Instance-Based Ontological Knowledge Acquisition

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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 …

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.

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  • 1. Instance-Based Ontological Knowledge AcquisitionThe Graduate University for Advanced Studies (SOKENDAI)National Institute of InformaticsLihua Zhao & Ryutaro IchiseESWC2013, Montpellier, France, 28th May, 2013
  • 2. OutlineIntroductionRelated WorkSemi-automatic Ontology Integration FrameworkGraph-Based Ontology IntegrationMachine-Learning-Based Ontology Schema ExtractionOntology MergerExperimentsConclusion and Future WorkLihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 2
  • 3. IntroductionLinked 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).WorldFact-bookJohnPeel(DBTune)PokedexPfamUS SEC(rdfabout)LinkedLCCNEuropeanaEEAIEEEChEMBLSemanticXBRLSWDogFoodCORDIS(FUB)AGROVOCOpenlyLocalDiscogs(DataIncubator)DBpediayovistoTele-graphistags2condeliciousNSFMediCareBrazilianPoli-ticiansdotACERAOpenCycItalianpublicschoolsUB Mann-heimJISCMoseleyFolkSemanticTweetOSGTAAtotl.netOAIPortu-gueseDBpediaLOCAHKEGGGlycanCORDIS(RKBExplorer)UMBELAffy-metrixriesebusiness.data.gov.ukOpenDataThesau-rusGeoLinkedDataUK Post-codesSmartLinkECCO-TCPUniProt(Bio2RDF)SSWThesau-rusRDFohlohFreebaseLondonGazetteOpenCorpo-ratesAirportsGEMETP20TCMGeneDITSource CodeEcosystemLinked DataOMIMHellenicFBDDataGov.ieMusicBrainz(DBTune)data.gov.ukintervalsLODEClimbingSIDERProjectGuten-bergMusicBrainz(zitgist)ProDomHGNCSMCJournalsReactomeNationalRadio-activityJPlegislationdata.gov.ukAEMETProductTypesOntologyLinkedUserFeedbackRevyuGeneOntologyNHS(En-AKTing)URIBurnerDBTropesEurécomISTATImmi-grationLichfieldSpen-dingSurgeRadioEuro-stat(FUB)PiedmontAccomo-dationsNewYorkTimesKlapp-stuhl-clubEUNISBricklinkreegleCO2Emission(En-AKTing)AudioScrobbler(DBTune)GovTrackGovWILDECSSouth-amptonEPrintsKEGGReactionLinkedEDGAR(OntologyCentral)LIBRISOpenLibraryKEGGDrugresearch.data.gov.ukVIVOCornellUniRefWordNet(RKBExplorer)Cornettomedu-catorDDC DeutscheBio-graphieWikiUlmNASA(Data Incu-bator)BBCMusicDrugBankTurismodeZaragozaPlymouthReadingListseducation.data.gov.ukKISTIUniPathwayEurostat(OntologyCentral)OGOLODTwarqlMusicBrainz(DataIncubator)GeoNamesPubChemItalianMuseumsGood-winFamilyflickrwrapprEurostatThesau-rus WOpenLibrary(Talis)LOIUSLinkedGeoDataLinkedOpenColorsWordNet(VUA)patents.data.gov.ukGreekDBpediaSussexReadingListsMetofficeWeatherForecastsGNDLinkedCTSISVUtransport.data.gov.ukDidac-taliadbpedialiteBNBOntosNewsPortalLAASProductDBiServeRecht-spraak.nlKEGGCom-poundGeoSpeciesVIVO UFLinkedSensor Data(Kno.e.sis)lobidOrgani-sationsLEMLinkedCrunch-baseFTSOceanDrillingCodicesJanusAMPntnuscWeatherStationsAmster-damMuseumlingvojCrime(En-AKTing)Course-warePubMedACMBBCWildlifeFinderCalamesChronic-lingAmericadata-open-ac-ukOpenElectionDataProjectSlide-share2RDFFinnishMunici-palitiesOpenEIMARCCodesListVIVOIndianaHellenicPDLCSHFanHubzbibleontologyIdRefSudocKEGGEnzymeNTUResourceListsPRO-SITELinkedOpenNumbersEnergy(En-AKTing)RomaOpenCalaisdatabnf.frlobidResourcesIRITtheses.frLOVRådatanå!DailyMedTaxo-nomyNew-castleGoogleArtwrapperPoké-pédiaEURESBibBaseRESEXSTITCHPDBEARThIBMLast.FMartists(DBTune)YAGOECS(RKBExplorer)EventMediaSTWmyExperi-mentBBCProgram-mesNDLsubjectsTaxonConceptPisaKEGGPathwayUniParcJamendo(DBtune)Popula-tion (En-AKTing)Geo-WordNetRAMEAUSHUniSTSMortality(En-AKTing)AlpineSkiAustriaDBLP(RKBExplorer)Chem2Bio2RDFMGIDBLP(L3S)Yahoo!GeoPlanetGeneIDRDF BookMashupEl ViajeroTourismUberblicSwedishOpenCulturalHeritageGESISdatadcsLast.FM(rdfize)Ren.EnergyGenera-torsSearsRAE2001NSZLCatalogHomolo-GeneOrd-nanceSurveyTWC LOGDDisea-someEUTCProduc-tionsPSHWordNet(W3C)semanticweb.orgScotlandGeo-graphyMagna-tuneNorwe-gianMeSHSGDTrafficScotlandstatistics.data.gov.ukCrimeReportsUKUniProtUS Census(rdfabout)Man-chesterReadingListsEU Insti-tutionsPBACVIAFUN/LOCODELexvoLinkedMDBESDstan-dardsreference.data.gov.ukt4gminfoSudocECSSouth-amptonePrintsClassical(DBTune)DBLP(FUBerlin)Scholaro-meterSt.AndrewsResourceListsNVDFishesofTexasScotlandPupils &ExamsRISKSgnossDEPLOYInterProLoticoOxPointsEnipediandlnaBudapestCiteSeerMediaGeographicPublicationsUser-generated contentGovernmentCross-domainLife sciencesAs of September 2011Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 3
