The Linked Open Data (LOD) cloud contains tremendous amounts of interlinked instances, from where we can retrieve abundant knowledge. However, because of the heterogeneous and big ontologies, it is time consuming to learn all the ontologies manually and it is difficult to observe which properties are important for describing instances of a specific class. In order to construct an ontology that can help users easily access to various data sets, we propose a semi-automatic ontology inte- gration framework that can reduce the heterogeneity of ontologies and retrieve frequently used core properties for each class. The framework consists of three main components: graph-based ontology integration, machine-learning-based ontology schema extraction, and an ontology merger. By analyzing the instances of the linked data sets, this framework acquires ontological knowledge and constructs a high-quality integrated ontology, which is easily understandable and effective in knowledge ac- quisition from various data sets using simple SPARQL queries.

1. Instance-Based Ontological Knowledge Acquisition
The Graduate University for Advanced Studies (SOKENDAI)
National Institute of Informatics
Lihua Zhao & Ryutaro Ichise
ESWC2013, Montpellier, France, 28th May, 2013

2. Outline
Introduction
Related Work
Semi-automatic Ontology Integration Framework
Graph-Based Ontology Integration
Machine-Learning-Based Ontology Schema Extraction
Ontology Merger
Experiments
Conclusion and Future Work
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 2

3. Introduction
Linked Open Data (LOD)
Machine-readable and interlinked at instance-level.
295 data sets, 31 billion RDF triples (as of Sep. 2011).
Around 504 million owl:sameAs links.
7 domains (cross-domain, geographic, media, life sciences,
government, user-generated content, and publications).
World
Fact-
book
John
Peel
(DBTune)
Pokedex
Pfam
US SEC
(rdfabout)
Linked
LCCN
Europeana
EEA
IEEE
ChEMBL
Semantic
XBRL
SW
Dog
Food
CORDIS
(FUB)
AGROVOC
Openly
Local
Discogs
(Data
Incubator)
DBpedia
yovisto
Tele-
graphis
tags2con
delicious
NSF
Medi
Care
Brazilian
Poli-
ticians
dotAC
ERA
Open
Cyc
Italian
public
schools
UB Mann-
heim
JISC
Moseley
Folk
Semantic
Tweet
OS
GTAA
totl.net
OAI
Portu-
guese
DBpedia
LOCAH
KEGG
Glycan
CORDIS
(RKB
Explorer)
UMBEL
Affy-
metrix
riese
business.
data.gov.
uk
Open
Data
Thesau-
rus
Geo
Linked
Data
UK Post-
codes
Smart
Link
ECCO-
TCP
UniProt
(Bio2RDF)
SSW
Thesau-
rus
RDF
ohloh
Freebase
London
Gazette
Open
Corpo-
rates
Airports
GEMET
P20
TCM
Gene
DIT
Source Code
Ecosystem
Linked Data
OMIM
Hellenic
FBD
Data
Gov.ie
Music
Brainz
(DBTune)
data.gov.uk
intervals
LODE
Climbing
SIDER
Project
Guten-
berg
Music
Brainz
(zitgist)
ProDom
HGNC
SMC
Journals
Reactome
National
Radio-
activity
JP
legislation
data.gov.uk
AEMET
Product
Types
Ontology
Linked
User
Feedback
Revyu
Gene
Ontology
NHS
(En-
AKTing)
URI
Burner
DB
Tropes
Eurécom
ISTAT
Immi-
gration
Lichfield
Spen-
ding
Surge
Radio
Euro-
stat
(FUB)
