W. Grosso, H. Eriksson, R. Fergerson, J. Gennari, S. Tu, and M. Musen. Knowledge modeling at the millennium -- the design and evolution of prot eg e-2000.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....RDF. To maintain a portal and keep it alive its content needs to be updated frequently not only by information integration of different sources but also by additional inputs from human experts. The input view is defined by queries to the schema, i.e. queries to the ontology itself. Similar to [7] we support the knowledge acquisition task by generating forms out of the ontology. The forms capture data according to the ontology in a consistent way which are stored afterwards in the warehouse. To navigate and browse the warehouse we automatically generate navigational structures, i.e. ....

E. Grosso, H. Eriksson, R. W. Fergerson, S. W. Tu, and M. M. Musen. Knowledge modeling at the millennium: the design and evolution of PROTEGE-2000.

.... z[fatherInLawOf x:Man] AND x[marriedWith y] 4 Related Work The proposal described in this paper is based on several related approaches, viz. we have build on considerations made for the RDF inference service SiLRi [4] the ontology engineering environments ODE [1] and Protege [5], the ontology interchange language OIL [7] and our own earlier work on general ontology engineering [12, 14] SiLRi [4] was one of the first approaches to propose inferencing facilities for RDF. It provides most of the basic inferencing functions one wants to have in RDF and, hence, has ....

....symbol level onto the knowledge level following and extending the general arguments made for ODE [1] This strategy has helped us here in providing an RDF(S) object representation for a number of different axiom types. Nearest to our actual RDF(S) based ontology engineering tool is Protege [5], which provides comprehensive support for editing RDFS and RDF. Nevertheless, Protege currently lacks any support for axiom modeling and inferencing though our approach may be very easy to transfer to Protege, too. A purpose similar to our general goal of representing ontologies in RDF(S) is ....

E. Grosso, H. Eriksson, R. W. Fergerson, S. W. Tu, and M. M. Musen. Knowledge modeling at the millennium --- the design and evolution of Protege-2000. In Proc. of the 12th International Workshop on Knowledge Acquisition, Modeling and Mangement (KAW'99), Banff, Canada, October 1999, 1999. 14

....RDF. To maintain a portal and keep it alive its content needs to be updated frequently not only by information integration of different sources but also by additional inputs from human experts. The input view is dened by queries to the schema, i.e. queries to the ontology itself. Similar to [6] we support the knowledge acquisition task by generating forms out of the ontology. The forms capture data according to the ontology in a consistent way which are stored afterwards in the warehouse. To navigate and browse the warehouse we automatically generate navigational structures, i.e. ....

E. Grosso, H. Eriksson, R. W. Fergerson, S. W. Tu, and M. M. Musen. Knowledge modeling at the millennium: the design and evolution of PROTEGE-2000.

....satisfaction [33] skeletal planning [34] and Bayesian classification. Some methodologies for building intelligent systems, such as Common KADS [35] emphasize the use of abstract descriptions of problem solving methods primarily for conceptual modeling [36] other approaches, such as Protg [37, 38] and EXPECT [39] actually provide libraries of implemented problem solving methods that can contribute program code to a system under development. Problem solving methods provide an enormous degree of procedural abstraction, allowing developers to treat complex algorithms as black boxes. ....

....of concepts, we can view a knowledge base as an instantiation (or an extension) of an ontology. Thus, a knowledge base comprises filled in concept descriptions, enumerating the details of the particular application being built. Given a domain ontology, knowledge acquisition systems such as Protg [37, 38] allow straightforward entry of the corresponding knowledge base. The Protg system permits developers to create a domain ontology using a simple editing system. Protg then uses the domain ontology to create programmatically a user interface through which subject matter experts can enter the ....

W.E. Grosso, et al., Knowledge Modeling at the Millennium: The Design and Evolution of Protg- 2000.

....RDF. To maintain a portal and keep it alive its content needs to be updated frequently not only by information integration of different sources but also by additional inputs from human experts. The input view is defined by queries to the schema, i.e. queries to the ontology itself. Similar to [6] we support the knowledge acquisition task by generating forms out of the ontology. The forms capture data according to the ontology in a consistent way which are stored afterwards in the warehouse. To navigate and browse the warehouse we automatically generate navigational structures, i.e. ....

E. Grosso, H. Eriksson, R. W. Fergerson, S. W. Tu, and M. M. Musen. Knowledge modeling at the millennium: the design and evolution of PROTEGE-2000. In Proceedings of the 12th International Workshop on Knowledge Acquisition, Modeling and Mangement (KAW99) , Banff, Canada, October 1999.

