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Ontology matching



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  • 1. Ontology matching Ícaro Medeiros Jaumir Valença da Silveira Franklin Amorim Pedro Henrique
  • 2. Outline● Context● Definitions● Classifications of Ontology Matching Techniques● Basic Techniques● Matching Strategies
  • 3. Bibliography[1] Jerome Euzenat and Pavel Shvaiko. 2010. Ontology Matching (1st ed.).Springer Publishing Company, Incorporated.[2] Namyoun Choi, Il-Yeol Song, and Hyoil Han. 2006. A survey on ontologymapping. SIGMOD Rec.35, 3 (September 2006), 34-41.[3] Yannis Kalfoglou and Marco Schorlemmer. 2003. Ontology mapping: thestate of the art. Knowl. Eng. Rev. 18, 1 (January 2003), 1-31.[4] Noy, N., 2005. Ontology Mapping and Alignment. Search, p.1-34. Availableat:[5] Casanova, M. A., 2012. Tecnologias de Banco de Dados para a WebSemântica - Módulo 9a - Ontologias - Matching.
  • 4. Outline● Context● Definitions● Classifications of Ontology Matching Techniques● Basic Techniques● Matching Strategies
  • 5. Context● We have to deal with heterogeneity● Different models are based on different domains of knowledge and use different tools, at different detail levels● Distributed nature of ontology development has lead to different ontologies in the same or overlapping domains
  • 6. The need for ontology matching● Creating global ontologies from local ontologies● Reuse information between ontologies● Dealing with heterogeneity● Queries across multiple distributed resources● Data transformation
  • 7. Outline● Context● Definitions● Classifications of Ontology Matching Techniques● Basic Techniques● Matching Strategies
  • 8. What is ontology matching?It is the process of finding relationshipsor correspondences between entities of different ontologies.entities - classes, instances, properties or formulas
  • 9. Other terms used
  • 10. The matching processOntologies o and oAlignment AParameters Alignment AResources
  • 11. Ontology matching example
  • 12. Outline● Context● Definitions● Classifications of Ontology Matching Techniques● Basic Techniques● Matching Strategies
  • 13. Classifying ontology matching in regard to the use● Matching local ontologies to global ontologies● Matching ontologies of complementary domains● Merging two ontologies of the same domain
  • 14. Synthetic Classifications● Granularity/Input Interpretation Layer ○ e.g. element- or structure-level● Kind of Input Layer ○ Classification based on the kind of input used by elementary matching techniques● Basic Techniques Layer ○ Classification based on how input information is interpreted
  • 15. Granularity/Input Interpretation Layer● Element-level matching techniques ○ Analysing entities or instances in isolation ○ Ignoring their relations with other entities or their instances● Structure-level techniques ○ Analysing how entities or their instances appear together in a structure (e.g. by representing ontologies as a graph)
  • 16. Granularity/Input Interpretation LayerSyntactic techniques ○ Interpret the input with regard to its sole structureExternal techniques ○ Uses external resources of a domain and common knowledgeSemantic techniques ○ Interpret the input by using model-theoretic semantics
  • 17. Kind of Input Layer● Terminological ○ Strings found in the ontology descriptions● Structural ○ Structures found in the ontology descriptions● Semantics ○ Requires some semantic interpretation of the ontology● Extensional ○ Use data instances● In some papers, semantic=logic; extensional=semantic
  • 18. Kind of Input Layer (Second level)● Terminological ○ String-based: terms as sequences of characters ○ Linguistic: interpretation of the terms as linguistic objects● Structural ○ Internal: consider the internal structure of entities ○ Relational: consider the relation of entities with other entities
  • 19. Basic Techniques LayerA label can be interpreted as ○ A string (a sequence of letters) ○ A word or a phrase in some natural languageA hierarchy can be considered as ○ A graph ○ A taxonomy
  • 20. Basic Techniques LayerElement-level ● String-based ● Language-based ● Based on linguistic resources ● Constraint-based ● Alignment reuse ● Based on upper level and domain specific formal ontologies
  • 21. Basic Techniques LayerStructure-level ● Graph-based ● Taxonomy-based
  • 22. Element-level Techniques● String-based techniques ● The more similar the strings, the more likely they are to denote the same concepts ● Distance functions map a pair of strings to a real number● Language-based techniques ● Based on natural language processing techniques exploiting morphological properties of the input words
