Ontology engineering: Ontology alignment
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Ontology engineering: Ontology alignment






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  • And what the consequences are.
  • All handed in in time except two, who have handed it in in the middle of the night, so we will overlook that because it is the first assignment. Two groups have submitted via email. Do not do that. SPAM. One group handed in more than one file: do not do that! Separate rdf file. Only two groups used restrictions. Not absolutely necessary.
  • Breadcrumbs. Example could be: electronics, cameras, slrs, nikons: all cameras, digital or not.
  • Conversion of vocabs into a shared vocab. Step 1 and 2 of your assignment. Lecture is about the second type
  • As I said in previous lectures: there are so many ontologies out there. Why not link them all, once and for all, and then stick to it?
  • Ontologies don’t solve the ‘problem’ that people have multiple views on the world. That there are multiple views, that people don’t agree. It gives us a tool to make our view explicit, to explain the meaning of our terms. So we have something clear to agree on. There is no uniform way to classify the world! You can’t just merge ontologies. Ontologies change.
  • Broader public. It is surprising what you can do with just a few links Let op: geen VERANDERING maar TOEVOEGING
  • Differences between sameAs and EquivalentClass? Strong assumption when you use sameAs or EquivalentClass. Examples: editorialnotes, restrictions, domain range. Subclasses if you know your own vocab + instances, but less about the other. Are all instances of the other also instances of my class? Don’t know? Subclass.
  • For thesauri. Parallel with skos:semanticRelation. What are the characteristics of these properties? Think of symmetric, inverse, transitive properties, subproperties? C losematch: they can be used interchangeably in some information retrieval applications E xactmatch = transitiveprop, = subprop of closematch. skos:broadMatch is a sub-property of skos:broader, etc. SO: we can also use skos:semanticRelations for mapping. skos:narrowMatch is owl:inverseOf the property skos:broadMatch. skos:relatedMatch, skos:closeMatch and skos:exactMatch are each instances of owl:SymmetricProperty. Difference between skos:exactmatch en equivalentclass?
  • Lelijk, maar: Uitwerking van vraag op vorige slide.
  • One to many
  • What is mapping here? aat:artist broader wn:artist.
  • Much smaller field. Painter (role in ULAN?) is more specific than Creator (VRA) Trick: wordnet:hyponym subpropertyOf rdfs:subClassOf (visualization, treating as transitivity)
  • E culture demo pop-art. K eith haring. Part of in many domains. TODO: example of domain specific match.
  • Active field, automatically finding mappings
  • 1. Talk about prefix, suffix, edit distance, n-gram. 2. Tokenization (split into words without punctuation), lemmatization (reduce to its basic form) , stopping. 3. Or use the alignment to another thesaurus. 4. Bounded path matching These take two paths with links between classes defined by the hierarchical relations, compare terms and their positions along these paths, and identify similar terms 4. Super(sub)-concepts rules: If super-concepts are the same, the actual concepts are similar to each other 4. If two nodes from two schemas/ontologies are similar, their neighbors might also be somehow Similar Teken zonodig voorbeeld onto op bb.
  • What could be a way to find part-of relations? Willems work Could also learn these patterns from know part-whole combinations. Also for subclassof, then they are called hearsh patterns.
  • What is an instance, class? Example of different models of wn-dbpedia on the blackboard. W n: synsets have wordsenses, who have words, who have lexical forms. Hyponym, part of relation between synsets. WIKIPEDIA: things corresponding to a wikipedia page, who ha ve abstracts, titles, categories. Categories have subcategories, hierarchy. No direct hierarchy between pages.
  • Comparing the logical consequences of the models: comparing full transitive closure of a mapping to full transitive closure of the reference alignment. Mappings to a concept that is too broad or too narrow are now still party good. End to end is similar to competency questions: what do I need to do with my app? Does it do it better with this mapping or this one?
  • What are the mappings here? Plant material (wn) subclassof plant material (aat). Wood – exactmatch – wood(plant material) SAAI.
  • END

Ontology engineering: Ontology alignment Ontology engineering: Ontology alignment Presentation Transcript

