Keyword Searching and Browsing in Databases using BANKS Seoyoung Ahn Mar 3, 2005 The University of Texas at Arlington

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Outline  Introduction  Database and Query Model  Searching for the best answers  Browsing features of BANKS  Experiment  Conclusion

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Introduction  Search engines on Web have popularized an unstructured querying and browsing Simple and user-friendly Users just type in keywords and follow hyperlink  Relational databases are commonly searched using structured query language Users need to know the schema  Keyword searching techniques cannot be used on data stored in databases It often splits across the tables/tuples due to normalization

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Introduction(cond..)  BANKS (Browsing And Keyword Searching) a system which enables keyword-based search on relational databases, together with data and schema browsing User BANKS system Database HTTPJDBC

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Introduction(cond..)  BANKS (Browsing And Keyword Searching) a framework for keyword querying of relational database a novel and efficient heuristic algorithm for executing keyword queries key features of BANKS system

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Outline  Introduction  Database and Query Model Informal Model Formal Model Query and Answer Model  Searching for the best answers  Browsing features of BANKS  Experiment  Conclusion

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Database and Query Model  Informal Model Model Description each tuple in db fk-pk-Link database node in the graph directed edge directed graph   

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Database and Query Model The Schema

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Database and Query Model A Fragment of the Database

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Database and Query Model  Informal Model(cond.) An answer to a query should be a subgraph connecting nodes matching the keywords. The importance of a link depends upon the type of the link i.e. what relations it connects and on its semantics Ignoring directionality would cause problems because of “hubs” which are connected to a large numbers of nodes.

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Database and Query Model  Informal Model(cond.) We may restrict the information node to be from a selected set of nodes of the graph We incorporate another interesting feature, namely node weights, inspired by prestige rankings Node weights and tree weights need to be combined to get an overall relevance score

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Database and Query Model  Formal Database Model Nodes and edges Node Weight : N(u) Depends on the prestige Set the node prestige = the indegree of the node Nodes that have multiple pointers to them get a higher prestige Node score N = root node weight + ∑ leaf node weight

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Database and Query Model  Formal Database Model (Cond.) Edge Weights Some pupluar tuples can be connected many other tuples  Edge with forward and backward edge weights Weight of a forward link = the strength of the proximity relationship between two tuples (set to 1 by default) Weight of a backward link = indegree of edges pointing to the node Total edge weight = ∑ edge weights Edge score E = 1 / Total edge weight

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Database and Query Model  Formal Database Model (Cond.) Overall relevance score = Node weights + Edge Weight Normalize in the range [0,1] Combine using weighting factor Additive: (1- ) E + N; multiplicative: E * N

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Database and Query Model  Query and Answer Model Query A set of keywords e.g.{k 1,k 2,…k n } A set of nodes S i = {S 1,S 2,…S n } Locate nodes matching search terms t 1,t 2,…t n Answer Model A rooted directed tree connecting keyword nodes Relevance score of an answer tree Relevance scores of it nodes and its edge weight

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Database and Query Model  Answer Model A rooted directed tree connecting keyword nodes Multiple answers Ranked by proximity + prestige Proximity  edges weights Prestige  indegree of nodes Relevance score of an answer tree Relevance scores of it nodes and its edge weight

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Database and Query Model Result of query “sudarshan soumen”

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Outline  Introduction  Database and Query Model  Searching for the best answers Backward expanding search algorithm  Browsing features of BANKS  Experiment  Conclusion

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Searching for the best answers  Backward expanding search algorithm Offers a heuristic solution for incrementally computing query results. Assume that the graph fits in memory Start at leaf nodes each containing a query keyword Run concurrent single source shortest path algorithm from each such node Traverses the graph edges backwards Confluence of backward paths identify answer tree roots Output a node whenever it is on the intersection of the sets of nodes reached from each keyword Answer trees may not be generated in relevance order Insert answers to a small buffer (heap) Output highest ranked answer from buffer to user when buffer is full

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Searching for the best answers Model (Query : Charuta Sudarshan Roy ) S. SudarshanPrasan Roy writes author paper Charuta BANKS: Keyword search…

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Outline  Introduction  Database and Query Model  Searching for the best answers  Browsing features of BANKS  Experiment  Conclusion

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Browsing  BANKS system provides A rich interface to browse data stored in a relational database Automatically generates browsable views of database relations and query results Schema browsing and data browsing A hyperlink to the referenced tuple Templates for several predefined ways of displaying data

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Browsing Data browsing

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Browsing Schema browsing

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Outline  Introduction  Database and Query Model  Searching for the best answers  Browsing features of BANKS  Experiment  Conclusion

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Error scores vs parameter choices  The rankings are relatively stable across different choices of parameter values  = 0.2 coupled with log scaling of edges weights does best

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Outline  Introduction  Database and Query Model  Searching for the best answers  Browsing features of BANKS  Experiment  Conclusion

Seoyoung AhnKeyword Searching and Browsing in Databases using BANKS Conclusion  BANKS system provides an integrated browsing and keyword querying system for relational databases allows users with no knowledge of database systems or schema to query and browse relational database with ease