  • 23. Element-level Techniques● Constraint-based techniques ● Deal with the internal constraints being applied to the definitions of entities, such as types, cardinality of attributes, etc● Linguistic resources ● Lexicons or domain specific thesauri, used to match words based on linguistic relations between them like synonyms, hyponyms, etc
  • 24. Element-level Techniques● Alignment reuse ● Record alignments of previously matched ontologies● Upper level and domain specific ontologies ● Used as external sources of common knowledge
  • 25. Structure-level Techniques● Graph-based techniques ● Treat input ontologies as labelled graphs ● If two nodes from two ontologies are similar, their neighbours may also be somehow similar● Taxonomy-based techniques ● is-a links connect terms that are already similar, therefore their neighbours may be also somehow similar
  • 26. Outline● Context● Definitions● Classifications of Ontology Matching Techniques● Basic Techniques● Matching Strategies
  • 27. Basic Techniques● Examples of metrics: Similarity and Distance● Name-based techniques● Structure-based techniques● Extensional techniques● Semantic-based techniques
  • 28. Basic TechniquesSimilarity: Function from a pair of entities to a real number
  • 29. Name-based Techniques● They can be applied to the name, the label or the comments of entities in order to find those which are similar● They can be used for comparing class names and/or URIs
  • 30. String-based methods● Based on string similarity only● Useful if conceptual schemas (or ontologies) use very similar strings to denote the same concepts● Yield a low similarity, if schemas use synonyms with different syntax● Yield many false positives, if pairs of strings with low similarity are selected
  • 31. String-based methodsString distance functions:
  • 32. String-based methodsLevenshtein (edit) distance ● Measure the similarity between two strings by the minimum number of insertions, deletions, and substitutions of characters required to transform one string into the other ● Example:(“Gaming”, “Games”) = 2 substitutions [“e” by “i” and “n” by “s”] + 1 deletion [“g”] =3
  • 33. String-based methodsToken-based distance ● Usually applied to the complete description of a concept ● Treats strings as a bag of words (multisets of substrings) ● May split strings into independent tokens ● Example: "InProceedings" is represented by ● the bag of words {In, Proceedings} ● or a bag of substrings of length 3 {InP, roc, eed, ing, s}
  • 34. String-based methodsBag of words represented as a vector ● Each dimension corresponds to a token ● Each position of the vector is the number of occurrences of the token
  • 35. Cosine Similarity Ontology Ontology Mapping, ontology mapping=(1,1) mapping=(1,2) 1 Mapping 1 2V = {"Ontology", "Mapping" }
  • 36. Language-based methodsIntrinsic methods ● reduce each term to a normal form to facilitate matching ● use traditional natural language processing techniques ● stopword elimination ● tokenization: segment strings into sequences of tokens ● lemmatization: reduce words to normal forms ● suppress tense, gender and number
  • 37. Language-based methodsExample – Variants of the term “theory paper”
  • 38. Language-based methodsExtrinsic methodsUse dictionaries, lexicons and terminologies tohelp match terms from different schemas orontologies ● e.g. a terminology - a thesaurus which very often contains phrases rather than single words ● deal with synonyms ● word sense disambiguation
  • 39. Language-based methodsWordNet – an example of an external resource● ● an electronic lexical database for English ● based on the notion of synsets (sets of synonyms) ● a synset denotes a concept or a sense of a group of terms ● WordNet also provides: ● an hypernym structure (superconcept / subconcept) ● a meronym relation (part of) ● textual descriptions of the concepts (glossary)
  • 40. Language-based methods● Example ● WordNet 2.0 entry for the word author author1 noun: Someone who originates or causes or initiates something; Example ‘he was the generator of several complaints’. Synonym generator, source. Hypernym maker. Hyponym coiner. author2 noun: Writes (books or stories or articles or the like) professionally (for pay). Synonym writer2. Hypernym communicator. Hyponym abstractor, alliterator, authoress, biographer, coauthor, commentator, contributor, cyberpunk, drafter, dramatist, encyclopedist, essayist, folk writer, framer, gagman, ghostwriter, Gothic romancer, hack, journalist, libretist, lyricist, novelist, pamphleter, paragrapher, poet, polemist, rhymer, scriptwriter, space writer, speechwriter, tragedian, wordmonger, word-painter, wordsmith, Andersen, Assimov... author3 verb.: Be the author of; Example ‘She authored this play’. Hypernym write. Hyponym co-author, ghost.
