The whitepaper addresses the challenges in the data–driven organizations, medical research and health care. It summarizes how the context-enabled and semantic enrichment can transform the traditional method to search optimum data. 3RDi has advanced content enrichment with Named Entity Recognition, Semantic similarity, Content classification and Content summarization. Get the right data at the right time that helps medical researchers and health care practitioners.

1. A Semantic Data Platform for Context-Enabled Search,
Discovery, & Analytics in Data-Driven Enterprises & Organizations
In this era of big data, all enterprises and systems have more content than ever before. There’s a lot of data which is
exploded, fragmented, redundant, underutilized, inconsistent, hard to find and hard to understand. “ ‘Big Data’is a
Challenge and an Opportunity at the same time.”
Organizations face the challenge of making the best possible use of their data while dealing with dynamic and changing
data and also responding to business demand for faster time to market. Information users, with more content and less
time to analyze, need systems that are smart and intelligent enough to integrate the scattered content, provide quicker
discovery of information and tools for thorough analysis of content.
“At T/DG, we realize the
enormous challenges and the
possibilities associated with
Big Data. Our products and
tools help integrate all your
content seamlessly, exploit the
power of semantic enrichment,
transforming the content to
knowledge; thus taking you to
that next level of Search and
Analytics solutions.”
White Paper
EMPOWERING SEARCHES THROUGH
3RDi SEMANTIC ENRICHMENT
3RDi – Relevant | Deep | Insights
“The thing that differentiates one enterprise from another, is the way they understand the
content and utilize it!”
OF THE 60 % YEARLY GROWTH IN
CONTENT, MAJORITY OF IT IS
UNSTRUCTURED.
EVERY MINUTE, OVER 72 HOURS OF
NEW VIDEO CONTENT IS UPLOADED
TO YOUTUBE.
EACH DAY, OVER 1 MILLION NEW
BLOG POSTS ARE CREATED ON
WORDPRESS
On one side, there is a risk
associated with integration
and management of data,
while, on the other side, there
are exciting possibilities with
increased live content.
Access to content is not the
differential anymore - it is
pretty much with everyone.

2. White Paper
HOW CAN SEMANTICS HELP?
Giving meaning to content!
“Use of semantics makes content more contextual, more discoverable, and
thereby, more valuable.”
Semantics is about computers interpreting and understanding the content at a
deeper level, assisted by its meaning and context. It helps in information search
and discovery by associating additional information with the content.
“46% of Scientific journal
publishers are
semantically enriching
their content” says a
study on Journal Article
Mining by the Publishing
research consortium.
3RDi solves all of your
content enrichment,
content discovery and
result analysis problems. Semantic Enrichment is the process of adding or associating
semantic tags – usually concepts, relationships, events and
properties described in an ontology – to augment unstructured
data items.
Enrichment frees you from manual ‘meta-data’ tagging. It identifies several interesting properties and associations of
your content which go a long way in empowering the information search and analysis process.
The process of semantic enrichment, integrates the content with the identified ontologies and links it to available
open data. This adds more information to the content – referred to as Enrichment. The enriched content is indexed
and stored. This serves as a base for search and discovery applications.

3. SEMANTIC ENRICHMENT AND
SEARCH WITH 3RDi
Discovery, beyond search!
3RDi takes a holistic view of the problem of Search, Discovery and Analysis. It
provides comprehensive semantic enrichment of unstructured content and unlike
most other products in the market, it also provides a complete Semantic Search
Solution to leverage the enrichment of the Search, Discovery and Analysis
applications.
“3RDi embodies semantic interpretation in all phases of the information
retrieval systems – content integration, search, result navigation, and
search analytics.”
SEMANTIC ENRICHMENT CAPABILITIES OF 3RDi
3RDi’s semantic and natural language processing (NLP) capabilities are built on a
strong base of machine learning and lookup based algorithms. These algorithms
are pre-integrated with a comprehensive set of general and domain specific
thesaurus as well as taxonomies and are built on top of best-in-class tools.
Here is what 3RDi provides for content enrichment:
Recognizing the mentions of entities in the content and identifying their
types – a process called Named Entity Recognition
Automatic analysis of sentiments represented in the content
Identifying the key topics from the content and classifying the content as per
those
Disambiguation of entities based on context in which they appear
Refining the entities by mapping them to standardized terms from
taxonomies and ontologies
Enriching content by linking it to URIs of other open data (like DBpedia)
White Paper
Named Entity
Recognition
Semantic
Similarity
Content
Summarization
Sentiment Analysis Content
Classification
Ontology &
LOD Mapping
Vocabulary Support
WordNeT 3.1
YAGO
DBpedia 2014
UNESCO 2.0
EUROVOC 4.3
UMLS Metathesaurus
2015
MeSH 2015
UIMA 2.7
cTAKES 3.2.2
SNOMED CT 2015
Custom and Legacy
Vocabularies
SKOS-RDF, OWL

