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UML As an Ontology Modelling Language

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UML As an Ontology Modelling Language UML as an Ontology Modelling Language Stephen Cranefield and Martin Purvis Department of Information Science University of Otago PO Box 56, Dunedin, New Zealand g E-mail: fscranefield, mpurvis @infoscience.otago.ac.nz Abstract of standard object modelling techniques for ontology devel- opment. Current tools and techniques for ontology develop- This work is motivated primarily by consideration of the ment are based on the traditions of AI knowledge role that ontologies play in agent-based infrastructures for representation research. This research has led to supporting queries over open and dynamic collections of het- popular formalisms such as KIF and KL-ONE style erogeneous and distributed information sources. Systems languages. However, these representations are lit- such as SIMS [Knoblock and Ambite, 1997], Infosleuth [Ba- tle known outside AI research laboratories. In con- yardo et al., 1997] and Observer [Mena et al., 1999] use on- trast, commercial interest has resulted in ideas from tologies to model the semantic structure of individual infor- the object-oriented programming community ma- mation sources, as well as to describe models of a domain that turing into industry standards and powerful tools are independent of any particular information source. The for object-oriented analysis, design and implemen- challenges for these systems are to support the construction tation. These standards and tools have a wide and of user queries using domain ontologies that may be initially rapidly growing user community. This paper ex- unfamiliar to the user, and to allow queries to span multi- amines the potential for object-oriented standards ple information sources by representing and computing the to be used for ontology modelling, and in particular mappings between domain ontologies and the ontologies sup- presents an ontology representation language based ported by individual information sources. on a subset of the Unified Modeling Language to- gether with its associated Object Constraint Lan- guage. 2 Common Ontology Modelling Languages The most common formalisms used to represent ontolo- gies are the Knowledge Interchange Format (KIF) [NCITS, 1 Introduction 1998] and KL-ONE style knowledge representation lan- In recent years a number of subfields of artificial intelligence guages [Brachman and Schmolze, 1985]. have been aiming to increase the ability of their systems to KIF provides a Lisp-like syntax for expressing sentences interact with humans and other external agents by developing of first order predicate logic and also provides extensions for and sharing ontologies— formally specified models of bod- representing definitions and metaknowledge. KIF is a highly ies of knowledge defining the concepts used to describe a expressive but low-level language for representing ontolo- domain and the relationships that hold between them. Re- gies; however, the Stanford University Knowledge Sharing search areas investigating the design of ontologies include Laboratory’s ontology editing tool, Ontolingua [Farquhar et agent-based software interoperability [Genesereth and Ketch- al., 1996], allows users to build KIF ontologies at a higher pel, 1994], knowledge acquisition [SMI, 1998] and natural level of description by importing predefined ontologies defin- language processing [Bateman et al., 1995]. ing concepts such as sets, standard units, time and simple ge- Various formalisms have been developed for expressing ometrical functions. In particular, the frame ontology [KSL, ontologies, notably the Knowledge Interchange Format (KIF) 1994] allows ontologies to be defined in terms of relations, [NCITS, 1998] and knowledge representation languages de- classes (and subclasses), functions and sets. scended from KL-ONE [Brachman and Schmolze, 1985].In Much of the research on ontology design and use is per- this paper we examine the use of an alternative formalism for formed by researchers using knowledge representation tools representing ontologies: a subset of the Object Management descended from KL-ONE [Brachman and Schmolze, 1985]. Group’s Unified Modelling Language (UML) together with KL-ONE was the basis for much work in the field of knowl- its associated Object Constraint Language (OCL). Object- edge representation. It implemented “structural inheritance oriented analysis, design and implementation is a maturing networks”: networks containing descriptions of named con- field with many industry standards emerging for distributed cepts with generalisation/specialisation links between them. computation. The large user community and commercial sup- Descendants of KL-ONE include LOOM [ISI, 1998] and a port for object-oriented standards warrants the investigation family of logics called description logics or terminological logics [Donini et al., 1996; Owsnicki-Klewe, 1990].TheKIF formal characterisation of the representational and deductive frame ontology discussed above also allows this type of spec- capabilities of KL-ONE style systems and allow their com- ification to be used in conjunction with more general KIF sen- putations to be studied in terms of completeness, computa- tences. tional complexity, etc. Although domain knowledge could be In a description logic, concepts can be introduced by sim- represented using first order predicate logic, the benefit of us- ply naming them and specifying where they fit in the general- ing a specialised representation is that special-purpose data isation/specialisation hierarchy. The following examples are structures and algorithms can be used to support efficient rea- adapted from Nebel [1990]: soning. In addition, the structured knowledge base supports efficient processing of declarative queries about the defined Anything Human concepts. Set Anything 3 UML for Ontology Modelling where represents concept specialisation and Anything is a predefined concept representing the class of all things. Knowledge representation (KR) systems such as LOOM are New concepts can also be defined in terms of existing con- large and complex systems with a steep learning curve and cepts using the operations of concept conjunction:theand are little known outside AI laboratories. Instead of using operator can be used to specify that the new concept is a com- such technology, the authors are investigating the more main- mon specialisation of a number of other concepts: stream and rapidly growing arena of object-oriented technol- ogy to construct a distributed information retrieval and pro- = and Man Student Male-student cessing system. Currently there is no counterpart for the de- New roles may be introduced to represent possible rela- ductive capabilities of KR systems in current object-oriented tionships that may hold between instances of a concept and technology; however, for distributed information systems other individuals in the world, for example: these capabilities are not necessarily needed. Many of the benefits of KR systems occur during the process of designing anyrelation member an ontology. This support is undoubtedly useful, but in the object-oriented world there is also much support available for where anyrelation represents the class of all relations. the design of models, with mature and commonly used lan- Concepts may be specialised by operations such as value guages, methodologies and tools available. restriction, where the operator all is used to restrict a role’s possible values to be instances of a certain class, and num- The other function of KR systems — to store highly struc- tured data and answer queries about it — is not an issue in atmost ber restriction, where the operators atleast and are used to restrict the possible number of values that a given role distributed information systems. The point of systems such as may have. The following example states that a team is a set SIMS, Infosleuth and Observer is to allow disparate databases for which all values for its “member” role are instances of the and other information sources to be integrated. Nothing can or should be assumed about the underlying databases and in- Human concept with the cardinality of the member role being at least two. formation storage systems. In particular, it cannot be assumed that the information sources will be implemented using KR = and Set all member Human Team systems. While systems such as LOOM can be used to im- 2 member atleast plement key components of a distributed information system Systems such as KL-ONE and LOOM structure their infrastructure (such as query planning agents), it is certainly knowledge bases to allow certain types of inferences to be possible to use other reasoning engines. In the authors’ view, performed efficiently on the user-defined concepts, such as unless a system that uses ontologies is constructed around a the following list paraphrased from Owsnicki-Klewe [1990]: tool such as LOOM, there seems to be nothing inherently in- tuitive or appealing in the use of a description logic formalism Subsumption: Is a given concept description more gen- to represent ontologies. eral or more specific than another, or can no such rela- The ontology representation formalism presented in this tion be established? paper is a subset of the Unified Modeling Language (UML) Coherence: Is a concept description logically coherent, [Rumbaugh et al., 1998] from the Object Management Group i.e. can there be an instance of this term? (OMG) [OMG, 1998], together with its associated Object Constraint Language (OCL) [OMG, 1997b; Warmer and Identity: Do two concept descriptions refer to the same Kleppe, 1998]. Benefits of using
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