CoBase: Scalable and Extensible Cooperative Information System

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CoBase Scalable and Extensible Cooperative
Information System
Wesley W. Chu Computer Science Department Universi
ty of California, Los Angeles http//www.cobase.c
s.ucla.edu
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Conventional Query Answering

• Need to know the detailed database schema
• Cannot get approximate answers
• Cannot answer conceptual queries
• Cooperative Query Answering
• Derive approximate Answers
• Answer Conceptual Queries

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Cooperative Queries
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Generalization and Specialization
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Type Abstraction Hierarchy (TAH)
Provide multi-level knowledge representations
Chemical-Suit Size TAH (A non-numerical TAH)
All_Sizes
Small_Size
Large_Size
Very_Small
Large_to_Extra_Large
Small_to_Medium
Very_Large
XL
XXL
L
M
S
XXS
XXXS
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Type Abstraction Hierarchy (TAH)
(Location Example)
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Relaxation Agent
Use knowledge-based approach (generalization and
specialization via Type Abstraction Hierarchy) to
relax the followings for matching

• query conditions
• constraints

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Query Relaxation
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Visualization of Relaxation Process
Query Find seaports in the given region.
relaxed region
given region
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Relaxation Control Primitives

• not-relaxable
• runway-length
• relaxation-order
• (runway length, location)

• preference-list
• unacceptable-list
• answer-size
• relaxation-level

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Relaxation Primitives

• (approximate)
• 9 am
• between
• near-to (context-sensitive)
• Airport near-to LAX
• Restaurant near-to UCLA

• similar-to
• Airport similar-to LAX base-on (traffic,runway)
• within

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Similar-to
Find all airports in Tunisia similar to the
Bizerte airport based on runway length and (more
importantly) runway width.
select aport_name, runway_length,
runway_width from runways, countries where
aport_name similar-to Bizerte based-on
((runway_length 1.0)
(runway_width 2.0)) and
country_state_name Tunisia and
countries.glc_cd runways.glc_cd
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Similar-to Result
Similar-to module ranks the returned
answers according to mean-squared error.
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Unacceptable List Operator
Constraint
CoBase Relaxation Manager
Tunisia
Tunisia
Central Tunisia
SW Tunisia
NE Tunisia
Central Tunisia
SW Tunisia
NW Tunisia
...
Gafsa
El Borma
Bizerte
El Borma
Gafsa
Trimmed TAH
Type Abstraction Hierarchy
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TAH Generation for Numerical Attribute Values

• Relaxation Error
• Difference between the exact value and the
  returned approximate value
• The expected error is weighted by the probability
  of occurrence of each value
• DISC (Distribution Sensitive Clustering) is based
  on the attribute values and frequency
  distribution of the data

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TAH Generation for Non-numerical Attribute Values

• Pattern Based Knowledge Induction (PBKI)
• Rule-based approach
• Clusters attribute values into TAH based on other
  attributes in the relation (i.e.,
  Inter-Attributes Relationships)
• Provides attribute correlation value (measure how
  well the rules applied to the databases)

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Type Abstraction Hierarchy (TAH)
Provide multi-level knowledge representations
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Associative Query Answering
Provide relevant information not explicitly asked
by the user User Query List all airports with
runway length between 8500 and approximately
10000 feet
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CoBase and GLADIntegration

• Wesley W. Chu

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CoBase Functionality

• Provide approximate matching
• Find HETs with capacity of approximate 5-ton
• Provide conceptual query answering
• Find Earth Moving Equipment
• Provide content-sensitive spatial queries
• Find storage sites near selected location
• (Integration with MATT map server)
• Provide relaxation control
• Relaxation order
• Not-relaxable
• At-least (answer set, quantity on hand)

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Cooperative Operations Added to GLAD