4. White Paper
Domain specific
Semantic Enrichment
Ready support for
Bio-Medical, Legal,
Education and
Publishing domains
LOD linking
Thesauri and custom
vocabulary support
“Semantic Search
matches items
intelligently across
unstructured data by
enriching the data
with semantic
information during
indexing.”
SEMANTIC ANALYSIS PROCESS IN 3RDi
Semantic analysis within 3RDi, is split into several stages. In each stage, the
system uses fast and superior algorithms that result in comprehensive
enrichment and faster integration of content.
3RDi SEMANTIC SEARCH EXPERIENCE
Semantic search tries to understand what a user is asking in a query by
analyzing the query’s terms and language and placing it in context. It
produces meaningful results, even though the retrieved items contain none
of the query terms or the search involves no query text at all. Further,
instead of only returning a set of results, semantic search addresses the user
information needs more directly with aids like semantic facets and result
clustering, etc.
Semantic Analysis Process in 3RDi
Tokenization: splitting text into individual
sentences and words.
Morphological analysis, stemming:
recognizing when two different word forms
come from the same root word.
Grammatical analysis, parts-of-speech
tagging: labeling when a word is used as a
noun, verb, adjective and adverb, etc.
Word sense disambiguation: is "crane" in a
given text a type of bird or a type of machine.
Vocabulary, ontology mapping: does “crane”
have synonyms or does “crane” belong to a class
of “construction automobile”.
Linked Open Data: map to Wikipedia, DBpedia
and link to useful information about, say,
manufacturers of “crane”

5. White Paper
3RDi’s Semantic Search makes the information retrieval process more
efficient. A range of tools like automatic facets, concept search, taxonomy
browsing and clustering, etc. help the user skim through the content quickly
and easily and locate the desired information.
Ontology navigation: Ontology navigation: With an easy-to-use graphical
interface, you can navigate through the taxonomy (or ontology) and select
any combination of concepts you wish to explore. You can search content
that relates to these concepts and also broaden or target your search by
including or excluding synonyms, hyponyms and related terms.
Semantic Cloud: Gain a quick insight into the content using the semantic
cloud which sums up your content at a stretch.
Knowledge Graph: Without having to read the content at all, you can get an
insight into it using the graphical representation of the entities and their
interrelations as contained in the text.
Semantic Facets: You can drill down on the search results using dynamically
created business-centric navigational facets.
Concept Search: You can search for documents that relate conceptually to
the query word even when the query word does not exist anywhere in the
content.
Result Clustering: Automatic grouping of search results depending on their
content can help you discover information faster.
Similar Documents
Related Recommendation
Concept Search
Knowledge Graph
Sentiment
Clustering
Semantic Cloud
3RDi provides a
powerful way of
finding, discovering
and analyzing
information.

6. White Paper
3RDi Healthcare Edition
The ‘3RDi Healthcare Edition’ is customized and optimized to cater to the specific needs of researches in the
biomedical area. The tool is integrated with the most trusted healthcare terminology and ontologies that include
UMLS, Mesh, ICD and SNOMED CT, etc. Through the specialized biomedical entity extraction – including difficult to
identify ‘Diseases’, ‘Chemicals’, ‘Drugs’, ‘Equipment’, ‘Anatomy’ and ‘Procedures’ - and the rich semantic analysis of the
results, 3RDi fosters improved biomedical research and helps healthcare professionals with evidence based medicine.
For Medical Researchers For Healthcare Practitioners
Diagnose quickly and correctly
Confront observations with authoritative
knowledge and facts
Visuals
Comprehensive
Critical
Identify Scientific Publications
Relevant to my research area…
Protect Against Infringement
Find insights
Biological pathways
Disease mechanisms
New targets/leads/markers
Drug side effects
New uses for existing drugs

7. White Paper
More information
Visit the http://www.3rdisearch.com to understand more about 3RDi - T/DG’s Enterprise Semantic Search, Discovery
and Analytics Platform.
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