Piedmont
Accomo-
dations
New
York
Times
Klapp-
stuhl-
club
EUNIS
Bricklink
reegle
CO2
Emission
(En-
AKTing)
Audio
Scrobbler
(DBTune)
GovTrack
GovWILD
ECS
South-
ampton
EPrints
KEGG
Reaction
Linked
EDGAR
(Ontology
Central)
LIBRIS
Open
Library
KEGG
Drug
research.
data.gov.
uk
VIVO
Cornell
UniRef
WordNet
(RKB
Explorer)
Cornetto
medu-
cator
DDC Deutsche
Bio-
graphie
Wiki
Ulm
NASA
(Data Incu-
bator)
BBC
Music
Drug
Bank
Turismo
de
Zaragoza
Plymouth
Reading
Lists
education.
data.gov.
uk
KISTI
Uni
Pathway
Eurostat
(Ontology
Central)
OGOLOD
Twarql
Music
Brainz
(Data
Incubator)
Geo
Names
Pub
Chem
Italian
Museums
Good-
win
Family
flickr
wrappr
Eurostat
Thesau-
rus W
Open
Library
(Talis)
LOIUS
Linked
GeoData
Linked
Open
Colors
WordNet
(VUA)
patents.
data.gov.
uk
Greek
DBpedia
Sussex
Reading
Lists
Metoffice
Weather
Forecasts
GND
LinkedCT
SISVU
transport.
data.gov.
uk
Didac-
talia
dbpedia
lite
BNB
Ontos
News
Portal
LAAS
Product
DB
iServe
Recht-
spraak.
nl
KEGG
Com-
pound
Geo
Species
VIVO UF
Linked
Sensor Data
(Kno.e.sis)
lobid
Organi-
sations
LEM
Linked
Crunch-
base
FTS
Ocean
Drilling
Codices
Janus
AMP
ntnusc
Weather
Stations
Amster-
dam
Museum
lingvoj
Crime
(En-
AKTing)
Course-
ware
PubMed
ACM
BBC
Wildlife
Finder
Calames
Chronic-
ling
America
data-
open-
ac-
uk
Open
Election
Data
Project
Slide-
share2RDF
Finnish
Munici-
palities
OpenEI
MARC
Codes
List
VIVO
Indiana
Hellenic
PD
LCSH
FanHubz
bible
ontology
IdRef
Sudoc
KEGG
Enzyme
NTU
Resource
Lists
PRO-
SITE
Linked
Open
Numbers
Energy
(En-
AKTing)
Roma
Open
Calais
data
bnf.fr
lobid
Resources
IRIT
theses.
fr
LOV
Rådata
nå!
Daily
Med
Taxo-
nomy
New-
castle
Google
Art
wrapper
Poké-
pédia
EURES
BibBase
RESEX
STITCH
PDB
EARTh
IBM
Last.FM
artists
(DBTune)
YAGO
ECS
(RKB
Explorer)
Event
Media
STW
my
Experi-
ment
BBC
Program-
mes
NDL
subjects
Taxon
Concept
Pisa
KEGG
Pathway
UniParc
Jamendo
(DBtune)
Popula-
tion (En-
AKTing)
Geo-
WordNet
RAMEAU
SH
UniSTS
Mortality
(En-
AKTing)
Alpine
Ski
Austria
DBLP
(RKB
Explorer)
Chem2
Bio2RDF
MGI
DBLP
(L3S)
Yahoo!
Geo
Planet
GeneID
RDF Book
Mashup
El Viajero
Tourism
Uberblic
Swedish
Open
Cultural
Heritage
GESIS
data
dcs
Last.FM
(rdfize)
Ren.
Energy
Genera-
tors
Sears
RAE2001
NSZL
Catalog
Homolo-
Gene
Ord-
nance
Survey
TWC LOGD
Disea-
some
EUTC
Produc-
tions
PSH
WordNet
(W3C)
semantic
web.org
Scotland
Geo-
graphy
Magna-
tune
Norwe-
gian
MeSH
SGD
Traffic
Scotland
statistics.
data.gov.
uk
Crime
Reports
UK
UniProt
US Census
(rdfabout)
Man-
chester
Reading
Lists
EU Insti-
tutions
PBAC
VIAF
UN/
LOCODE
Lexvo
Linked
MDB
ESD
stan-
dards
reference.
data.gov.
uk
t4gm
info
Sudoc
ECS
South-
ampton
ePrints
Classical
(DB
Tune)
DBLP
(FU
Berlin)
Scholaro-
meter
St.
Andrews
Resource
Lists
NVD
Fishes
of
TexasScotland
Pupils &
Exams
RISKS
gnoss
DEPLOY
InterPro
Lotico
Ox
Points
Enipedia
ndlna
Budapest
CiteSeer
Media
Geographic
Publications
User-generated content
Government
Cross-domain
Life sciences
As of September 2011
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 3