  • 41. Language-based methods● Example ● fragment of the WordNet hierarchy (limited to nouns) for “illustrator”, “author”, “creator”, “person”, “writer” (“author”) = {A1, A2W2} (“writer”) = {W1, A2W2, W3}
  • 42. Language-based methodsExample – Synonym Similarity● (s,t) = 1 iff (s) (t) (terms have a synset in common) = 0 otherwise (“author”) = {A1, A2W2} (“writer”) = {W1, A2W2, W3} (“author”) (“writer”)
  • 43. Language-based methodsExample – Co-synonymy similarity● ’(s,t) = | (s) (t)| | (s) (t)| (“author”) = {A1, A2W2} (“writer”) = {W1, A2W2, W3} (“author”) (“writer”) = 1 (“author”) (“writer”) = 4
  • 44. Structure-based techniquesInternal structure (constraint-based approaches)● based on the internal structure of classes● calculate the similarity between two classes based on ○ the set of their properties, including keys ○ the range of their properties (attributes and relations) ○ the cardinality of their properties ○ the transitivity or symmetry of their properties
  • 45. Structure-based techniquesInternal structure (constraint-based approaches)
  • 46. Structure-based techniquesInternal structure (constraint-based approaches) ● positive point: ● can be used to eliminate incompatible matches ● negative points: ● does not provide much information about the classes to compare ● different classes may have properties with the same datatypes ● different models of a concept use different, and incompatible, types ● approach suggested: ● use method in combination with other methods
  • 47. Structure-based techniquesRelational Structure● similarity between two concepts● based on the relations between the concepts with other concepts ○ similar concepts should have similar related concepts● given a relation r, a pair of concepts may be: ○ directly related through r ○ inversely related through r ○ transitively related through r ○ the maximal elements of r+
  • 48. Structure-based techniquesExample subclass(Book) = {Science, Pocket, Children} subclass−1(Book) = {Product} subclass+(Book) = {Science, Pocket, Textbook, Popular, Children} subclass ↑ (Book) = {Textbook, Popular, Pocket, Children}
  • 49. Structure-based techniquesTaxonomic Structure● Similarity between two concepts ○ Based on the graph of the subClassOf relation ○ Example ■ (e,e’) = number of edges of the taxonomy between e and e’, normalized by dividing by the longest path
  • 50. Structure-based techniquesBounded path matchers ● use anchors relating paths from two distinct taxonomies ● take two paths with links from two distinct taxonomies ● compare terms and their positions along these paths ● identify similar terms
  • 51. Structure-based techniquesExample “Book -> Volume” and “Popular -> Autobiography” implies that possibly “Science -> Biography” or “Science -> Essay”
  • 52. Structure-based techniquesSummary of relational structure methods● Powerful methods to match conceptual schemas and ontologies ○ Allow relations between concepts to be taken into account● Often used in combination with internal structural and terminological methods
  • 53. Extensional techniquesWhen two ontologies share the same set ofindividuals, matching is highly facilitated.
  • 54. Extensional techniques● Jaccard Similarity: Given two sets A and B, let P(X) be the probability of a random instance to be in the set X.● Note that the Jaccard Similarity reaches 1 when A = B and 0 when they are disjoint.
  • 55. Semantic-based techniques● Semantic-based techniques rely on using the axioms of ontologies and deductive methods.● But for an inductive task like ontology matching, they do not perform well alone. So, a preprocessing is needed.● Therefore, we need, firstly, to suppress the lack of a common ground between the ontologies.● For those reasons, authors propose the use of semantic techniques in two steps: the so-called anchoring step and the deriving relations step.
  • 56. Semantic-based techniques● Anchoring: is matching ontologies o and o to the background ontology o. This can be done using any method described so far.● Deriving relations: is the (indirect) matching of ontologies o and o by using the correspondences discovered during the anchoring step.● Example: Micro-company: Has at most 5 employees. SME: Has at most 10 associates. anchoring: employees ---> EMPLOYEE <--- associates Micro-company ---> FIRM <--- SME deriving relations: Micro-company is a subclass of SME.
  • 57. Outline● Context● Definitions● Classifications of Ontology Matching Techniques● Basic Techniques● Matching Strategies
  • 58. Matching strategies - GlobalMethods● Aggregating the results of the basic methods● Developing a strategy for computing these similarities● Learning from data the best method and the best parameters for matching● Using probabilistic methods to combine matchers or to derive missing correspondences● Involving users in the loop● Extracting the alignments from the resulting (dis)similarity
  • 59. Matcher composition● Sequential composition of matchers
  • 60. Matcher composition● Using matrices to represents a similarity or distance measure between entities to be matched
  • 61. Matcher composition● Parallel composition of matchers
  • 62. Similarity aggregationCompound similarity is concerned with theaggregation of heterogeneous similarities ○ e.g. A single similarity measure composed by the similarity obtained from their names, the similarity of their superclasses, the similarity of their instances and that of their properties