PRIMERA INFANCIA, ETAPA CLAVE

A mapping problem occurs when there are different representations of similar information. To allow for the information to be shared a mapping must be constructed in order to transform one representation into another. The mapping will allow information to be understood with each representation. Ontology mapping is one solution to this semantic heterogeneity problem [Shvaiko 2008]. Matching is the generation of a set of candidate correspondences between semantic models. These candidate correspondences need to be validated before they become correspondences. A mapping between ontologies consists of a set of correspondences between semantically related entities. Each correspondence is formally defined as a 5-tuple <id, e1, e2, n, r> [Euzenat 2006]: where id is the unique identifier of the correspondence, e1 is an entity in the source ontology, e2 is an entity in the target ontology, r is the relation between entities, and n is the confidence measure that the correspondence holds for e1 and e2.and is generally a value between 0 and 1. The relationship is typically one of equivalence, more general, less general, disjointed, or overlapping [Shvaiko 2008]. However the exact relationship specified is often application dependent.

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2.1.1 Motivating Example

Figure 2-1 shows a snapshot of a partial mapping between two ontologies. On the left is the source ontology which is a partial branch of the university ontology for the University of Aberdeen [Aberdeen 2003]. On the right is the target ontology which is a partial branch of the university ontology for the University of Manchester [Manchester 2003]. The ontologies are provided in the accompanying DVD media under SOA.

Figure 2-1: Example of partial portion of a mapping between Aberdeen University ontology and Manchester University ontology. Entities from both source and target ontology involved in the mapping are bounded by ellipses and correspondences are represented by the solid curved arcs.

The ontologies are both modelled as a different structure and uses a different vocabulary to represent similar information. This difference can be due to both the differing needs of each ontology and the fact that different people, who would have differing views [Lewotin 1982],

Manchester University Person Student PhdStudent supervisor Employee AcademicStaff Researcher cooperatesWith memberOf researchinterest Lecturer AdministrativeStaff Secretary secretaryOf TechnicalStaff Aberdeen University Person Affiliated-Person Student PhD-Student has-supervisor studies-at has-affiliation Working-Person Employee Educational-Employee Academic Lecturer-In-Academia works-for Professor-In-Academia works-for Senior-Lecturer-In-Academia works-for Secretary System-Administrator

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were used to build each ontology. An example of two entities which use different vocabulary but model similar information is ‘System-Administrator’ from the Aberdeen ontology and ‘TechnicalStaff’ from the Manchester ontology. The two ontologies also contain many of the same concepts but the concepts are sometimes represented differently, for example ‘PhD Student’ is represented differently in both ontologies. The Aberdeen ontology represents ‘PhD Student’ as a sub class of ‘Student’ with the properties has-supervisor, studies-at and has- affiliation. While the Manchester ontology represents ‘PhD Student’ as a sub class of ‘Student’ with the property supervisor. Both concepts have different properties and the different class based structure, ‘Student’ is a sub class of ‘Affiliated-Person’ in the Aberdeen ontology but is not in the Manchester ontology. This heterogeneity causes problems when information needs to be shared or exchanged. To resolve this semantic interoperability problem, a mapping needs to be constructed between the two ontologies. However, developing a mapping is a difficult process.

2.1.2 Difficulty in Mapping

The study of mapping problems is not new in computer science and has been persistent through various different areas from theoretical computing [Hertling 1999] to the database community [Konstantinou 2008]. In fact people deal with mapping problems everyday with examples being both reading and interpreting our surroundings. Ontology mapping is closely related to the mapping problems we encounter every day, i.e. mapping one person’s view of the world to another person’s view of the world. However, ontologies are quite limited when expressing representations as they are based on the classical view of categories [Murphy 2002]. This classical view has three main claims: first that concepts are mentally represented definitions which provide the necessary conditions for membership in this category, second that every object is either in or not in a category, and finally that each member of a category is equally good, that is, a member cannot be a more typical member than another member.

One of the main problems using this approach is that it is very difficult to define concepts through necessary and sufficient conditions. For example “dog” can be defined to be an animal that has four legs, barks, has fur, eats meat but this is not a valid definition as there are dogs with less than for legs and dogs that are furless [Murphy 2002]. Also studies have shown that people have difficulty assessing category membership by not being able to segregate items into clear members and non-members [Hampton 1979]. These issues can lead to similar problems in mapping where the same term is structured and verbalised differently. Also the underlying data language used for specifying the ontology (e.g. OWL, RDF) introduces added problems as they constrain the expressiveness. For example, many formats lack information relating to the

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context of use [Bernstein 2007]. Without knowing the context of use for a concept it can be difficult to understand what is the meaning of the concept. For example ‘Football’ can have different meanings based on the perception of different cultures, e.g. in Europe against in USA. This is especially problematic in mapping as the context of both terms needs to be understood. Mismatches between ontologies are the key type of problem that hinders the combined use of independently developed ontologies [Klein 2001]. Figure 2-2 displays Klein’s classification for ontology mismatches.

Figure 2-2: Classification of Ontology Mismatches [Klein 2001]

The “language level” distinguishes mismatches of the language primitives that are used to specify the ontology. The “ontology level” distinguishes the differences in the way the representations of ontologies are modelled and is subdivided into conceptualisation and explication mismatches. “Conceptualisation Mismatches” relate to the difference in the way a representation is interpreted and is subdivided into Scope, two classes may seem to be the same but are modelled differently, and Coverage & Granularity, appear to be modelling the part of the same domain but at different levels and detail. “Explication Mismatches” represent a difference in the way the conceptualisation is specified, covering style of modelling,

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terminological and encoding mismatches. As recognised by Klein [Klein 2001], it relies on human expertise to recognise ontology mismatches.

All these problems make mapping generation a very challenging problem. Despite significant research on the development of matching algorithms, it has become obvious that the user must accept a degree of imperfection [Gal 2005]. A couple of prime reasons for this is the enormous ambiguity and heterogeneity of concept descriptions of data [Gal 2006] and the description of a concept in a schema can be semantically misleading [Miller 2000]. This has lead to automatic mapping being seen as impracticable [Noy 2004] and there remaining a need for “a human to be in the loop” [Hull 1997] [Shvaiko 2008] [Falconer 2009].