Approximate Semantic Matching of Heterogeneous Events

Digital Enterprise Research Institute www.deri.ie

Approximate Semantic Matching of
Heterogeneous Events
Souleiman Hasan, Sean O’Riain, Edward Curry
Digital Enterprise Research Institute (DERI)
National University of Ireland, Galway (NUIG)
In proceedings of DEBS 2012, Berlin, Germany

Stefan.Decker@deri.org
http://www.StefanDecker.org/

© Copyright 2010 Digital Enterprise Research Institute. All rights reserved.

Further Reading

Hasan S, O’Riain S, Curry E.
Approximate Semantic Matching of Heterogeneous Events. In:
6th ACM International Conference on Distributed Event-Based
Systems (DEBS 2012)

www.edwardcurry.org

Outline

n  Introduction n  Experiments
¨  Smart Environments ¨  Wikipedia
¨  Motivational Scenario ¨  Freebase

¨  Related Work n  Conclusions
n  Proposal n  Q&A
¨  Approximate Semantic
Matching

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Smart Environments

n  Smart Homes, Grids, Cities…
n  Internet-of-Things, Sensor Web…
by 2020 50 billion devices connected to mobile networks (OECD, 2012)

n  Non-technical users
n  High heterogeneity
n  Trend for dynamic data-driven decision making
Event/Situation of Interest
Event/Situation of Interest Soccer match played in Berlin
New free parking space near me
........

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Motivational Scenario- Enterprise
CIO
CSO
Situation of Interest
Company CO2 emissions
performance Energy usage by
global IT
department
Helpdesk
Various terms used:
energy consumption,
energy usage…. PUE of the
Data Center in
room, space, zone…
Dublin
Maintenance Personnel

Dynamic Environments:
New events from kWhs used by
equipments joining and server
leaving 172.16.0.8

Building

Data Center

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Requirements

n  Handling of semantically heterogeneous
events
n  Handling of dynamic environments with
event types by sources joining and leaving
n  Low cost of rules management
n  Usability
n  Precision

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Event Processing

Situation of Interest
When a floor is empty and its energy usage for an hour is above
threshold w.r.t budget then it is an excessive usage
Non-technical users with User
Translation
Developer
natural language needs
CEP Engine Separated from the engine

UI
Rules tied RULE vocabulary
EVENT PROCESSING to

EPL Interface
Rules

Repository
and Parser

Execution
INSERT INTO ExcessiveEnergyUsageByFloor Pattern Matcher Repository
SELECT a.floor as floor case of
High cost in
heterogeneity or change
FROM PATTERN
Single Event Templates
[(a=FloorEmptySensor -> every b=DeviceEnergyUsageSensor
Matcher Repository
(a.floor=b.floor))]
.WIN:TIME(1 hour)
GROUP BY a.floor
WHERE (b.usage) > GetAcceptableThreshold(a.budgetValue) ERP
PC NO XDG26359
Floor: 1st
usage: 3 kWh

VM: vmdgsit01.deri.ie
Floor: 1st BMS
usage: 15 kWh

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Exact Event Processing Paradigm

Requirement Addressing by the paradigm
Semantic Heterogeneity Does not scale out to high
heterogeneous environments
Dynamic Environment Does not scale out to high dynamic
environments
Rule Management High cost on large heterogeneity and
dynamicity
Usability Low
Precision 100% (typically)

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Decoupling in Event Systems

n  Space Producers and consumers don’t know each other
n  Time Participants don’t need to be actively involved in the
interaction the same time

n  Synchronization Event producers and consumers don’t get
blocked to send/receive events
Space

Time
Event Event
Producer Consumer
Synchronization

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Decoupling in Event Systems

n  Principle
¨  “Removal of explicit dependencies between
participants” (Eugster et al., 2003)
n  Outcome
¨  Scalability
Space

Time
Event Event
Producer Consumer
Synchronization

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Semantic Coupling

n  Current event-based systems keep explicit semantic
dependency between participants
n  Limited scalability in highly heterogeneous and
dynamic environment
Space