  • Ontology AlignmentCourse “Ontology Engineering”
  • Goals of the lecture Understand why ontology alignment is done Know what constructs can be used to express an alignment between two concepts Know what options there are to find mappings 2
  • Agenda Why ontology alignment? Alignment relations Alignment techniques 3
  • Why is Ontology Alignment done? 4
  • Interoperability problem IIA private company wants to participate in a marketplaceE.g. eBay:Home > Buy > Cameras & Photo > Digital Cameras > Digital SLR > Nikon > D40Needed: correspondences between entries of its catalogs and entries of a common catalog of a marketplace. 5
  • Example use of vocabulary alignment “Tokugawa” AAT style/period SVCN period Edo (Japanese period) Edo TokugawaAAT is Getty’s SVCN is local in-houseArt & Architecture Thesaurus ethnology thesaurus
  • Alignment architecture for P2P
  • Two kinds of interoperability Syntactic interoperability – using data formats that you can share – XML family is the preferred option Semantic interoperability – How to share meaning / concepts – Technology for finding and representing semantic links 8
  • Reusing vocabularies 9
  • The myth of a unified vocabulary There will always be multiple ontologies Partly overlapping In multiple languages Each with their own perspective 10
  • Links between ontologies “Ontology Alignment” / “Ontology Mapping” – use ontologies jointly by defining a limited set of links – Benefit from knowledge encoded in the other ontology – Enable access across applications/collections. – Partial by nature! 11
  • Why ontology alignment?Summary: There is no single ontology of the world People work with different viewpoints and thus multiple conceptualizations But: these concepts often overlap Semantic relations between ontologies help integrating information sources Currently seen as a major issue in development of distributed (web) systems 12
  • How do we represent the alignment between two concepts? 13
  • Link types between concepts in different ontologiesEquality Individual → individualowl:sameAs “Den Haag” = “The Hague”Equivalence class → classowl:EquivalentClass wood-material = woodSubclass class → classrdfs:subClassOf aat:Artist ⊇ wn:ArtistInstance of individual → classrdf:type tgn:Africa ∈ wn:ContinentDisjoint class → classowl:disjointWith aat:wood ⊥ wn:plastic 14
  • Types of links between concepts in different thesauriskos:mappingRelation - skos:closeMatch - skos:exactMatch - skos:broadMatch - skos:narrowMatch - skos:relatedMatch 15
  • SKOS mapping properties- skos:closeMatch - skos:narrowMatch - symmetricProperty -subPropertyOf- skos:exactMatch skos:narrower - subPropertyOf -inverseOf skos:closeMatch skos:broadMatch - transitiveProperty - symmetric property -skos:relatedMatch -subPropertyOf- skos:broadMatch skos:related - subPropertyOf skos:broader -symmetric property - inverseOf skos:narrowMatch 16
  • Example: partial alignment between citations 17
  • Example: alignment between XML Schemas 18
  • Example: alignment between thesauri 19
  • Types of links between properties in different ontologiesLinks between properties: equivalentProperty subPropertyOf inverseOfE.g. painterOf – creatorOf Trick: wn:hyponym subPropertyOf rdfs:subClassOf 20
  • Types of links between concepts in different ontologies Domain-specific links – Van Gogh (ULAN) born-in Groot-Zundert (TGN) – Derain (ULAN) related-to Fauve (AAT)) – Wandelkaart Pyreneeën RANDO.07 Haute- Ariège - Vicdessos (Pied à Terre) related to Pyrénées (TGN) – Part-of relations 21
  • Alignment Techniques 22
  • Alignment tools Input: two ontologies, each consisting of a set of discrete entities • HTML table headers • XML elements • Classes • Properties Output: relationships holding between these entities (equivalence, subsumption, etc.) + confidence measure. Cardinality (e.g., 1:1, 1:m) 23
  • Alignment techniques Syntax: comparison of characters of the terms – Measures of syntactic distance – Language processing • E.g. Tokenization, single/plural, Relate to lexical resource – Relate terms to place in WordNet hierarchy Taxonomy comparison – Look for common parents/children in taxonomy Instance based mapping – Two classes are similar if their instances are similar. 24
  • String-based techniques (1) Exact string match Prefix – takes as input two strings and checks whether the first string starts with the second one – net = network; but also hot = hotel Suffix – takes as input two strings and checks whether the first string ends with the second one – ID = PID; but also word = sword
  • String-based techniques (2) Edit distance – takes as input two strings and calculates the number of edition operations, (e.g., insertions, deletions, substitutions) of characters – required to transform one string into another, normalized by length of the maximum string – EditDistance ( NKN , Nikon ) = 0.4 (2/5)
  • Language-based techniques Tokenization – parses names into tokens by recognizing punctuation, cases – Hands-Free Kits => hands, free, kits Lemmatization – analyses morphologically tokens in order to find all their possible basic forms – Kits => Kit Elimination – discards “empty” tokens that are articles, prepositions, conjunctions . . . – a, the, by, type of, their, from
  • Linguistic techniques using WordNet senses A subClassOf B if A is a hyponym of B – Pine subClassOf Tree A hasPart B if A is a holonym of B – Europe hasPart Greece A = B if they are synonyms – Quantity = Amount A disjoint B if they are antonyms or ar siblings in the same part of hierarchy – Pine disjoint Oak
  • Linguistic techniques: gloss-based WordNet gloss comparison – The number of the same words occurring in both input glosses increases the similarity value. – The equivalence relation is returned if the resulting similarity value exceeds a given threshold – Maltese dog is a breed of toy dogs having a long straight silky white coat Afghan hound is a tall graceful breed of hound with a long silky coat
  • Structural technique:taxonomy comparison
  • Techniques for Part-of RelationsPhrase (Hearst) patterns: add <part> to <whole> <whole> is made of <part> <part> gives the <whole> its <whole>-containing <part> <whole> consists of <part> 31
  • Overview of alignment techniques
  • Alignment issues (1) Nature of the input – Underlying data models – Schema-level vs. Instance-level – Example: Link WordNet to Wikipedia Interpretation of the output – Approximate vs. exact – Graded vs. absolute confidence Performance varies > semi-automatic alignment. 33
  • Involving the human in alignment evaluation
  • Evaluation of alignments Judging individual alignments – Precision Comparison to a reference alignment – Recall – Precision? Comparing the logical consequences of the models End-to-end evaluation 35
  • The intrinsic fuzziness of alignment
  • AATWordNet 37
  • Literature / acknowledgment Some slides from this lecture are based on a tutorial of Pavel Shvaiko and Jerome Euzenathttp://dit.unitn.it/~accord/Presentations/ESWC05 Some slides are from Antoine Isaac (STICH) 38