• Implicit Query Relaxation
• Explicit Query Relaxation
• Approximate operator
• Similar-to/based-on
• Spatial relaxation
• Relaxation Control
• Relaxation-order
• Not-relaxable
• At-least (answer-set size, quantity on hand)

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CoBase Features Added to GLAD

• Enhance GLAD queries with cooperative operators
  (similar-to, relaxation-order, etc.)
• Display the query relaxation process
• modified query conditions (value, spatial)
• type abstraction hierarchies
• Rank returned answers with similarity measures
• e.g., spatial relaxation ranks answers according
  to their distance from the selected location

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CoBase and GLAD TIE
Report Collection
Spatial Area Selection
Filter Editor
Display Generator
Query Collection
NSNs
Object Cache
Report Query Constructor
CoBase Query Editor
CoBase Relaxation Manager
GLAD
Data Cache
CoBase Data Source Manager
Databases
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GLAD Query
Find NSNs of aircraft with passenger capacity gt
10, combat type 'I', capacity weight lt 2 tons
and price lt 700,000. select nsn, price,
pax_capacity_qty, capacity_wt_ston from
nsn_description where (upper(class) '7' and
upper(cbs_category_nomen) 'AIRCRAFT' and price
lt 700000 and pax_capacity_qty gt 10 and upper
(combat_type) 'I' and capacity_wt_ston lt 2)
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CoGLAD Query with Relaxation Control Operators
Find NSNs of aircrafts with passenger capacity gt
10, combat type 'I', capacity weight lt 2 tons
and price lt 700,000. Attribute
passenger capacity is not relaxable. Relax price
first and then capacity weight. select nsn,
price, pax_capacity_qty, capacity_wt_ston from
nsn_description where (upper(class) '7' and
upper(cbs_category_nomen) 'AIRCRAFT' and price
lt 700000 and pax_capacity_qty gt 10 and upper
(combat_type) 'I' and capacity_wt_ston lt
2) not-relaxable pax_capacity_qty relaxation-order
price capacity_wt_ston
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CoGLAD Querywith Similar-to Operator
Find aircraft similar to NSN '0000IB0000961'
based on the attributes price, passenger capacity
and air mileage. Passenger capacity has a weight
of 8 and price and air mileage has a weight of
1. select nsn from nsn_description where
upper(nsn) similar-to '0000IB0000961' based-on
((price 1.0) (pax_capacity_qty 8.0)
(air_mileage 1.0)) at-least 4 '0000IB0000961'
is an answer from the previous query
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CoGLAD Querywith Approximate Operator
Find DLA stock report with NSN like 8340 (FSC
for tents and tarpaulin) and on-hand quantity is
approximate 150. select nsn, ric from
dla_stock_report where nsn like 8340
and on_hand_quantity 150
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Adding Constraints to a Query
GLAD query select nsn, ric from
dla_stock_report where nsn like 8340 and
nomenclature like TARP Query with added
constraints select nsn, ric from
dla_stock_report where nsn like 8340
and nomenclature like TARP and
on_hand_quantity 150 and size_in_square_fee
t 350
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Example of Spatial Relaxation
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Spatial Relaxation with Relaxation Control

• relaxation-order size, (latitude, longitude)
• not-relaxable price
• at-least
• value size of the tarpaulin
• quantity on hand relax until enough quantity on
  hand (specified by the user) is obtained

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Scalable and Extensible CoBase Architecture
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Mediator Inter-Communications via KQML
CoBase Ontology
Module Objects APIs
Content Language Data Actions
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Query Answers Without CoBase
Query find chemical suits
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Electronic Warfare

• Identify and locate sources of radiated
  electromagnetic energy
• Determine emitter type based on the operating
  parameters of observed signals
• Radio Frequency (RF)
• Pulse Repetition Frequency (PRF)
• Pulse Duration (PD)
• Scan Period (SP)
• other operating parameters
• Determine platform sites near the line of the
  bearing of an emitter

