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Second Presentation URLS to OPEN (and minimize): Michael Belanger, Cofounder, Jarg Corporation.

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Presentation on theme: "Second Presentation URLS to OPEN (and minimize): Michael Belanger, Cofounder, Jarg Corporation."— Presentation transcript:

1 Second Presentation URLS to OPEN (and minimize): Michael Belanger, Cofounder, Jarg Corporation and its SemanTx Life Sciences div.

2 First Key take-away Point Bioinformatics and life sciences areas are ahead in implementation of semantic standards and technologies. The Federal Semantic Enterprise Implementation as well as other fields can now benefit from that ontology-based computing investment and experience.

3 Second Key take-away Point How many different terms from how many different fields can mean the same concept ?

4 Located at Childrens Hospital Boston

5 Just One Professional Field

6 The CMCH SemanTx ontology mediates the disparate vocabulary of 10 professional fields that media research is occurring in: medicine, psychology, education, anthropology, public health, communication, criminology, gender studies, social work sociology.

7 Third Key take-away Point Using the Semantic Knowledge Indexing Platform (SKIP) there is no limitation as to how many different terms, XML tags, Jargon or other metadata from different fields that can be expressed to mean the same concept

8 Currently a few thousand media-related research abstracts are being searched

9 SemanTx Abstraction development test produces is_a measures eukaryotic telomericeukaryote recombinationtelomerecell chromosome process Is_a Is_a property_of location_of Telomeres, the physical ends of chromosomes, are essential for maintaining chromosome stability and structure. The mechanisms that maintain the simple sequences present at the telomere within a discrete distribution is poorly understood. One such mechanisms, termed rapid deletion events (RPD) has been described in our laboratory to occur frequently in Saccha- Development of an Assay for Eukaryotic Telomeric Recombination assay From Both The Info Source and The Query Ontologys Query Expansion From Institutional Knowledge

10 Step 3: Review highlighted contextual answer within document Step 1: Enter query in plain English Step 2: Proves match results Process Overview & Advantages Answers returned, ranked by contextual relevance Allows for cross-disciplinary research to shorten discovery & response cycles

11 Understands - as a Graph Why This Ranking!


13 Forth Key take-away Point Returns have been context-matched and then ordered by how well the meaning within sources matches the meaning expressed in your query The more context you articulate in your query, the more precision in your results Why is this better and different from other search architectures?

14 SKIP Serves-Up Well-Articulated results ! Domain Ontologys Contextual Meaning Cluster Pattern Match Syntactic Taxonomy Entity Extraction Word Match/Key Words Directory Ranked By Fit-To Context Bottom-Up Filtering Clearforest Quigo (categorization) Google MSN Verity, Convera, Endeca iPhrase Yahoo Inxight, Fast Autonomy Search Today Top-Down Semantic Resultsordered by best fit-to-context

15 Boston Childrens Hospital CMCH – SemanTx Smart Search More Contextual Query Ideas for you to try: Should reading and television together be encouraged? Do video games affect children's learning abilities? What is the impact of the media on adolescent sexual attitudes and behaviors? Does TV cause ADHD? Do language tapes help children talk? Can parents prevent children from experiencing unwanted effects of violent television programs? Does watching TV lead to obesity? Articulate Your Own Context-Filled Queries at:

16 E-Government controlled Metadata Tagging mandates? Need to append official cross-government meta-tags to all your s and documents? Problem Solved - (Childrens Hospital Boston) Semantic Abstractions of Content Overcomes Social Issues ********************************** Rapid Employee Turnover ? Critical Employees Retiring ? High Value Consultants Gone ? Scalable Pilot Installation Bio-medical-environmental related department < $25k An Example of: Immediate Learning Curve Transfer X X X X X X E-G Dpt

17 Addressing items 2 & 3 for today: Brand Niemann did try us and typed-in the SICoP challenge query into Jarg Corporations Semantic Life Sciences Divisions Semantic PubMed site: and saw relevant results. Essentially, you have come the closest to meeting the semantic query challenge we featured in the SICoP Module 1 White Paper!

18 Jarg Corporations Semantic Life Sciences Divisions Semantic PubMed site

19 Articulate your need with lots of Context There is no limit to the number of indexed databases - no performance penalties with SKIP Need to searching millions of data files?

20 Click - to MedLine Your query abstracted as a graph

21 Mass General Labs Paper

22 Many Joins

23 Mass General Missing Only 2 Joins

24 Term Synonym Found Mass General Lost the others

25 From All Ontology-Parsed Object Types – SKIP Enables Effective Achievement of Both: Excellent Semantic Precision & Excellent Semantic Recall Due to fit-to-context Search Results Fifth Key take-away Points

26 Filtering Objects By Their Contents Meaning A Fresh, Scalable, High Performance Approach Sub-Ontology based semantic object-parsing –Enables capture of context for the extraction of understood features from within all forms of information to be semantically represented then indexed in SKIPs common semantic (fragment) format Semantically-rich (complex queries) express the context of your need –Return a collage of rich-media results –Each result prioritized by its contextual fit to a users expressed query

27 Core Base Search & Retrieval High Performance Knowledge Interoperability Semantic Queries and SW Agent Alerts Dynamic Situation-Awareness, SW Agent-based Alerts Unified Content Awareness Across The Federal Enterprise Multimedia s Native Content & Geospatial Search Semantic Knowledge Indexing Platform (SKIP) Unique-Identifier Combined Index Ontologies Your Portal

28 SemanTx Life Sciences Seeks Semantic Search Collaborative / Licensing Partners Effective Semantic Use of large Ontologies (UMLS) Effective Achievement of Both Excellent Semantic Precision & Semantic Recall of Search Results Effective High-scale & High Performance (Google-like) Search Architecture Life Science Applications - as Operational Examples for Fast Adoption for: FEA Semantic Interoperable Search & Retrieval Platform Concept and Word, non-intrusive, Abstraction From New /Docs, to match / check-off-Append of Official Cross-FEA Meta Tags Communities of Interest – rapid deployment - Collaborations in: Avian Flu Pandemic or Environmental Toxin threats Early detection of developing medical disaster recovery problems Early detection of developing bio/medical terrorist threats Contact: Your Semantic Indexing & Search Collaborator

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