Mid-Ontology Learning from Linked Data @JIST2011

大学共同利用機関法人情報・システム研究機構

国立情報学研究所National Institute of Informatics

Mid-Ontology Learning from Linked Data

Lihua Zhao and Ryutaro Ichise

JIST2011, 12.05.2011, Hangzhou

Introduction Mid-Ontology Learning Approach Experimental Evaluation Related Work Conclusion and Future Work

Outline

Introduction

Mid-Ontology Learning Approach

Experimental Evaluation

Related Work

Conclusion and Future Work

Lihua Zhao and Ryutaro Ichise | Mid-Ontology Learning from Linked Data | 2大学共同利用機関法人情報・システム研究機構



Introduction

Linked Open Data295 data sets, 31 billion RDF triples (as of Sep. 2011)7 domains (cross-domain, geographic, media, life sciences,government, user-generated content, and publications)Interlinked Instances (owl:sameAs)




Introduction

Challenging ProblemEach data set has specific ontology schema

DBpedia: http://dbpedia.org/property/populationGeonames: http://www.geonames.org/ontology#population

Time-consuming to learn all the ontology schemaDBpedia: 320 classes and thousands of properties.

Heterogeneity of ontology schemahttp://dbpedia.org/property/populationTotalhttp://dbpedia.org/property/population




Introduction

Objective

Collected data based on “http://dbpedia.org/resource/Berlin”.Predicate Object

http : //dbpedia.org/property/name Berlinhttp : //dbpedia.org/property/population 3439100http : //dbpedia.org/property/plz 10001-14199http : //dbpedia.org/ontology/postalCode 10001-14199http : //dbpedia.org/ontology/populationTotal 3439100. . . . . . . . . . . .http : //www .geonames.org/ontology#alternateName Berlinhttp : //www .geonames.org/ontology#alternateName Berlyn@afhttp : //www .geonames.org/ontology#population 3426354. . . . . . . . . . . .http : //www .w3.org/2004/02/skos/core#prefLabel Berlin (Germany)http : //data.nytimes.com/elements/first use 2004-09-12http : //data.nytimes.com/elements/latest use 2010-06-13




Introduction

Simple ontology for various data sets: Mid-OntologyInvestigation on linked instances

owl:sameAs links identical or related instancesScale down the data set

Automatic ontology learningIntegrate ontologies from diverse domain data setsAutomate the ontology construction processAdapt to linked open data sets








Data Collection

We scale down the data sets by collecting only linked instances,from which we can extract related information.

Extract data linked with owl:sameAsSelect a core data set (inward & outward links)Collect all instances that have owl:sameAs

Remove noisy instances of the core data setNoisy instances: without any meaningful triple

Collect predicates and objectscollect <predicate, object> (PO) pairs from collected instancescollect PO pairs from linked instances (other data sets)




An Example of Collected Data

dbpedia:Berlin owl:sameAs http://sws.geonames.org/2950159/

http://data.nytimes.com/N50987186835223032381 owl:sameAs dbpedia:Berlin










Predicate Grouping

Grouping related predicates from different ontology schema,because many similar or related predicates actually refer to thesame thing.

Group predicates by exact matching

Prune groups by similarity matching

Refine groups using extracted relations




Predicate Grouping


Group predicates by exact matchingOne predicate may have various objectsDifferent predicates may have the same object value

Prune groups by similarity matching





Group Predicates by Exact Matching

Create initial groups (Gi ) of PO pairse.g. Gi .predicates = { db-prop:name, geo-onto:alternateName }

Gi .objects = { Berlin, Berlyn@af }






Predicate Grouping



Prune groups by similarity matchingExact matching may ignore

Terms of predicates or objects written in different languagesSemantically identical or related predicates





Prune Groups by Similarity Matching

Ontology similarity matching at the concept level

String-based similarity measure: StrSim(O(Gi ),O(Gj ))

O(Gi ): objects in Gi

Prefix, Suffix, Levenshtein distance, and n-gram.