Time
Event Event
Producer Consumer
Synchronization

Semantic
(Event types, property, values)

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Current Approaches

n  Ontology-based
¨  (Petrovic et al., 2003), (Zhang & Ye, 2008)…
¨  Does not “remove explicit dependency”
¨  Hard to achieve ontology agreement a priori at large-scale
of heterogeneity and dynamicism
¨  Medium usability, 100% precision typically
n  Fuzzy sets
¨  (Liu & Jacobsen, 2002)
¨  Address only event numerical values vs. string values
subscriptions
¨  Medium usability, High precision

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Proposed Approach

n  Approximate semantic matching of events
Event Types & properties
Type(s) possible mappings
Properties
Values

Subscription Values possible
Type(s) mappings
Properties
Values
Pick best overall
mapping

Post-matching event
processing

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Background

q  Semantic Similarity
q  f: Terms X Terms à [0,1]
q  term1, term2 are Terms
q f(term1, term2)=0 absolute semantic mismatch
q f(term1,term2)=1 exact match
q  E.g. Football Match and Soccer Match are similar
q  Relatedness: a general case of similarity
q  E.g. Football Match and Referee related but not similar
q  Thesaurus-based: e.g. WordNet-based
q  Distributional semantics-based: e.g. Wikipedia ESA
q  The more Wikipedia articles two terms occurs in, the more
related they are

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Proposed Approach Instantiation

Football Match Types & properties
possible mappings
2010 FIFA World
Howard Webb
type Cup Final
referee name Values possible
mappings
Spain National event
team
Football Team
team Pick best overall
location Netherlands National mapping
location Football Team
Johannesburg
Post-matching event
FNB stadium processing

Subscription
Event type “”Soccer Match
Event team “Spain”
Event place “South Africa”

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Event Subscription Types & properties
possible mappings
type type
name place Values possible
referee team mappings
team
location Pick best overall
mapping

1

0.9
Lin

0.8
Post-matching event
0.7
Jiang&Conrath
processing
Precision

0.6

0.5
Leacock&Chodorow

0.4

Lesk

0.3

0.2
Path

0.1

0
Resnik

0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1

Gloss
Vector

Recall

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possible mappings
type type
team
mapping

Determine top m correspondence candidates Post-matching event
RankSimJiiang&Conrath(ps, pe) processing

Measure properties relatedness
fP=Min(1,m-RankSimJiiang&Conrath(ps, pe) +1)*WikipediaESA(ps, pe))

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possible mappings
type type
team
mapping

type type Top 1
location 90% place Post-matching event
team team processing

type type Top 2
name 40% place
referee team

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possible mappings
Football Match
Howard Webb
Soccer Match
Spain National Football Team South Africa Values possible
Johannesburg
FNB stadium
Spain mappings

Netherlands National Football Team

Pick best overall
mapping

Measure values relatedness fV=WikipediaESA(Vs, Ve)
Post-matching event
processing

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possible mappings
Football Match
Howard Webb
Soccer Match
Spain National Football Team South Africa Values possible
Johannesburg
FNB stadium
Spain mappings


Pick best overall
mapping

Spain National 95% Spain
Football Team Post-matching event
processing

Netherlands National 30% Spain
Football Team

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possible mappings
type type
team
mapping
Football Match
Howard Webb
Soccer Match
Spain National Football Team South Africa Post-matching event
Johannesburg
FNB stadium
Spain processing


Calculate statements relatedness
fSTMT =fP(ps, pe)*fV(vs, ve)

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possible mappings
type type
team
mapping
Football Match
Howard Webb
Soccer Match
Spain National Football Team South Africa Post-matching event
Johannesburg
FNB stadium
Spain processing