4. Motivation
Figure: Interlinked Instances of “France”.
Problems when access to several data sets:
Ontology Heterogeneity Problem
Map related ontology classes and properties.
Ontology similarity matching on the SameAs graph patterns.
Difficulty in Identifying Core Ontology Schemas
Retrieve frequently used core ontology classes and properties.
Machine learning for core ontology schema extraction.
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 4

5. Related Work
Find useful attributes from frequent graph patterns using a
supervised machine learning method. [Le, 2010]
Only for geographic data and no discussion about the features.
A debugging method for mapping lightweight ontologies with
machine learning method. [Meilicke, 2008]
Limited to the expressive lightweight ontologies.
Construct intermediate-layer ontology by analyzing concept
coverings. [Parundekar, 2012]
Only for specific domains and limited between two resources.
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 5

6. Semi-automatic Ontology Integration Framework
Construct a global ontology by integrating heterogeneous
ontologies of the Linked Open Data.
Graph-Based Ontology Integration [Zhao, et al., 2012]
Group related classes and properties.
Machine-Learning-Based Ontology Schema Extraction
Extract frequent core classes and properties.
Ontology Merger
Merge extracted ontology classes and properties.
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 6

7. Semi-automatic Ontology Integration Framework
Construct a global ontology by integrating heterogeneous
ontologies of the Linked Open Data.
Graph-Based Ontology Integration [Zhao, et al., 2012]
Group related classes and properties.
Machine-Learning-Based Ontology Schema Extraction
Extract frequent core classes and properties.
Ontology Merger
Merge extracted ontology classes and properties.
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 7

8. Graph-based Ontology Integration
Extract graph patterns from interlinked instances to discover
related ontology classes and predicates.
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 8

9. STEP 1: Graph Pattern Extraction
SameAs Graph: An undirected SameAs Graph SG = (V , E, I), where
V : a set of vertices (the labels of data sets).
E ⊆ V × V : a set of sameAs edges.
I: a set of URIs of the interlinked SameAs Instances.
Example: SGFrance = (VFrance, EFrance, IFrance).
VFrance = {M, D, G, N}
EFrance = {(D, G), (D, N), (G, M), (G, N)}
IFrance = {mdb-country:FR, db:France, geo:3017382, nyt:67...21}.
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 9

10. STEP 2: <Predicate, Object> Collection
<Predicate, Object> (PO) pairs and types for SGFrance
Predicate Object Type
rdf:type db-onto:Country Class
rdfs:label “France”@en String
foaf:name “France”@en String
foaf:name “R´epublique fran¸caise”@en String
db-onto:wikiPageExternalLink http://us.franceguide.com/ URI
db-prop:populationEstimate 65447374 Number
. . . . . . . . . . . . . . . . . .
geo-onto:name France String
geo-onto:alternateName “France”@en String
geo-onto:featureCode geo-onto:A.PCLI Class
geo-onto:population 64768389 Number
. . . . . . . . . . . . . . . . . .
rdf:type mdb:country Class
mdb:country name France String
mdb:country population 64094000 Number
rdfs:label France (Country) String
. . . . . . . . . . . . . . . . . .
rdf:type skos:Concept Class
skos:inScheme nyt:nytd geo Class
skos:prefLabel “France”@en String
nyt-prop:first use 2004-09-01 Date
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 10

11. STEP 3: Related Classes and Properties Grouping
Related Classes Grouping (Leaf nodes)
Tracking subsumption relations from SameAs graphs.
< C1 owl:subClassOf C2 >
< C1 skos:inScheme C2 >
Example: SGFrance
Related Classes → {db-onto:Country, geo-onto:A.PCLI,
mdb:country, nyt:nytd geo }
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 11