....expressing frequently used entities. These are described in detail in [9] or [16] Knowledge bases or ontologies conforming to OKBC are often expressed in KIF [18] or RDF [28] An example of OKBC compliant editor of knowledge bases or ontologies that supports both of these formats is Protege 2000 [19]. FIPA specification [16] defines ontology FIPA meta ontology based on OKBC Knowledge Model to describe ontologies. This ontology must be used by an agent when it talks about ontologies. Ontology FIPA Ontol service Ontology must be used when requesting services of an ontology agent. This ontology ....

W. E. Grosso, H. Eriksson, R. W. Fergerson, J. H. Gennari, S. W. Tu, and M. A. Musen. Knowledge Modeling at the Millennium --- The Design and Evolution of Protege

....view. To maintain a portal and keep it alive its content needs to be updated frequently not only by information integration of different sources but also by additional inputs from human experts. The input view is defined by queries to the schema, i.e. queries to the ontology itself. Similar to [14] we support the knowledge acquisition task by generating forms out of the ontology. The forms capture data according to the ontology in a consistent way which are stored afterwards in the warehouse (cf. Figure 3) Navigation view. To navigate and browse the warehouse we automatically generate ....

E. Grosso, H. Eriksson, R. W. Fergerson, S. W. Tu, and M. M. Musen. Knowledge modeling at the millennium: the design and evolution of PROTEGE-

....document integration, there exist a number of related projects and tools able to help in solving these problems. In the rest of this section we survey several ultimately relevant approaches, which may be used in the B2B document integration tasks. 6. 1 Protg 2000 Ontology Editor Protg 2000 16 [Grosso et al., 1999] is an integrated knowledge base editing environment and an extensible architecture for the creation of customized knowledge based tools. The tool supports the OKBC knowledge model and maintains ontologies consisting of classes, slots, facets, and class instances. The tool has a decade long usage ....

Grosso, W., Eriksson, H., Fergerson, R., Gennari, J., Tu, S., and Musen, M., "Knowledge modeling at the millennium (the design and evolution of Protege-2000)", In: Proceedings of the Twelfth Banff Workshop on Knowledge Acquisition, Modeling, and Management, Voyager Inn, Banff, Alberta, Canada, October 16-21, 1999.

....allowed between classes in the formalism 7 . The notion of relation between classes is simply expressed through the slots of type Instance, so 7 At least in the version of Protg we have used for our example: Protg Win. This seems to be changed in a latest version of Protg, Protg 2000 [Grosso, Eriksson et al. 1999], that we have not yet explored in detail. 5 this is definitely not a first class construct of the formalism: it is supported by a simple pointer. In addition, classes and slots in Protg ontologies can all be documented through a specific meta feature (see e.g. the top of the documentation ....

W.E. Grosso, H. Eriksson, R.W. Fergerson, J.H. Gennari, S.W. Tu, et al. "Knowledge Modeling at the Millennium (The Design and Evolution of Protege-2000)". Internal report. SMI-1999-0801. Stanford Medical Informatics. 1999. http://smiweb. stanford.edu/pubs/SMI_Abstracts/SMI-1999-0801.html.

....changes in content and format of the accessed documents is gained. 1.2 Semi automatic vs. manual ontology construction The required domain specific ontologies for On ToKnowledge are built manually. Ontologies are usually built using tools like OntoEdit (Staab and Maedche, 2000) or Protege (Grosso et al. 1999). Using such tools has simplified ontology construction. However, the wide spread usage of ontologies is still hindered by the time consuming and expensivemanual construction task. Within On To Knowledge our work evaluates semi automatic ontology construction from texts as an alternative ....

E. Grosso, H. Eriksson, R. W. Fergerson, S. W. Tu, and M. M. Musen. 1999. Knowledge modeling at the millennium --- the design and evolution of Prot'eg'e-2000. In Proc. the 12th International Workshop on Knowledge Acquisition, Modeling and Mangement (KAW'99), Banff, Canada, October 1999.

.... in ontology modeling environments is restricted to what subsumption offers in a description logics framework (McGuinness Patel Schneider, 1998) or to what the ontology engineer encodes in some kind of first order logic language (Blazquez et al. 1998) or axiom modeling is neglected at all (e.g. (Grosso et al. 1999)) This situation is detrimental to the modeling of large scale ontologies, because it aggravates engineering and maintainance of large sets of axioms. Another drawback, along similar lines, arises from the fact that the ontology engineer obliges to a particular symbol representation of axioms ....

