1. Discovering and Ranking Semantic Associations over a Large RDF Metabase Chris Halaschek, Boanerges Aleman- Meza, I. Budak Arpinar, Amit P. Sheth 30th International Conference on Very Large Data Bases (Demonstration Paper)

2. Semantic Association Example &r1 &r5 &r6 worksFor “Chris” “Halaschek” fname lname Semantically Connected “LSDIS Lab” name “The University of Georgia” name associatedWith

3. Motivation Query between “Hubwoo [Company]” and “SONERI [Bank]” results in 1,160 associations (query over SWETO testbed) Cannot expect users to sift through resulting associations Results must be presented to users in a relevant fashion…need ranking

4. Ranking – Overview Define association rank as a function of several ranking criteria Two Categories:  Semantic – based on semantics provided by ontology Context Subsumption Trust  Statistical – based on statistical information from ontology, instances and associations Rarity Popularity Association Length

5. Overall Ranking Criterion Overall Association Rank of a Semantic Association is a linear function Ranking Score  where k i adds up to 1.0 Allows a flexible ranking criteria k 1 × Context + k 2 × Subsumption + k 3 × Trust + k 4 × Rarity + k 5 × Popularity + k 6 × Association Length =

6. System Implementation Ranking approach has been implemented within the LSDIS Lab’s SemDIS 2 and SAI 3 projects 2 NSF-ITR-IDM Award #0325464, titled ‘SemDIS: Discovering Complex Relationships in the Semantic Web.’ 3 NSF-ITR-IDM Award #0219649, titled ‘Semantic Association Identification and Knowledge Discovery for National Security Applications.’

7. System Implementation Native main memory data structures for interaction with RDF graph Naïve depth-first search algorithm for discovering Semantic Associations SWETO (subset) has been used for data set  Approximately 50,000 entities and 125,000 relationships SemDIS prototype 4, including ranking, is accessible through Web interface 4 SemDIS Prototype: http://lsdis.cs.uga.edu/demos/

8. Interface I

9. Interface II

10. Context Specification Interface

11. Ranking Configuration Interface

12. Ranked Results Interface

13. Publications [1] Chris Halaschek, Boanerges Aleman-Meza, I. Budak Arpinar, and Amit Sheth, Discovering and Ranking Semantic Associations over a Large RDF Metabase, 30th Int. Conf. on Very Large Data Bases, August 30 September 03, 2004, Toronto, Canada. Demonstration PaperChris HalaschekBoanerges Aleman-MezaI. Budak Arpinar Amit ShethDiscovering and Ranking Semantic Associations over a Large RDF Metabase [2] Boanerges Aleman-Meza, Chris Halaschek, Amit Sheth, I. Budak Arpinar, and Gowtham Sannapareddy, SWETO: Large-Scale Semantic Web Test-bed, International Workshop on Ontology in Action, Banff, Canada, June 20-24, 2004Boanerges Aleman-MezaChris HalaschekAmit ShethI. Budak ArpinarGowtham SannapareddySWETO: Large-Scale Semantic Web Test-bed [3] Boanerges Aleman-Meza, Chris Halaschek, I. Budak Arpinar, and Amit Sheth, Context-Aware Semantic Association Ranking, First International Workshop on Semantic Web and Databases, Berlin, Germany, September 7-8, 2003; pp. 33-50Boanerges Aleman-MezaChris HalaschekI. Budak Arpinar Amit ShethContext-Aware Semantic Association Ranking