1. Traversing Documents by Using Semantic Relationships Presenter: Bilal Gonen

2. Semantic Browser Semantic browser is a tool that supports browsing and navigation of a document space by utilizing the semantic relationships.

3. Physical Links vs. Virtual Links co_occurs_with analyzes affects is_result_of href affects co_occurs_with is_result_of

4. 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.

5. 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

6. A Real Example Such a statistical technique may return these terms. sun's harmful rays skin skin cancer leg ankle skin pigment melanin aneuploidy There are no named relationships.

7. Ontology Pathologic Function Body Substance Substance Diagnostic Procedure Amino Acid Peptide or Protein Disease or Syndrome Neoplastic Process affects Instance level Schema level Ureteral Calculi Kidney Neoplasms InstanceOf analyzes rdfs:subClassOf Ontology is at the heart of Semantic Web.

8. Relationships In Ontology breast cancer bone cancer non-melanoma melanoma blood cancer skin cancer cancers aneuploidy euploidy monoploidy chromosomal disorder is_result_of

9. A Real Example Our approach is to offer several relationships to the user. aneuploidy allelic imbalance chromosome aberrations This is what user is interested in. affects co_occurs_with occurs_in is_result_of Return files which includes “aneuploidy” Chromosomal Aneuploidies Identification of Aneuploidy Definition of Aneuploidy Aneuploidy and Deletions Name of files in which “aneuploidy” occurs.

10. JSP (Java Server Page) Java Script AJAX user Lucene Index for documents PubMed 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.

11. AJAX (Asynchronous JavaScript And XML)

12. Only this part is loaded to the client side.

13. 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 documents PubMed 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.

34. Questions, Comments Thank you… Email: bilalgonen@gmail.com Web: www.bilalgonen.com