Knowledge-based similarity measure: WNSim(T (Gi ),T (Gj ))

T (Gi ): pre-processed terms of predicates in Gi

Natural Language Processing: tokenizing terms, removing stop words,and stemming.WordNet-based similarity measures: LCH, RES, HSO, JCN, LESK,PATH, WUP, LIN, and VECTOR




Prune Groups by Similarity Matching

Similarity between initial groups {G1,G2, . . .Gk}

Sim(Gi ,Gj ) =StrSim(O(Gi ),O(Gj )) + WNSim(T (Gi ),T (Gj ))

2

Prune initial groups Gi

If Sim(Gi ,Gj ) is higher than the predefined similarity threshold, wemerge Gi and Gj .

If an initial group Gi has not been merged and has only one POpair, we remove Gi .




An Example of Similarity Calculation

Group Predicate Object

Gi http : //dbpedia.org/property/population 3439100http : //dbpedia.org/ontology/populationTotal 3439100

Gj http : //www .geonames.org/ontology#population 3426354

Example of String-based similarity measures on pairwise objects.Pairwise Objects prefix suffix Levenshtein distance n-gram

“3439100”, “3426354” 0.29 0 0 0.29

Example of WordNet-based similarity measures on pairwise terms.Pairwise Terms LCH RES HSO JCN LESK PATH WUP LIN VECTOR

population, population 1 1 1 1 1 1 1 1 1population, total 0.4 0 0 0.06 0.03 0.11 0.33 0 0.06

Sim(Gi ,Gj ) =0.145 + 0.5825

2= 0.36375




Predicate Grouping



Prune groups by similarity matchingRefine groups using extracted relations

Divide pruned groups according to rdfs:domain and rdfs:range.Keep groups with high frequency








Mid-Ontology Construction

Select terms for Mid-Ontology

Collect all the terms of predicates in each refined group Gi .

Collect all the pre-processed terms of P(Gi ) (predicates in Gi ).

Choose one term, which has the highest frequency and longestterm.e.g. “area” and “areaCode” are totally different

Construct Relations

mo-prop:hasMembers to link Mid-Ontology classes and integratedpredicates

Construct Mid-Ontology

Automatically construct Mid-Ontology using selected terms andmo-prop:hasMembers.




Experimental Evaluation

Evaluate the Mid-Ontology approach from four different aspects:

Evaluation of Data Reduction

Evaluation of Ontology Quality

Evaluation with A SPARQL Example

Analysis of Mid-Ontology Approach




Implementation

Environment

Linux Ubuntu 10.10, 16GB Memory, 1 TB DiskCore i7 CPU 880 3.07GHz

Java, Netbeans 6.9

Virtuoso

High-performance server for RDF storage

SPARQL query endpoint

WordNet::Similarity

Implemented in Perl

Knowledge-based similarity measures




Experimental Data

DBpedia: cross-domain, 3.5 million things, 8.9 million URIs

Geonames: geographical domain, 7 million URIs

NYTimes: media domain, 10,467 subject news

Choose DBpedia as the core data set, because of its wealth of inwardand outward links to other data sets.




Evaluation of Data Reduction

Evaluate the effectiveness of data reduction during the datacollection phase by comparing the number of instances.

Number of distinct instances during data collection phase.Data set Before reduction owl:sameAs retrieval Noisy data removal

DBpedia 8,955,728 135,749 (1.52%) 88,506 (0.99%)Geonames 7,479,714 128,961 (1.72%) 82,054 (1.10%)NYTimes 10,467 9,226 (88.14%) 8,535 (81.54%)

Evaluation Analysis

The data sets are dramatically scaled down by keeping onlylinked instances that share related information.

Successfully removed noisy instances, which may affect thequality of the Mid-Ontology.e.g. Removed instances with only db-prop:hasPhotosCollection(broken link) and owl:sameAs link.





Evaluate the quality of Mid-Ontology by validating whetherpredicates in each class share related information.

Accuracy of Mid-Ontology

ACC (MO) =

∑ni=1

|Correct Predicates in Ci ||Ci |

n

n: the number of classes|Ci |: the number of predicates in class Ci .

Cardinality

Cardinality =|Number of Predicates||Number of Classes|





Improvement achieved by our approach

MO no p r: with exact matching (without the pruning andrefining processes)

MO: with both pruning and refining processes

MO Number of Classes Number of Predicates Cardinality AccuracyMO no p r 11 300 27.27 68.78%MO 29 180 6.21 90.10%

Evaluation Analysis

Significantly improved the accuracy

Decreased the cardinality (Less number of predicates and moreclasses)

Successfully removed unrelated predicates




Evaluation with A SPARQL Example

Evaluate the effectiveness of information retrieval with theMid-Ontology constructed with our approach.