12. STEP 3: Related Classes and Properties Grouping
Related Properties Grouping
Exact matching for creating initial sets of PO pairs S1, S2, . . . , Sk .
Similarity matching on the initial sets of PO pairs.
Sim(POi , POj ) =
ObjSim(POi , POj ) + PreSim(POi , POj )
2
ObjSim(POi , POj ) =



1 −
|OPOi
−OPOj
|
OPOi
+OPOj
if OPO is Number
StrSim(OPOi
, OPOj
) if OPO is String
PreSim(POi , POj ) = WNSim(TPOi
, TPOj
)
StrSim(OPOi
, OPOj
): Average of 3 string-based similarity measures.
WNSim(TPOi
, TPOj
): Average of 9 WordNet-based similarity measures.
Refine sets of PO pairs according to rdfs:domain.
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 12

13. STEP 4: Aggregation of All Graph Patterns
Aggregate the integrated classes and properties from all the extracted
graph patterns.
Select A Term for Each Set
ex-onto:ClassTerm
ex-onto:propTerm
Construct Relations
ex-prop:hasMemberClasses
<class, ex-prop:hasMemberClasses, ex-onto:ClassTerm>
ex-prop:hasMemberDataTypes
<property, ex-prop:hasMemberDataTypes, ex-onto:propTerm>
Construct A Preliminary Integrated Ontology
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 13

14. STEP 5: Manual Revision
Manually revise the preliminary integrated ontology.
Terms of the integrated classes and properties:
Choose a proper term for each group of classes or properties.
Groups of related classes or properties:
Correct wrong grouping.
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 14

15. Semi-automatic Ontology Integration Framework
Construct a global ontology by integrating heterogeneous
ontologies of the Linked Open Data.
Graph-Based Ontology Integration [Zhao, et al., 2012]
Group related classes and properties.
Machine-Learning-Based Ontology Schema Extraction
Extract frequent core classes and properties.
Ontology Merger
Merge extracted ontology classes and properties.
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 15

16. Machine-Learning-Based Ontology Schema Extraction
Top-level classes and core properties are necessary.
Decision Table
Retrieves core properties in each data set.
Belongs to rule-based machine learning with simple hypothesis.
Retrieves a subset of properties that are important for describing
instances in a data set.
Apriori
Retrieves core properties in the instances of a specific
top-level class.
Belongs to association rule mining.
Finds a set of properties, whose support is greater than the
user-defined minimum support.
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 16

17. Decision Table
Retrieve top-level classes and core properties that are important for
describing instances in a data set.
Collect top-level classes.
Filter out infrequent properties.
Convert each instance for the Decision Table algorithm.
weight(prop1, inst), weight(prop2, inst), ... weight(propn, inst), class
PF-IIF (Property Frequency - Inverse Instance Frequency)
weight(prop, inst) = pf (prop, inst) × iif (prop, D)
pf (prop, inst) = the frequency of prop in inst.
iif (prop, D) = log
|D|
|instprop|
instprop: an instance that contains the property prop.
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 17

18. Apriori
Retrieve top-level classes and frequent core properties that are
important for describing instances in a specific class.
Collect top-level classes.
Filter out infrequent properties.
Convert each instance of top-level class c for the Apriori algorithm.
[prop1, prop2, ..., propn]
Define minimum support and confidence metric.
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 18

19. Semi-automatic Ontology Integration Framework
Construct a global ontology by integrating heterogeneous
ontologies of the Linked Open Data.
Graph-Based Ontology Integration [Zhao, et al., 2012]
Group related classes and properties.
Machine-Learning-Based Ontology Schema Extraction
Extract frequent core classes and properties.
Ontology Merger
Merge extracted ontology classes and properties.
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 19

