by Murat Şensoy murat.sensoy@boun.edu.tr Ontology Alignment by Murat Şensoy murat.sensoy@boun.edu.tr

Outline Introduction to Ontologies Ontology Alignment Current Approaches for Ontology Alignment Using Ontology Alignment in Service Selection

Introduction “Ontology is a formal specification of a conceptualization.” Gruber, 1993

Ontologies Ontologies are about vocabularies and their meanings, with explicit, expressive, and well-defined semantics, possibly machine-interpretable. Main elements of an ontology: Concepts Relationships Hierarchical Logical Properties Instances (individuals)

Meaning is in Connections is made from G a m Grape e a i e n r f W m i s p o e r Wine d

For machines... Wine is made from Grape The meaning of the document is not defined. Machines cannot understand it. Wine is made from Grape We are defining the structure of document by XML but now the meaning of the structure is not defined. <Sentence> <Subject> Wine </Subject> <Verb> is made from </Verb> <Object> Grape </Object> </Sentence> XML document

Ontology gives the meaning... Natural Language Document Ontology <Sentence> <Subject> </Subject> <Verb> </Verb> <Object> </Object> </Sentence> Wine is made from Grape

Ontology Alignment Problem Ontology is used to support interoperability and common understanding between different parties. Ontologies themselves may have some heterogeneities. Ontology Alignment is needed to find semantic relationships among entities of ontologies. ? d c b a How should I use them? !!!

An Example of Alignment Car : Ontology A ( ? ) Automobile : Ontology B

An Example of Ontology Merging Object Luxury Car Family Car Sport Car Vehicle Car Bus BMW Family Car Porsche Sport Car Automobile Thing

An Example of Ontology Merging Object Thing Vehicle Automobile Bus Car Sport Car Family Car Family Car Luxury Car Sport Car Porsche BMW

An Example of Ontology Merging Object Luxury Car Family Car Sport Car Vehicle Car Bus BMW Thing Automobile Sport Car Family Car Porsche

An Example of Ontology Merging Object, Thing Vehicle Bus Car, Automobile Sport Car Luxury Car Family Car BMW Porsche

Heterogeneity in Ontologies Coverage: cover different portions – possibly overlapping– of the world. Granularity: One ontology provides a more (or less) detailed description of the same entities. Perspective: an ontology may provide a viewpoint, which is different from the viewpoint adopted in another ontology.

Overcoming Heterogeneity Using Similarity Terminological Methods String Based Methods Token Based Methods Language Based Methods Structural Methods Internal Structure External Structure Extensional (based on instances) Methods When the classes share the same instances When they do not

Terminological Methods Terminological methods compare strings. Can be applied to: name, label comments concerning entities URI Take advantage of the structure of the string (as a sequence of letter). The main idea in using such measures is the fact that usually similar entities have similar names and descriptions in different ontologies.

Terminological M., cont. (String Based) Substring Similarity Hamming Distance N-Gram Distance Edit Distance Jaro Similarity

Terminological M., cont (String Methods) In string edit distance, the operations usually considered are insertion of a character, replacement of a character by another and deletion of a character. Levenstein Distance is an Edit Distance with all costs to 1.

Terminological M., cont. (Language Based) Rely on using NLP techniques to find associations between instances of concepts or classes. Intrinsic methods: perform the terminological matching with the help of morphological and syntactic analysis to perform term normalization. (Stemming) : going  go Extrinsic methods: make use of external resources such as dictionaries and lexicons (Wordnet).

Structural Methods The structure of entities that can be found in ontology can be compared, instead of comparing their names or identifiers. Internal Structure: use criteria such as the range of their properties (attributes and relations), their cardinality, and the transitivity and/or symmetry of their properties to calculate the similarity between them. External Structure: The similarity comparison between two entities from two ontologies can be based on the position of entities within their hierarchies.

Structural Methods (External) If two entities from two ontologies are similar, their neighbors might also be somehow similar. Criteria for deciding that the two entities are similar include: Their direct super-entities are already similar. Their sibling-entities are already similar. Their direct sub-entities are already similar. All (or most) of their descendant-entities (entities in the sub tree rooted at the entity in question) are already similar. All (or most) of their leaf-entities are already similar. All (or most) of entities in the paths from the root to the entities in question are already similar.

Extensional (based on instances) Methods Compares the extension of classes, i.e., their set of instances rather than their interpretation. These techniques can be used when the classes share the same instances

Using Learning Methods Supervised learning can be used for ontology alignment. Ontology alignment algorithm learns how to work through the presentation of many good alignment (positive examples) and bad alignments (negative examples).

Example Suppose Ag1 intends to convey van(a) Ag1 : AddConcept(van) Provided Examples vehicle van hotel vehicle van hotel roadvehicle campervan roadvehicle campervan van Not van Suppose Ag1 intends to convey van(a) Ag1 : AddConcept(van) Ag1 : Provide negative/pozitif examples Ag2 interprets van as a subclass of roadvehicle and superclass of campervan

Existing Works OntoMorph U.S. Army Smart Chimaera Prompt InfoSlueth Method Year Organization Project Leader Automatic Features Aggregation Lexical Structure String Semantic Instance OntoMorph 1997 S. California Chalupsky Semi T   U.S. Army 1999 DARPA Smart Sanford Fridman, Noy Chimaera Stanford McGuinness Prompt 2001 Noy, Musen InfoSlueth Amsterdam Ding A. Prompt 2002 Glue Illinois Doan IF Map 2003 Southampton Kafoglou NOM Karlsruhe Ehric QOM 2004 CROSI 2005

Service Selection In the problem of service selection, consumer agents cooperate to identify service providers that would satisfy their service needs the most.

Ontologies evolve separately depending on the needs of the consumers Service Selection Consumers communicate about their service needs. Different consumers may have different service needs Some consumers may come up with new service needs Consumer Agent 1 Consumer Agent 2 Ontologies evolve separately depending on the needs of the consumers

Service Selection Consumer1 requests information related to “Buying over internet using credit card” Consumer2 does not understand the request Consumer2 should learn what Consumer1 means. How can consumer2 add the concept to its own ontology? Using the terminological methods: syntax of the concept etc. Using structural methods: properties of the concept Using the instances related to the concept

Service Selection Terminological methods may not be used, concepts with similar meaning may be labeled highly different. Especially in our case, agents creates new concepts and name of these concepts may be irrelevant to semantics. Structural methods are good candidates, because services are already defined in terms of their properties and these properties can be used to map different service concepts or service needs. Instance-based methods are good candidates. However, in this context, what is an instance?

Service Selection In current approaches, ontologies evolve separately. This results in distinct ontologies. It may be a good idea to evolve ontologies cooperatively. This results in overlapping ontologies. Advantages: Ontologies are aligned over time Useful concepts emerge rapidly

The End THANK YOU

The Challenge of Communication