  • 4. MotivationFigure: Interlinked Instances of “France”.Problems when access to several data sets:Ontology Heterogeneity ProblemMap related ontology classes and properties.Ontology similarity matching on the SameAs graph patterns.Difficulty in Identifying Core Ontology SchemasRetrieve 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 WorkFind useful attributes from frequent graph patterns using asupervised machine learning method. [Le, 2010]Only for geographic data and no discussion about the features.A debugging method for mapping lightweight ontologies withmachine learning method. [Meilicke, 2008]Limited to the expressive lightweight ontologies.Construct intermediate-layer ontology by analyzing conceptcoverings. [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 FrameworkConstruct a global ontology by integrating heterogeneousontologies of the Linked Open Data.Graph-Based Ontology Integration [Zhao, et al., 2012]Group related classes and properties.Machine-Learning-Based Ontology Schema ExtractionExtract frequent core classes and properties.Ontology MergerMerge extracted ontology classes and properties.Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 6
  • 7. Semi-automatic Ontology Integration FrameworkConstruct a global ontology by integrating heterogeneousontologies of the Linked Open Data.Graph-Based Ontology Integration [Zhao, et al., 2012]Group related classes and properties.Machine-Learning-Based Ontology Schema ExtractionExtract frequent core classes and properties.Ontology MergerMerge extracted ontology classes and properties.Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 7
  • 8. Graph-based Ontology IntegrationExtract graph patterns from interlinked instances to discoverrelated ontology classes and predicates.Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 8
  • 9. STEP 1: Graph Pattern ExtractionSameAs Graph: An undirected SameAs Graph SG = (V , E, I), whereV : 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 SGFrancePredicate Object Typerdf:type db-onto:Country Classrdfs:label “France”@en Stringfoaf:name “France”@en Stringfoaf:name “R´epublique fran¸caise”@en Stringdb-onto:wikiPageExternalLink http://us.franceguide.com/ URIdb-prop:populationEstimate 65447374 Number. . . . . . . . . . . . . . . . . .geo-onto:name France Stringgeo-onto:alternateName “France”@en Stringgeo-onto:featureCode geo-onto:A.PCLI Classgeo-onto:population 64768389 Number. . . . . . . . . . . . . . . . . .rdf:type mdb:country Classmdb:country name France Stringmdb:country population 64094000 Numberrdfs:label France (Country) String. . . . . . . . . . . . . . . . . .rdf:type skos:Concept Classskos:inScheme nyt:nytd geo Classskos:prefLabel “France”@en Stringnyt-prop:first use 2004-09-01 DateLihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 10
  • 11. STEP 3: Related Classes and Properties GroupingRelated Classes Grouping (Leaf nodes)Tracking subsumption relations from SameAs graphs.< C1 owl:subClassOf C2 >< C1 skos:inScheme C2 >Example: SGFranceRelated 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 GroupingRelated Properties GroupingExact 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 )2ObjSim(POi , POj ) =1 −|OPOi−OPOj|OPOi+OPOjif OPO is NumberStrSim(OPOi, OPOj) if OPO is StringPreSim(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 PatternsAggregate the integrated classes and properties from all the extractedgraph patterns.Select A Term for Each Setex-onto:ClassTermex-onto:propTermConstruct Relationsex-prop:hasMemberClasses<class, ex-prop:hasMemberClasses, ex-onto:ClassTerm>ex-prop:hasMemberDataTypes<property, ex-prop:hasMemberDataTypes, ex-onto:propTerm>Construct A Preliminary Integrated OntologyLihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 13
  • 14. STEP 5: Manual RevisionManually 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 FrameworkConstruct a global ontology by integrating heterogeneousontologies of the Linked Open Data.Graph-Based Ontology Integration [Zhao, et al., 2012]Group related classes and properties.Machine-Learning-Based Ontology Schema ExtractionExtract frequent core classes and properties.Ontology MergerMerge extracted ontology classes and properties.Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 15
  • 16. Machine-Learning-Based Ontology Schema ExtractionTop-level classes and core properties are necessary.Decision TableRetrieves core properties in each data set.Belongs to rule-based machine learning with simple hypothesis.Retrieves a subset of properties that are important for describinginstances in a data set.AprioriRetrieves core properties in the instances of a specifictop-level class.Belongs to association rule mining.Finds a set of properties, whose support is greater than theuser-defined minimum support.Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 16