20. Ontology Merger
Graph-Based Ontology Integration outputs a Preliminary Integrated
Ontology.
For the ontology classes and properties retrieved from
Machine-Learning-Based Approach:
If Class c ∈ Preliminary Integrated Ontology,
add < ex-onto:ClassTermnew , ex-prop:hasMemberClasses, c >.
For each Property prop retrieved from top-level class c using Apriori,
add a triple < prop, rdfs:domain, c >.
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 20

21. Experiments
Data Sets
Graph-Based Ontology Integration
Decision Table
Apriori
Comparison of Integrated Ontology
Case Studies
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 21

22. Data Sets
DBpedia (v3.6): cross-domain, 3.5 million things, 8.9 million URIs.
Geonames (v2.2.1): geographical domain, 7 million URIs.
NYTimes: media domain, 10,467 subject news.
LinkedMDB: media domain, 0.5 million entities.
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 22

23. Data Sets - Machine Learning
Data Set Instances Selected Class Top-level Property Selected
Instances Class Property
DBpedia 3,708,696 64,460 241 28 1385 840
Geonames 7,480,462 45,000 428 9 31 21
NYTimes 10,441 10,441 5 4 8 7
LinkedMDB 694,400 50,000 53 10 107 60
Selected Instances
Randomly select instances per class:
DBpedia (5000), Geonames(3000), NYTimes(All), LinkedMDB(3000)
Top-level Classes
Ontology-based data set: Use subsumption relations.
Without ontology: Use categories.
Selected Properties
With frequency threshold θ as
√
n, where n is the total number of
instances in the data set.
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 23

24. Graph-Based Ontology Integration
13 graph patterns
Frequent graph patterns:
GP1, GP2, GP3
N,G,D: GP4, GP5, GP7, GP8
N,M,D: GP6
M,G,D: GP9
M,D,N,G: GP10, GP11,
GP12, GP13
13 graph patterns.
97 classes into 48 groups.
357 properties into 38 groups.
Retrieved related classes and properties by analyzing graph patterns.
[Zhao, I-Semantics2012]
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 24

25. Evaluation of Machine Learning Approaches
Evaluate the Decision Table and Apriori algorithm.
Evaluation of Decision Table
Evaluate whether the retrieved sets of properties are important for
describing instances by testing if they can be used to distinguish
different types of instances in the data set.
Evaluation of Apriori
Analyze the performance of Apriori algorithm in each data set with
examples of retrieved sets of properties.
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 25

26. Decision Table
Data Set Precision Recall F-Measure Selected Properties
DBpedia 0.892 0.821 0.837 53
Geonames 0.472 0.4 0.324 10
NYTimes 0.795 0.792 0.785 5
LinkedMDB 1 1 1 11
Core properties are evaluated by predicting classes of instances (10-fold).
11 properties from LinkedMDB can correctly identify class of an instance.
DBpedia and NYTimes performs good with selected properties.
10 properties from Geonames are commonly used for all types of classes.
Examples of retrieved core properties.
DBpedia: db-prop:city, db-prop:debut, db-onto:formationYear,etc.
Geonames: geo-onto:alternateName, geo-onto:countryCode, etc.
NYTimes: nyt:latest use, nyt:topicPage, wgs84 pos:long, etc.
LinkedMDB: mdb:director directorid, mdb:writer writerid, etc.
Retrieved top-level classes and core properties in each data set.
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 26

27. Apriori
Examples of retrieved core properties with Apriori Algorithm.
Data Set Class Properties
DBpedia
db:Event db-onto:place, db-prop:date, db-onto:related/geo.
db:Species db-onto:kingdom, db-onto:class, db-onto:family.
db:Person foaf:givenName, foaf:surname, db-onto:birthDate.
Geonames
geo:P geo-onto:alternateName, geo-onto:countryCode.
geo:R wgs84 pos:alt, geo-onto:name, geo-onto:countryCode.
NYTimes
nyt:nytd geo wgs84 pos:long.
nyt:nytd des skos:scopeNote.
LinkedMDB
mdb:actor mdb:performance, mdb:actor name, mdb:actor netflix id.
mdb:film mdb:director, mdb:performane, mdb:actor, dc:date.
DBpedia and LinkedMDB: Retrieved unique properties.
Geonames and NYTimes: Retrieved commonly used properties only.
Automatically added missing domain information:
< prop, rdfs : domain, classtop >.
Retrieved frequent core properties in each top-level class.
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 27