This research is a joint effort between CoBase
and Lockheed Martin Communication Systems (Russ
Frew, et al.), Camden, NJ
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Performance Improvement by Using CoBase in EW
Conventional DB parameter ranges from emitter
specifications CoBase DB peak parameters
(RF,PRF) and parameter ranges (PD,SP) KB TAHs
based on RF and PRF peak parameters TAHs based
on PD and SP parameter ranges Case 1 emitter
signals without noise Case 2 add noise - PD SP
(10), PRF (5), RF (2.5) Sample Size 1000
signals Emitter Types 75
This research is a joint effort between CoBase
and Lockheed Martin Communication Systems (Russ
Frew, et al.), Camden, NJ
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Current CoBase Users and Applications
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Conclusions

• Provide user and context sensitive query
  relaxations (structured and unstructured data)
• Provide additional information (associative query
  answering) based on past cases
• CoSQL (Cooperative SQL)
• similar-to, near-to, approximate
• relaxation control operators
• GUI
• map server, high-level query formation

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CoSent An Active Data Base Technology

• Natural language-like rule supports conceptual
  approximate terms
• Decompose natural language-like rule to low level
  rules via knowledge based (TAH)
• Mimic human cognitive process and thus ease in
  rule specification
• Ease in rule maintenance

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CoSent An Active Database Technologies
CoSent monitors temporal composition events and
executes rules with conceptual and approximate
terms.

• Trigger with high-level rules containing
• conceptual term (e.g., bad, heavy) and
• approximate operators (e.g., similar-to, near-to,
  approximate)
• Allow trigger conditions to be specified with
  fuzzy and conceptual terms
• Mimic human cognitive expression

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Key Features of CoSent

• User defined rules transformed into low-level
  range values via knowledge base--Type Abstraction
  Hierarchies (TAHs)
• TAHs are typically generated from data sources
  automatically
• Leveraged on conventional DBMS (e.g., Oracle,
  Sybase, Teradata) triggering systems
• Rule definition is either specified by domain
  expert or derived by data mining technologies

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Example of Rule Definitions with Data Mining
Technology

• Find attributes that frequently appear together
  for a given target attribute.
• If bad road condition and also bad weather, then
  cause traffic congestion.
• If a person wrote many bad checks and also has
  past eviction, then this person is a poor credit
  risk.
• Based on the frequency of occurrence, the derived
  rules can be ranked according to certain
  information measure.

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Conventional vs. Natural Language-Like Rules
Conventional Rule If wind_speed gt MAX_WIND_SPEED
and wave_height gt MAX_WAVE_HEIGHT then notify
affected units in regions.

• Natural Language-Like Rul
• If the weather turns bad,
• then notify all affected units in that region and
  all those that are near to that region.

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Natural Language-Like Rule Specifications
Example 1 If the number of departures of large
cargo carrier (e.g., C-5, C-141) becomes
significantly low in the past seven days, notify
the Air Mobility Command.
Example 2 If the aircraft has a fuel
contamination problem and the aircraft type is
similar-toC-5 based on the fuel type and
fueling method, then notify the authority
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Example
DoD Transportation PlanningWeather Report Table
Wind Speed is the hourly average over an
eight-minute period for buoys and a two-minute
period for land stations Wave height is sampled
in a 20-minute period
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TAH Example
Wave Height
Wave Height 0.6, 7.2
VERY HIGH 2.45, 7.2
LOW 1.25, 1.75
HIGH 1.75, 2.45
VERY LOW 0.6, 1.25
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A Portion of Wave Height TAH
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Triggering Based on Temporal Composite Events
Notify the commander if within the past seven
days, the total departure of C-5 is significantly
low and the filter problem on C-5 is extremely
high.
C-5 Departure
C-5 Filter Problem
Low 9-134.5
High 134.5-208
High 53-79
Low 0-53
Ex High 60-79
Signt. Low 9-53
Very High 134.5-162
Extra. Low 0-36
Very High 53-60
Very Low 53-134.5
Signt High 162-208
Very Low 36-53
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Natural Language-Like Rule Translations
Rule Translation/Relaxation
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CoSent Architecture
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CoSent Demo