  • 17. Decision TableRetrieve top-level classes and core properties that are important fordescribing 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), classPF-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. AprioriRetrieve top-level classes and frequent core properties that areimportant 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 FrameworkConstruct a global ontology by integrating heterogeneousontologies of the Linked Open Data.Graph-Based Ontology Integration [Zhao, et al., 2012]Group related classes and properties.Machine-Learning-Based Ontology Schema ExtractionExtract frequent core classes and properties.Ontology MergerMerge extracted ontology classes and properties.Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 19
  • 20. Ontology MergerGraph-Based Ontology Integration outputs a Preliminary IntegratedOntology.For the ontology classes and properties retrieved fromMachine-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. ExperimentsData SetsGraph-Based Ontology IntegrationDecision TableAprioriComparison of Integrated OntologyCase StudiesLihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 21
  • 22. Data SetsDBpedia (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 LearningData Set Instances Selected Class Top-level Property SelectedInstances Class PropertyDBpedia 3,708,696 64,460 241 28 1385 840Geonames 7,480,462 45,000 428 9 31 21NYTimes 10,441 10,441 5 4 8 7LinkedMDB 694,400 50,000 53 10 107 60Selected InstancesRandomly select instances per class:DBpedia (5000), Geonames(3000), NYTimes(All), LinkedMDB(3000)Top-level ClassesOntology-based data set: Use subsumption relations.Without ontology: Use categories.Selected PropertiesWith frequency threshold θ as√n, where n is the total number ofinstances in the data set.Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 23
  • 24. Graph-Based Ontology Integration13 graph patternsFrequent graph patterns:GP1, GP2, GP3N,G,D: GP4, GP5, GP7, GP8N,M,D: GP6M,G,D: GP9M,D,N,G: GP10, GP11,GP12, GP1313 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 ApproachesEvaluate the Decision Table and Apriori algorithm.Evaluation of Decision TableEvaluate whether the retrieved sets of properties are important fordescribing instances by testing if they can be used to distinguishdifferent types of instances in the data set.Evaluation of AprioriAnalyze the performance of Apriori algorithm in each data set withexamples of retrieved sets of properties.Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 25
  • 26. Decision TableData Set Precision Recall F-Measure Selected PropertiesDBpedia 0.892 0.821 0.837 53Geonames 0.472 0.4 0.324 10NYTimes 0.795 0.792 0.785 5LinkedMDB 1 1 1 11Core 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. AprioriExamples of retrieved core properties with Apriori Algorithm.Data Set Class PropertiesDBpediadb: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.Geonamesgeo:P geo-onto:alternateName, geo-onto:countryCode.geo:R wgs84 pos:alt, geo-onto:name, geo-onto:countryCode.NYTimesnyt:nytd geo wgs84 pos:long.nyt:nytd des skos:scopeNote.LinkedMDBmdb: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 OntologyPrevious Work Machine Learning Current WorkGraph-Based Decision Apriori IntegratedIntegration Table OntologyClass 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 IFind Missing Links of Islands with Integrated OntologySELECT DISTINCT ?geo ?db ?stringwhere { ?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 exactmatching on the labels of instances.Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 29
  • 30. Case Studies IIPredicates grouped in ex-prop:birthDateProperty Number of Instances rdfs:domaindb-onto:birthDate 287,327 db-onto:Persondb-prop:datebirth 1,675 N/Adb-prop:dateofbirth 87,364 N/Adb-prop:dateOfBirth 163,876 N/Adb-prop:born 34,832 N/Adb-prop:birthdate 70,630 N/Adb-prop:birthDate 101,121 N/ASuggest “db-onto:birthDate” as the standard property because ithas rdfs:domain definitionhas the highest usage in the DBpedia instances.Lihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 30
  • 31. Case Studies IIIGive me all the cities with more than 10,000,000 inhabitants.Standard Query Query with the Integrated OntologySELECT DISTINCT ?uri ?string SELECT DISTINCT ?uri ?stringWHERE { 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 WorkConclusionSemi-automatic ontology integration frameworkGraph-Based Ontology Integration.Ontology similarity matching on SameAs graph patterns.Retrieve related ontology classes and properties.Machine-Learning-Based Ontology Schema ExtractionDecision Table and Apriori.Extract top-level classes and core properties.Ontology MergerFind missing links, detect misuses of ontologies, and access variousdata sets with integrated ontology.Future WorkAutomatically 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.jpRyutaro Ichise, ichise@nii.ac.jpLihua Zhao & Ryutaro Ichise | Instance-Based Ontological Knowledge Acquisition | 33