application centric view
3. One of the few Semantic Organizations with a
strong in-house Implementation Team
4. Ability to provide different Commercial Models
based on Customer requirements
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14. Thank You
Anand Ramakrishnan
President – Sales and Marketing
eMudhra Consumer Services Limited
anand.r@emudhra.com
+91 9900541568