Determine correspondent event statement
Corre by Max fSTMT

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Types & properties
n  Rank within a window possible mappings

n  Complex Event Processing
Values possible
n  … mappings

Pick best overall
mapping

Post-matching event
processing

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Experiments Overview

n  Methodology
¨  Prepare an event set that reflect required semantic
heterogeneity (Wikipedia events)
¨  Prepare gold standard set of subscriptions that stress
multiple aspects of semantic coupling
¨  Validate suitability of semantic approximation from
precision perspective
¨  Use a different event set and same subscriptions to
validate low maintainability cost (Freebase events)
n  Evaluation Criteria
¨  Average interpolated Precision-Recall Curve on 11 recall
points
¨  Maximal F1 Score over the average curve

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Experiment 1- Wikipedia Events

Event Set Statistics
Source structured Wikipedia Infoboxes,
DBpedia 31 August 2011
Collection Triples directly associated to instances
of dbpedia-owl:Event class
Data model RDF
Total # of events 20,156
Total # of distinct event types 4,950
Total # of distinct event properties 1,459
Total # of distinct event values 500,717
Total # of triples 1,502,599
Average # of distinct type per event 7.42
Average # of distinct property per event 30.52
Average # of distinct value per event 54.16
Average # of triple per event 64.67

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Experiment 1- Wikipedia Events

n  Example Event Types
¨  Football Match
¨  Race
¨  Music Festival
¨  Space Mission
¨  Election
¨  10th-Century BC Conflicts
¨  Academic Conference
¨  Aviation Accident
¨  …

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Experiment 1- Subscription Set

n  Manually created gold standard set of subscriptions
ID Description Subscription # of # of Event type Event Literals and
relevant needed approximation properties resources
events exact approximation approximation
rules

1 Football matches event type "Football Match" 1 1 NO NO NO
played by Spain in event team "Spain national football
the FNB stadium team"
event stadium "FNB Stadium"
2 Football matches event type "Football Match" 2 2 NO YES NO
played in the FNB event place "FNB Stadium"
stadium
3 Events taking place in event type "Event" 219 5 NO YES Syntactic
Wembley stadium event place "Wembley Stadium"
4 Charity events taking event type "Charity" 29 6 YES YES Semantic
place in Wembley event place "Wembley Stadium" + Syntactic
stadium
5 Charity Rock events event type "Charity" 2 2 YES YES Semantic
taking place in event type "Rock" + Syntactic
6 Football matches event type "Football Match" 505 603 NO YES Background
played in the UK event stadium "United Kingdom" Knowledge
7 Football matches event type "Football Match" 20 123,774 NO YES Background
played by a South event team "South America" Knowledge
American team in event stadium "Europe"
Europe

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approximation

approximation

approximation
n  Manually created gold standard set of subscriptions

# of relevant
Subscription

# of needed

Literals and
Description

exact rules

Event type
ID Description Template # of # of Event type Event Literals and

properties

resources

events
rules

Event
1 Football matches event type "Football Match" 1 1 NO NO NO
ID

played by Spain in event team "Spain national football
the FNB stadium team"
3 Events taking event type
219 5 NO YES Syntactic
place in "Event"
2 Football matches event type "Football Match" 2 2 NO YES NO
Wembley
stadium event place
3
stadium
Events taking place in
Wembley stadium
"Wembley
event type "Event"
event place "Wembley Stadium"
219 5 NO YES Syntactic

4 Charity events
Stadium"
event type "Charity" 29 6 YES YES Semantic
taking place in event place "Wembley Stadium" + Syntactic
Wembley stadium event type "Event"
Subscription events
5 Charity Rock event place "Wembley Stadium"
event type "Charity" 2 2 YES YES Semantic
taking place in event type "Rock" + Syntactic
Wembley stadium ?event rdf:type dbpedia-owl:Event.
event place "Wembley Stadium"
SPARQL pattern 1
6 Football matches ?event dbpprop:stadium
event type "Football Match" 505 dbpedia:Wembley_Stadium.
603 NO YES Background
?event rdf:type dbpedia-owl:Event.
SPARQL pattern 2
7 Football matches event type "Football Match" 20 123,774 NO YES Background
played by a South ?event dbpedia-owl:location
event team "South America" dbpedia:Wembley_Stadium.
Knowledge
… Europe …