....c # 2000 S. Staab and A. Maedche ECAI 2000. 14th European Conference on Artificial Intelligence Workshop on Applications of Ontologies and Problem Solving Methods R.V. Benjamins, A. Gomez Perez, N. Guarino, M. Uschold (eds. onto concepts and relations similar to the ones available in Protege (Grosso et al. 1999). While one may conceive of some minor extensions of this approach, e.g. to account for finer distinctions like the ones between SUBCONCEPTOF and PROPERSUBCONCEPTOF ( # vs. # in description logics) our overall approach for modeling concepts and relations is consistent with all of the above ....

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Grosso, E.; Eriksson, H.; Fergerson, R. W.; Tu, S. W.; and Musen, M. M. 1999. Knowledge modeling at the millennium --- the design and evolution of Protege-2000. In Proc. the 12th International Workshop on Knowledge Acquisition, Modeling and Mangement (KAW'99), Banff, Canada, October 1999.

....to the ontology itself as well as to concepts, relations and axioms. This is reflected in Section 3. 2. 1 Concepts and Relations Considering the level of concepts and relations, we provide very much the same object oriented model that is well known from tools like ODE [BFG 1998] and Proteg [GEF 1999] (cf. BDS 1999] for a survey) Multiple taxonomies of concepts, i.e. subconceptOf relations with multiple inheritance, provide the backbone of our approach. Relations are considered as firstorder entities that come with several properties of their own, e.g. names. Concepts are linked by relations ....

W. E. Grosso, H. Eriksson, R. W. Fergerson, J. H. Gennari, S. W. Tu, M. M. Musen. Knowledge Modeling at the Millennium -- The Design and Evolution of Protege-2000. Proceedings of the 12 th International Workshop on Knowledge Acquisition, Modeling and Mangement (KAW'99), Banff, Canada, October 1999.

....including the example can be downloaded from the Prot eg e 2000 website. 2 Background 2. 1 Prot eg e 2000 Prot eg e 2000 is the latest incarnation of the series of tools developed for many years by researchers at SMI to provide efficient support in knowledge modeling and knowledge acquisition [7]. Prot eg e 2000 is platform independent and offers a component based architecture, which is extensible through its API. Prot eg e was recently adapted to support the creation and editing of RDF Schema ontologies and the acquisition of RDF instance data (see [5] for more details) In the rest of ....

W. E. Grosso, H. Eriksson, R. W. Fergerson, J. H. Gennari, S. W. Tu, and M. A. Musen. Knowledge modeling at the millennium: The design and evolution of Prot eg e-2000.

....group has been working on both a series of knowledge acquisition workbenches and an associated methodology for building intelligent computer based systems. Each of these workbenches has been called Protg, although the capabilities of each system have increased from one generation to the next [1]. Each new incarnation of the Protg approach has explored the consequences of relaxing additional knowledge acquisition constraints, while attempting to make more declarative and more explicit the components from which developers can build knowledge based systems. The first incarnation of the ....

Grosso, W.E., Eriksson, H., Fergerson, R.W., Gennari, J.H., Tu, S.W., and Musen, M.A. Knowledge modeling at the millennium: The design and evolution of Protg-2000.

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W. Grosso, H. Eriksson, R. Fergerson, J. Gennari, S. Tu, and M. Musen. Knowledge modeling at the millennium -- the design and evolution of prot eg e-2000.

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W. E. Grosso, H. Eriksson, R. W. Fergerson, J. H. Gennari, S. W. Tu, and M. A. Musen. Knowledge modeling at the millennium: The design and evolution of Protege-2000. In 12th Ban# Workshop on Knowledge Acquisition, Modeling, and Management. Ban#, Alberta, 1999. URL: http://smi.stanford.edu/projects/protege (access date: 18 December 2000).

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W. E. Grosso, H. Eriksson, R. W. Fergerson, J. H. Gennari, S. W. Tu, and M. A. Musen. Knowledge modeling at the millennium: The design and evolution of Protege-2000. In 12th Ban# Workshop on Knowledge Acquisition, Modeling, and Management. Ban#, Alberta, 1999. URL: http://smi.stanford.edu/projects/protege (access date: 18 December 2000).

No context found.

W. Grosso, H. Eriksson, R. Fergerson, J. Gennari, S. Tu, and M. Musen. Knowledge Modeling at the Millennium -- The Design and Evolution of Protege. In Proceedings of the 12th International Workshop on Knowledge Acquisition, Modeling and Mangement (KAW'99), 1999.

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Grosso, W.E., Eriksson, H., Fergerson, R.W., Gennari, J.H., Tu, S.W., Musen, M.A.: Knowledge modeling at the millennium: The design and evolution of Prot eg e-2000. In: 12th Banff Workshop on Knowledge Acquisition, Modeling, and Management. Banff, Alberta. (1999)

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