Query Formation From High-Level Concepts for Relational Databases Guogen Zhang Wesley Chu Frank Meng Gladys Kong Computer Science Department University of California Los Angeles, CA

Outlines Overview Semantic Graph Model High-Level Query Formation for SPJ queries Incremental Query Formation for Complex Queries Conclusions

Overview: Query Formation Based on semantic graph model, including user-defined relationships User specifies requests and constraints Formulate simple query by graph search technique –Candidates ranked by information measure –English-like query description A complex query can be formulated by a series of simple queries

Related Work Query formulation as Steiner tree problem (Wald and Sorenson, 1984) –limited to partial 2-tree graphs Formulate simple Select-Project-Join (SPJ) queries via Universal Relation Model: no need to specify natural joins (Ullman 1988, Vardi, 1988) Object-oriented query path expression completion: partial order relationship between different path for ranking (Ioannidis and Lashkari, 1994) Query-by-Icon (QBI) [Massari and Chrysanthis, 1995] Natural language interfaces (text/voice): logical form to query

Semantic Graph Model Weighted graph G=(V,E): Nodes: entities -- strong, weak, user-defined Links: relationships -- ISA, HAS, simple, complex, user-defined –For relational databases: nodes: relations links: natural and user-defined joins Weight: information measure of a node or link

Semantic Graph Example

Query Feature Query expression in a semantic graph –Query Topic, T : A set of Joins represented by links –Query Constraints, C : Query Conditions –Query Aspect, A : Attribute list

A query topic for “aircraft can land on airports at geographical locations of countries” airports runways can land have is a located airfield_chars geoloc country

Semi-Automatic Generation of Semantic Model Find natural joins through key and foreign key between nodes. User-defined links can be added into the graph model. Designers need to specify link types and assign names to all the elements in the graph.

Example of Semantic Model Generation AIRPORT: APORT_NM, GEOLOC_TYPE, GLC_CD, ELEV_FT, …; key: APORT_NM. RUNWAY: APORT_NM, RUNWAY_NM, GLC_CD, RUNWAY_LENGTH_FT, RUNWAY_WIDTH_FT, …; key: RUNWAY_NM. GEOLOC: GLC_CD, GLC_NM, CY_CD, LATITUDE, LONGITUDE, …; key: GLC_CD. COUNTRY: CY_CD, CY_NM, …; key: CY_CD. Links: AIRPORT--RUNWAY: APORT_NM; AIRPORT--GEOLOC: GLC_CD; RUNWAY--GEOLOC: GLC_CD; GEOLOC--COUNTRY: CY_CD;

Information Measure Information measure of a node or link, a I ( a ) = - log P ( a ) where P ( a ) is the probability of a being used in queries. Assume nodes and links are independent, for a subgraph with a set of elements A ={ a i | i = 1, …, n }, information measure is additive: n I ( A ) = SUM I ( a i ) i = 1

Information Measure (cont.) Initial Information Measure: all the nodes = 1 different nodes have a different value Information measure is normalized and converted into counts Probability of a node or a link is P ( a i ) = c i /c Update Information measure Ranking based on Information measure, thus adapt to user feedback

Query Formulation To formulate (simple) queries without knowledge of query language or database schema Example: Find airports in Tunisia that can land a C-5 cargo plane User input: Query aspect: AIRPORTS.APORT_NM Constraints: AIRCRAFT_AIRFIELD_CHARS.AC_TYPE_NAME = ‘C-5’ COUNTRY_STATE.CY_NM = ‘Tunisia’ Links: CAN LAND

Formulated Query SELECT R3.APORT_NM FROMAIRCRAFT_AIRFIELD_CHARS R0 AIRPORTS R3, COUNTRY_STATE R11 GEOLOC R12, RUNWAYS R16 WHERER0.AC_TYPE_NM = ‘C-5’ AND R11.CY_NM = ‘Tunisia’ AND R0.WT_MIN_AVG_LAND_DIST_FT <= R16.RUNWAY_LENGTH-FT AND R0.WT_MIN_RUNWAY_WIDTH_FT <= R16.RUNWAY_WIDTH_FT AND R11.GLC_CD = R3. GLC_CD AND R3.APORT_NM = R16.APORT_NM AND R11.CY_CD = R11.CY_CD

Query Completion as Graph Search Problem Given: An incomplete input query topic T i Find a set of links to complete the topic (to make T i connected) Minimum Missing Information principle: The query completion candidate T c (the missing links and nodes) for an incomplete input topic T i contains the minimum information

Query Formulation Algorithm Input: subgraph T of the semantic graph G –Find candidates with the minimum Information measure Two methods used to limit the search scope: –L-step-bound paths: paths that connect two components with at most L links, to limit search within the neighborhood of the input subgraph –k-minimum completion candidates: only at most k candidates with minimum Information measure are kept (alpha-beta pruning)

Initial Components and 2-Step-Bound Paths For the “CAN LAND” Query airports repair (1) 2 aircraftsairports haveauthorize 12 (2) runways can land airports country geoloc atis a 11 geoloc atlocated 11 geoloc is alocated 11 airports have 1 (3) (4) (5) (6) (a) Initial components (b) 2-step-bound paths airfield_chars airports runways airfield_chars country airports

The Semantic Graph For the Transportation Domain airports runways can land Relation Node at have is a located weather airfield_chars geoloccountry

Incremental Query Formulation –To assist user reach a complex query goal with a series of simple queries –The subsequent queries may depend on results of preceding queries (derived relations) Issues –Incorporate derived relations into the semantic graph –Suggest missing attributes to link isolated derived nodes to the graph Incremental Query Formulation

Incremental Query Examples Find airports in Tunisia. Which of these airports can land a C-5? What is the weather at these airports?

Incorporating Derived Relations Source relation: contributes attributes to the derived relations Derived relation: inherits properties of attributes from their source relations Deriving link: links to the source relations through inherited keys Inherited link: inherits links from the source relations

Extended semantic graph showing derived nodes, derived links and inherited links airports runways can land Relation Node at have is a located Derived Node Derived Link Inherited Link airfield_chars weather geoloc country airporttunisiacanlandairporttunisiacanlandweather airporttunisia

Suggesting Key Attributes for a Query Find source relations for the isolated derived relation. Suggest key of the source relations as attributes to include.

Concept and Attribute Specification Interface

Query Constraint Specification

Action Specification

English-Like Query Description and the Formulated Query

Conclusions Semantic graph model provides a basis for query formulation search Ranking of query candidates by information measure in formulation provides adaptive behavior Incremental query formulation is effective for complex queries GUI and voice interface can be built for query formulation from high-level concepts