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Experiment 1- Results

1

0.9

0.8

0.7

Precision

0.6

0.5
Events taking place in Wembley stadium
0.4

0.3
Need for a hybrid matcher that
0.2

0.1
combines both
0

0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1

Recall

45%
Jiang&Conrath
40% Wikipedia
ESA

35%
Frequency

30%
25% 1

20% 0.9

15% 0.8

10% 0.7

Precision

5%
0.6

0.5

Football matches played in the UK
0%
0.4

0 2^ -25 2^ -20 2^ -15 2^ -10
0.3
2^ -5 1
0.2

Semantic similarity or relatedness score
0.1

(log scale) 0

Jiang&Conrath WikipediaESA 0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9

0
0.1
1

Recall

Jiang&Conrath
Wikipedia
ESA

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n  Hybrid matcher outperforms a single similarity or
relatedness measure matcher.
Matcher Jiang&Conrath Wikipedia ESA Hybrid
Maximal F1 Score 70.06% 44.26% 75.45%
Recall 80% 80% 90%
Precision 62.31% 30.59% 64.94%
1

0.9

0.8

0.7

Precision

0.6

0.5

0.4

0.3

0.2

0.1

0

0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1

Recall

Jiang&Conrath
Wikipedia
ESA
Hybrid

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Experiment 2- Freebase Event Set

Event Set Statistics
Source Freebase events dump 1 December
2011, triples current
Collection Triples directly associated to instances
of “fbase:time.event" class
Data model RDF
Total # of events 84,529
Total # of distinct event types 858
Total # of distinct event properties 1,242
Total # of distinct event values 1,199,627
Total # of triples 1,859,338
Average # of distinct type per event 3.33
Average # of distinct property per event 10.67
Average # of distinct value per event 21.66
Average # of triple per event 21.99

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n  Same as in Experiment 1.
ID Description Subscription # of # of Event type Event Literals and
rules

1 Football matches event type "Football Match" 1 1 YES YES NO
played by Spain in event team "Spain national
the FNB stadium football team"
2 Football matches event type "Football Match" 8 2 YES YES NO
stadium
3 Events taking place in event type "Event" 29 5 NO YES NO
4 Charity events taking event type "Charity" 0 - - - -
place in Wembley event place "Wembley Stadium"
stadium
5 Charity Rock events event type "Charity" 0 - - - -
taking place in event type "Rock"
6 Football matches event type "Football Match" 34 1,398 YES YES Background
7 Football matches event type "Football Match" 2 219,600 YES YES Background
played by a South event team "South America" Knowledge
Europe

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n  Hybrid matcher gives similar results in Freebase as
in DBpedia
Matcher Jiang&Conrath Wikipedia ESA Hybrid
Maximal F1 Score 44.60% 70.73% 76.33%
Recall 60% 80% 80%
Precision 35.49% 63.39% 72.98%
1

0.9

0.8

0.7

Precision

0.6

0.5

0.4

0.3

0.2

0.1

0

0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1

Recall

Jiang&Conrath
Wikipedia
ESA
Hybrid

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Conclusions

n  Approximate semantic matcher addresses
subscriptions/ rules maintainability cost in
heterogeneous and dynamic environments
n  Approximate semantic matcher is suitable when less
than 100% precision is acceptable
Approximate Semantic
Exact Matcher
Matcher
Number of Required Subscriptions 345,000 7
Maximal F1-Score 100% 75.89%

n  A hybrid matcher outperforms a single similarity or
relatedness measure matcher.

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Future Work

n  Need to enhance subscription set for more
representativeness.
n  Approximate semantic matcher generates “uncertain”
results whose impacts on further event processing
functions such as CEP needs to be studied

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