Veda Semantic Technology
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Transcript of Veda Semantic Technology
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
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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Veda Overview - Product Portfolio Area Technology / Tools Description
C o
l l e
c t
Data Aggregation
Web Crawler Supports HTTP with authentication
Email Crawler Supports IMAP, POP3 protocols
Database Crawler Supports relational databases
File Crawler Word Reader, PDF Reader, Text Reader, HTML Reader
Information Extraction
Visual Entity Extractor Domain specific entity extraction platform
Term Extraction NLP based concept, phrase identification
O r
g a
n I
z e
Information Organization
Semantic Net API & Editor
S-net editor
Ontology Editor OWL editor, Rules
AutoClassification Lazy (term-based) and machine learning
Ontology Data Mapping
Maps data to the ontology
P r
e s
e n
t
Information Retrieval and Analysis
Semantic Matching Context based retrieval using semantic net
Inference Engine Supports OWL DL Lite
Latent Semantic Indexing
Latent semantic analysis and information retrieval
Semantic Rule Engine Rule engine with inferencing capabilities
Patents Filed Visual entity extraction technique
Method of Context-based information retrieval
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Product Strategy
Semantic Search
Standards Text Analysis Automated
Reasoning Semantic
Content Mgmt. Knowledge
based applications
Semantic Social Computing
Linked Data
2011 2012 2013
Evo
luti
on
of
Sem
anti
c Te
chn
olo
gy
Matured
Advanced
Progressive
Inception
Legend
Semantic Search Standards Text Analysis Automated
Reasoning Semantic
Content Mgmt. Knowledge
based Apps Semantic Social
Computing Linked Data Predictive
Analytics
2014
Semantic Search
Standards Text Analysis Automated
Reasoning Semantic
Content Mgmt. Knowledge
based Apps Semantic Social
Computing Linked Data Predictive
Analytics
Semantic Search
Standards Text Analysis Automated
Reasoning Semantic
Content Mgmt. Knowledge
based Apps Semantic Social
Computing Linked Data Predictive
Analytics
6
Product Map vis-à-vis Opportunity Areas Functional Areas Technology Components Availability in
Veda
Content
Management
Social Media
Platform
Semantic
Search
Platform
Advertising
Tools
Text
Analysis
Consumer
Insights
Semantic
Mobile and
Web Apps
Content
Aggregators
Web crawler Yes Y Y Y Y
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 Net Editor Yes Y Y Y Y Y Y
Ontology Editor Yes Y Y Y Y Y Y
Semantic Rule Editor Yes Y Y Y Y Y Y
Ontology storage (RDBMS) Yes Y Y Y Y Y
Semantic Net Storage (RDBMS, XML) Yes Y Y Y Y Y
Semantic Net based classification Yes Y Y Y Y Y
Bayes Naïve Classifier Yes Y Y Y
Ontology Inference Engine (support for
OWL 1.0)
Yes Y Y Y Y
Semantic Matching (Semantic Net) Yes Y Y Y
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
Preprocessing - Identification of
paragraphs and sentences
Yes Y Y Y Y Y Y Y
Preprocessing - Identification of POS Yes Y Y Y Y Y Y Y
Preprocessing - Identification of term Yes Y Y Y Y Y Y Y
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
Relationships (NLP based)
WIP Y Y Y Y Y Y Y
Named Entity Recognition (Visual
Segregation)
Yes Y Y Y Y Y Y Y
Phrase Identification Yes Y Y Y Y Y Y Y
Sentiment Analysis WIP Y Y Y
Semantic Storage
of Content
Semantic
Organization of
Content
Semantic
Retrieval
Semantic
understanding of
content and
natural language
query
Content
Aggregators
Semantic
Network /
Ontology
• Configurable to any Business requirement across Industries
• Sources of content can be structured AND Unstructured
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• 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
Veda Solutions Currently Deployed Veda for Business Process Workflow
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Veda Social Media Analytics
Registration & log in
Inputs from Social Media
Inputs from Blogs, Websites
Heirarchy & Relevance Analysis
Sentiment Analysis
Rich Reporting
Veda Solutions Currently Deployed
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Veda Recruiter
Veda Solutions Currently Deployed
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Veda Patent Search
Registration & log in
Subscription
Payment Gateway
Keyword Search
Semantic Search
Rich Internet Application
Saved Search
Filters
Veda Solutions Currently Deployed
Industry Examples of Applicability of Veda
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• 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
Industry Examples of Applicability of Veda
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• 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
• Recent performance data
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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Key Differentiators
Thank You
Anand Ramakrishnan
President – Sales and Marketing
eMudhra Consumer Services Limited
+91 9900541568