28. Comparison of Integrated Ontology
Previous Work Machine Learning Current Work
Graph-Based Decision Apriori Integrated
Integration Table Ontology
Class 97 50 (38 new) 50 (38 new) 135 (38 new)
Property 357 79 (49 new) 119(80 new) 453 (96 new)
Previous Work: 97 classes in 49 groups, 357 properties in 38 groups.
Current Work: 135 classes in 87 groups, 453 properties in 97 groups.
Apriori retrieves more properties than Decision Table.
33 new properties are found with both Apriori and Decision Table.
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 28

29. Case Studies I
Find Missing Links of Islands with Integrated Ontology
SELECT DISTINCT ?geo ?db ?string
where { ?geo geo-onto:featureCode geo-onto:T.ISL.
?geo ?gname ?string.
ex-onto:name ex-prop:hasMemberDataTypes ?gname.
?db rdf:type db-onto:Island.
ex-onto:name ex-prop:hasMemberDataTypes ?dname.
?db ?dname ?string. }
Retrieved 509 links, including 218 existing SameAs links:
97 existing links from DBpedia to Geonames.
211 links from Geonames to DBpedia.
90 bidirectional links between DBpedia and Geonames.
Discovered 291 missing links with the integrated ontology using exact
matching on the labels of instances.
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 29

30. Case Studies II
Predicates grouped in ex-prop:birthDate
Property Number of Instances rdfs:domain
db-onto:birthDate 287,327 db-onto:Person
db-prop:datebirth 1,675 N/A
db-prop:dateofbirth 87,364 N/A
db-prop:dateOfBirth 163,876 N/A
db-prop:born 34,832 N/A
db-prop:birthdate 70,630 N/A
db-prop:birthDate 101,121 N/A
Suggest “db-onto:birthDate” as the standard property because it
has rdfs:domain definition
has the highest usage in the DBpedia instances.
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 30

31. Case Studies III
Give me all the cities with more than 10,000,000 inhabitants.
Standard Query Query with the Integrated Ontology
SELECT DISTINCT ?uri ?string SELECT DISTINCT ?uri ?string
WHERE { WHERE {
?uri rdf:type db-onto:City. ?uri rdf:type db-onto:City.
ex-onto:population ex-prop:hasMemberDataTypes ?prop.
?uri db-prop:populationTotal ?inhabitants. ?uri ?prop ?inhabitants.
FILTER (?inhabitants > 10000000). FILTER (?inhabitants > 10000000).
OPTIONAL { ?uri rdfs:label ?string. OPTIONAL { ?uri rdfs:label ?string.
FILTER (lang(?string) = ’en’) }} FILTER (lang(?string) = ’en’) }}
A SPARQL example from QALD-1 Open Challenge.
Standard query: 9 cities.
Query with the integrated ontology: 20 cities.
Help QA systems for finding more related answers with simple queries.
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 31

32. Conclusion and Future Work
Conclusion
Semi-automatic ontology integration framework
Graph-Based Ontology Integration.
Ontology similarity matching on SameAs graph patterns.
Retrieve related ontology classes and properties.
Machine-Learning-Based Ontology Schema Extraction
Decision Table and Apriori.
Extract top-level classes and core properties.
Ontology Merger
Find missing links, detect misuses of ontologies, and access various
data sets with integrated ontology.
Future Work
Automatically detect and revise mistakes in ontology merger.
Automatically detect ranges and domains of properties.
Test our framework with more LOD data sets.
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 32

33. Thank you!
Questions?
Lihua Zhao, lihua@nii.ac.jp
Ryutaro Ichise, ichise@nii.ac.jp
Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 33