• Natural Language-like rule with conceptual terms
  very high wave height and very strong wind
  speed
• Natural language-like rule with approximate term
  nearby and conceptual term bad weather
• Install trigger by drag-and-drop on the desired
  location on the map

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Natural Language-Like Rule

• Natural language-like rule containing conceptual
  terms, such as wave_height very-high and
  wind_speed very-strong, can be translated to
  range values by domain knowledge. For instance,
  type abstraction hierarchy.
• Natural language-like rules reduce the number of
  rules, thus easing rule maintenance

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Rules With Approximate Terms

• Rules can contain approximate terms, such as
  near-by and approximate, thus ease in rule
  specification
• The Trigger can be installed on the desired
  location on a map by drag-and-drop method
• The near-by region affected by the bad weather
  condition is specified by the trigger condition
  shown by a red circle

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Map Server Architecture
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Current Capabilities of Map Server

• Visualization of Query Answers
• Icons
• Paths
• Enter Query Constraints Graphically
• Visualization of Query Relaxation Process

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Visualization of Relaxation Process
Query Find seaports in the given region.
relaxed region
given region
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Explanation Agent

• Based on process traces and invocation rules,
  generate English-like explanation of
• Relaxation process
• Quality of approximate matching
• Further explanation on definitions and terms in
  explanation

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Explanation of Relaxation Process
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Relaxation Primitive within
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Extend near-to Primitive Points to Regions
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Dynamic Nearness

• Uses transaction history to identify nearness
  between tuples and values
• If two tuples (or attribute values) appear
  together in a query answer, then that is a piece
  of evidence that they should be clustered
  together.
• Gather evidence over time
• Evolve the hierarchy

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The BOOKS Relation
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Schematic of a Browsing System
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Schematic of a Query Modification System
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The Links Between Tuples in BOOKS
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Dynamic Links After Two Queries
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Links with Counts
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Number of Links with Threshold Value q
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Number of Links is determined by Maximum Answer
Set Size a
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Query Formation From High-LevelConcepts for
Relational Databases

• Guogen Zhang
• Wesley Chu
• Frank Meng
• Gladys Kong

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Outlines

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

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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

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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

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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

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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

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A query topic for aircraft can land on airports
at geographical locations of countries
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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.

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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

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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 Aai i 1, ,
  n, information measure is additive
• n
• I(A) SUM I(ai)
• i 1

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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(ai) ci/c
• Update Information measure
• Ranking based on Information measure, thus adapt
  to user feedback

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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

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Formulated Query

• SELECT R3.APORT_NM
• FROM AIRCRAFT_AIRFIELD_CHARS R0
• AIRPORTS R3, COUNTRY_STATE R11
• GEOLOC R12, RUNWAYS R16
• WHERE R0.AC_TYPE_NM C-5
• AND R11.CY_NM Tunisia
• AND R0.WT_MIN_AVG_LAND_DIST_FT lt
• R16.RUNWAY_LENGTH-FT
• AND R0.WT_MIN_RUNWAY_WIDTH_FT lt
• 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

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Query Completion as Graph Search 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

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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)

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Initial Components and 2-Step-BoundPaths For the
CAN LAND Query
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The Semantic Graph For theTransportation Domain
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Incremental Query Formulation

• 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

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Incremental Query Examples

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

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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

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Extended semantic graph showing derived nodes,
derived links and inherited links
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Suggesting Key Attributes for a Query

• Find source relations for the isolated derived
  relation.
• Suggest key of the source relations as attributes
  to include.

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Concept and Attribute Specification Interface
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Query Constraint Specification
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Action Specification
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English-Like Query Descriptionand the Formulated
Query
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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

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