Query Formation From High-LevelConcepts for Relational Databases

Guogen ZhangWesley ChuFrank MengGladys Kong

Computer Science DepartmentUniversity of California

Los Angeles, CA http://www.cobase.cs.ucla.edu

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

Title:D:\WINNT\Profiles\fmeng\Personal\CoBase Documents\graph.epsCreator:ImageMark Software LabsPreview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.

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

runwayscan 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 SemanticModel 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, aI(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={ai | i = 1, …, n}, information measure is additive:

n

I(A) = SUM I(ai)

i = 1

Information Measure(cont.)

Initial Information Measure:all the nodes = 1different nodes have a different value

Information measure is normalized and converted into counts

Probability of a node or a link is P(ai) = ci/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_NMConstraints: AIRCRAFT_AIRFIELD_CHARS.AC_TYPE_NAME = ‘C-5’

COUNTRY_STATE.CY_NM = ‘Tunisia’Links: CAN LAND

Formulated Query

SELECT R3.APORT_NMFROM AIRCRAFT_AIRFIELD_CHARS R0

AIRPORTS R3, COUNTRY_STATE R11GEOLOC R12, RUNWAYS R16

WHERE R0.AC_TYPE_NM = ‘C-5’AND R11.CY_NM = ‘Tunisia’AND R0.WT_MIN_AVG_LAND_DIST_FT <= R16.RUNWAY_LENGTH-FTAND R0.WT_MIN_RUNWAY_WIDTH_FT <= R16.RUNWAY_WIDTH_FTAND R11.GLC_CD = R3. GLC_CDAND R3.APORT_NM = R16.APORT_NMAND R11.CY_CD = R11.CY_CD

Query Completion as GraphSearch Problem

Given: An incomplete input query topic Ti

Find a set of links to complete the topic (to make Ti connected)

Minimum Missing Information principle:The query completion candidate Tc (the missing links and nodes) for an incomplete input topic Ti 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-BoundPaths For the “CAN LAND” Query

airportsrepair(1)

2

aircrafts airportshave authorize

1 2(2)runways

can land

airports

country

geolocat is a

1 1

geolocat located

1 1

geolocis a located

1 1

airportshave

1(3)

(4)

(5)

(6)

(a) Initial components (b) 2-step-bound paths

airfield_chars

airports

runways

runways

runways

airfield_chars

airfield_chars

country

country

airports

The Semantic Graph For theTransportation Domain

airports

runwayscan land

Relation Node

at

have

is a located

2

1

1 1

1

weather

airfield_chars

geoloc country

• 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

runwayscan land

Relation Node

at

have

is a located

2

1

1 1

1

Derived Node

Derived Link

Inherited Link

airfield_chars

weather

geoloc country

airporttunisiacanland airporttunisiacanlandweather

airporttunisia

Suggesting KeyAttributes for a Query

• Find source relations for the isolated derived relation.

• Suggest key of the source relations as attributes to include.

Concept and AttributeSpecification Interface

Query Constraint Specification

Action Specification

English-Like Query Descriptionand 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