Predicates grouped in mo-onto:population.<rdf:Description rdf:about=“mid-onto:population”><mo-prop:hasMembers rdf:resource=“http://dbpedia.org/property/population”/><mo-prop:hasMembers rdf:resource=“http://dbpedia.org/property/popLatest”/><mo-prop:hasMembers rdf:resource=“http://dbpedia.org/property/populationTotal”/><mo-prop:hasMembers rdf:resource=“http://dbpedia.org/ontology/populationTotal”/><mo-prop:hasMembers rdf:resource=“http://dbpedia.org/property/einwohner”/><mo-prop:hasMembers rdf:resource=“http://www.geonames.org/ontology#population”/></rdf:Description>




Evaluation with A SPARQL ExampleSPARQL: Find places with a population of more than 10 million.

SELECT DISTINCT ?placesWHERE{ mid-onto:population mo-prop:hasMembers ?prop.

?places ?prop ?population.FILTER (xsd:integer(?population) > 10000000). }

Single property for population Number of Results

http://dbpedia.org/property/population 177http://dbpedia.org/property/popLatest 1http://dbpedia.org/property/populationTotal 107http://dbpedia.org/ontology/populationTotal 129http://dbpedia.org/property/einwohner 1http://www.geonames.org/ontology#population 244

Evaluation AnalysisFind 517 places with mid-onto:population.Less results with each single predicate under the samecondition.




Analysis of Mid-Ontology Approach

Analyze whether we can successfully identify how data sets areconnected.

Sample classes in the Mid-OntologyDBpedia DBpedia & Geonames DBpedia & Geonames & NYTimes

mo-onto:birthdate mo-onto:population mo-onto:namemo-onto:deathdate mo-onto:prominence mo-onto:longmo-onto:motto mo-onto:postal

Evaluation Analysis

Predicates in DBpedia are heterogeneous.

Linked instances between DBpedia and Geonames are aboutplaces.

Linked instances among DBpedia, Geonames, and NYTimesare about events, persons, or places.




Possible Application

Find missing owl:sameAs linkse.g. Find missing owl:sameAs link with mo-onto:populationhttp://dbpedia.org/resource/Cyclades db-prop:population “119549”http://dbpedia.org/resource/Cyclades db-prop:name “Cyclades”http://sws.geonames.org/259819/ geo-onto:population “119549”http://sws.geonames.org/259819/ geo-onto:alternateName “Cyclades”




Possible Application

Find missing owl:sameAs linkse.g. Find missing owl:sameAs link with mo-onto:populationhttp://dbpedia.org/resource/Cyclades db-prop:population “119549”http://dbpedia.org/resource/Cyclades db-prop:name “Cyclades”http://sws.geonames.org/259819/ geo-onto:population “119549”http://sws.geonames.org/259819/ geo-onto:alternateName “Cyclades”

Add owl:sameAs linkhttp://dbpedia.org/resource/Cyclades owl:sameAs http://sws.geonames.org/259819/http://sws.geonames.org/259819/ owl:sameAs http://dbpedia.org/resource/Cyclades




Related Work

Construct intermediate-layer ontology from geospatial, zoology,and genetics data resources. [Parundekar, et al.,2010]

Limited to a specific domain

Construct intermediate-level ontology by enriching upperontology (by adding new classes and properties). [Damova, etal., 2010]

Still too large

Analysis of basic properties of SameAs network,Pay-Level-Domain network and Class-Level Similarity network.[Ding, et al., 2010]

Only frequent types are considered to analyze how data are connected




Conclusion and Future Work

Conclusion

Learning heterogeneous ontology schema in the linked opendata sets is not feasible.

An automatic Mid-Ontology learning approach can solve theheterogeneity problem by integrating related predicates.

The Mid-Ontology has a high accuracy, and effective to searchfrom various data sets.

A simple Mid-Ontology can be constructed without learningthe entire ontology schema.

Future Work

Billion Triple Challenge (BTC) data set

Crawl links at two or three depths without a core data set



Questions?

Lihua Zhao, [email protected] Ichise, [email protected]



Technology

Mid-Ontology Learning from Linked Data @JIST2011