Keyword Searching and Browsing in Databases using BANKS

1. Keyword Searching and Browsing in Databases using BANKS Charuta Nakhe, Arvind Hulgeri, Gaurav Bhalotia, Soumen Chakrabarti, S. Sudarshan Presented by Sushanth Sivaram Vallath

3. Motivation • Keyword search of documents on the web as been enormously successful • Simple and intuitive, no need to learn any query language • Database querying using keywords is desirable • SQL is not appropriate for casual users • Form interfaces cumbersome: • Require separate form for each type of query – confusing for casual users of Web information systems • Not suitable for ad hoc queries

4. Motivation • Many Web documents are dynamically generated from databases • E.g. Catalog data • Keyword querying of generated Web documents • May miss answers that need to combine information on different pages • Suffers from duplication overheads

5. Examples of Keyword Queries • Airticket reservation database • “DFW LAX” • University database • Info on courses • Online shopping • Canon Digital Rebel

6. Differences from IR/Web Search • Related data split across multiple tuples due to normalization • Different keywords may match tuples from different relations

7. Schema

8. Basic Model • Database: modeled as a graph • Nodes = tuples • Edges = references between tuples • foreign key • Edges are directed.

9. The BANKS Answer Model • Query: set of keywords {k1, k2, .., kn} • Each keyword ki matches set of nodes Si • Answer: rooted, directed tree connecting nodes, with one node from each Si • Root node has special significance, may be restricted to some relations • May include intermediate nodes not in any Si and hence a steiner tree. • Multiple answers • Ranking based on proximity + prestige

10. Edge Directionality • Some popular tuples are connected to many other tuples • E.g. Students -> departments -> university • Popular tuples would create misleading shortcuts from every tuple to every other • E.g. every student would be closely linked with every other student via the department/university • Solution: define different forward and backward edge weights • Forward edges: In the direction of the foreign key reference

11. Node Weight • Nodes have prestige weights too • nodes with greater prestige tend to have greater indegree

12. Finding Answer Trees • Backward Expanding Search Algorithm: • Intuition: find vertices from which a forward path exists to at least one node from each Si. • Run concurrent single source shortest path algorithm from each node matching a keyword • Create an iterator for each node matching a keyword • Traverse the graph edges in reverse direction • Output a node whenever it is on the intersection of the sets of nodes reached from each keyword

13. Finding Answer Trees • Backward Expanding Search • Intuition: travel backwards from keyword nodes till you hit a common node Query: sudarshan roy MultiQuery Optimization paper writes Sudarshan Prasan Roy authors

14. References • Keyword Searching and Browsing in Databases using BANKS • Keyword Searching and Browsing in Databases using BANKS(PPT)

15. Thank You