Traversing Documents by Using Semantic Relationships
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Traversing Documents by Using Semantic Relationships Presenter: Bilal Gonen
description
Traversing Documents by Using Semantic Relationships. Presenter: Bilal Gonen. Semantic Browser. Semantic browser is a tool that supports browsing and navigation of a document space by utilizing the semantic relationships. Physical Links vs. Virtual Links. affects. co_occurs_with. - PowerPoint PPT Presentation
Transcript of Traversing Documents by Using Semantic Relationships
Traversing Documents by Using Semantic Relationships
Presenter: Bilal Gonen
Semantic Browser
• Semantic browser is a tool that supports browsing and navigation of a document space by utilizing the semantic relationships.
Physical Links vs. Virtual Links
co_occurs_with
analyzesaffects
is_result_of
href
href
href
href
affects
affects
affects
co_occurs_withco_occurs_with
co_occurs_with
co_occurs_with
co_occurs_withis_result_of
is_result_of
is_result_of
is_result_of
A Real Example
How are these articles related?
How do we find other documents related with “melanoma”?
One common option is to use statistical techniques.
Recommendation Systems
Amazon, the best-known commercial recommender system, recommends books to customers based on the statistical similarity between customers' previous purchases.
The product: Digital Camera
Customers who bought this item also often bought
Digital Memory Card
Statistical proximity
A Real Example
Such a statistical technique may return these terms.
sun's harmful raysskinskin cancerlegankleskin pigmentmelaninaneuploidy
There are no named relationships.
Ontology
Pathologic Function
Body Substance
Substance
Diagnostic Procedure
Amino Acid Peptide or
ProteinDisease or Syndrome
Neoplastic Process
affects
Instance level
Schema level
Ureteral Calculi
Kidney Neoplasms
InstanceOf InstanceOf InstanceOf
analyzes
rdfs:subClassOfrdfs:subClassOf
rdfs:subClassOf
Ontology is at the heart of Semantic Web.
Relationships In Ontology
breast cancer bone cancer
non-melanoma melanoma
blood cancerskin cancer
cancers
aneuploidy euploidymonoploidy
chromosomal disorder
is_result_of
A Real Example
Our approach is to offer several relationships to the user.
aneuploidyallelic imbalancechromosome aberrations
This is what user is interested in.
affectsco_occurs_with
occurs_in
is_result_of
Return files which includes “aneuploidy”
Chromosomal AneuploidiesIdentification of AneuploidyDefinition of AneuploidyAneuploidy and Deletions
Name of files in which “aneuploidy” occurs.
JSP(Java Server Page)
Java Script
AJAXuser
Lucene Index for documentsPubMed
dataset
Ontology SemDis API
Lucene indexing is used to index the documents with the 21,945 MESH terms when they occur in the documents.
User Interface(HTML page)
The advantage of the AJAX technology is to send and receive only needed information between the client and server.
request
Built in LSDIS Lab. This API is used to process the triples in the ontology.
Contains 135 classes and 49 relationships in schema level. And 21,945 entity instances in the instance level
Contains 48,252 documents
Because we also used the synonyms of the 21,945 MESH terms, therefore we used ~104,000 terms to index the documents.
AJAX (Asynchronous JavaScript And XML)
AJAX (Asynchronous JavaScript And XML)
Only this part is loaded to the client side.
User Interface(HTML page)
The MESH term is sent to server as a request to get its types from the ontology by using the SemDis API.
Java Script
AJAX
JSP(Java Server Page)
user
Lucene Index for documentsPubMed
dataset
Ontology SemDis API
SemDis API gets the types of the instance term from the ontology.
request
response
keyword
related documents
List of the documents are returned from the Lucene index.
