The proposed approach is a first step towards a concrete new concept of knowledge resources repository. The proposed repository is only a proof of concept since it is not scalable for large resources and it does not implement all the aspects that are required for its management (users management, versioning, implement services, implement sophisticated search interfaces, etc.). We did not work on this because it was not included in the research problems and multiple solutions and propositions are available in the state of the art.
For the scalability issue, we assume that there are enough solutions to build this repository based on the proposed model on top of a performant RDF triple store. For the versioning and process execution issues, the model provides all the required elements that make implementing versioning and process monitoring algorithms an engineering matter. Users management and resources sharing issues can be solved by representing users and their resources from a social network perspective, which allows to a user to manage his own resources and share them and edit their visibility and status (this is also an engineering matter and does not require further studies except of using the right model). Other features such as domain management can be implemented easily, since the representation model covers it.
Besides implementations of missing components there are further research problems and research areas to explore. For instance, we used a Semantic Web-based approach in order to be able to apply reasoning over knowledge resources in order to discover mappings between ontological entities. An-other interesting aspect to consider is the possibility to use abduction as a reasoning mechanism for discovering missing facts that prevent the execu-tion of a specific process: What are the missing operators or elements in a possible chain for generating a resource based on existing ones? It means for realizing a process, we should find out automatically what is required to make it possible (e.g. align a SKOS vocabulary with an OWL ontology, it should know that depending on the alignment operator, we need to trans-form the vocabulary into OWL to make the alignment possible). Another perspective is to help a user pick the most relevant knowledge engineering operator for his resources. For example, in case of having multiple matching services that are available (implemented or imported) within the repository, we will be able to automatically pick the right matching tool for a specific resource based on the tool’s and the resource’s criteria [Ajmiet al. 2012].
9.3 Use of the methodology for research and indus-try
The amount of approaches and tools for linked data and semantic web tech-nologies (such as alignment, indexing, cleaning, combining and transforming knowledge resources) is becoming more and more considerable. Organizing all this knowledge about the field of semantic web and knowledge manage-ment will make it easier for scientists from this field to disseminate for their methodologies, share them and compare them with other methodologies or tools in order better reuse or compose them. The number of approaches for these operations evolved during the last decades. Researchers from differ-ent backgrounds design tools before the birth of the Semantic Web concept.
Problems such as web services representation and compositions are already explored and multiple solutions are available. This offers a solid technical background to build such a system. Regarding the number of available tools, a designer of a knowledge management approach considers existing method-ologies for a purpose of reusing them to reduce design and development costs and enhance the quality of his/her solution.
In order to collect the necessary knowledge about available knowledge engineering resources and methodology, a designer is obliged to go through a large amount of scientific papers or industrial documentation. This docu-mentation can have different types going from detailed specifications to only general information. This variety in the description cannot fulfill the infor-mation need of a knowledge engineer. This domain is quite rich and large, so it is time consuming to be aware of all the novelties that may be useful in a particular case to manage an amount of heterogeneous information (data sets) that have to be combined and reused.
This is why we propose to use our approach to build an ontology driven large knowledge repository that allows a knowledge engineer to share and get all the information he needs about the knowledge resources and operators that are relevant for his/her context. Building this ontology of knowledge resources and processes relies on defining their formal representation and classify them based on their evaluation and usage, which is already fulfilled by the current model.
The contributions of this work are of a great use for research and in-dustry; It proposes the basic elements that support a library of tools for knowledge engineering. The proposed ontology offers the possibility to in-tegrate knowledge resources representation. This insures different levels of interoperability and a dynamic representation of knowledge resources. The representation of knowledge resources operators is a support for building an algebra for combining and composing these operators. Some research issues
have been addressed and solved in terms of resources representation and com-bination. In terms of usage for research, our contributions offer the ground for a potential open repository where researchers can share their experiences (tools and processes) and their resources (derived, adapted and validated).
The proposed approach is a candidate for an industrial application. A system can be proposed as a laboratory of knowledge resources combina-tion based on commercial or open-source tools that derive knowledge from existing public or private resources.
About some uses cases of the repository
Contents
A.1 Enriching an ontology with a bilingual glossary . . . 173 A.2 Importing the resources . . . 174
This appendix is about showing some details about the use cases de-scribed in the previous chapters. The first use case is was presented in the section 4.4 about the need of an ontology designer to enrich an ontology in the field of ‘Aeronautics’. She/he wants to add definitions in two languages (English and French) to the named concepts of this ontology.
A.1 Enriching an ontology with a bilingual glossary
The task of enriching a domain ontology there requires external resources that can be found either within the repository or provided by the user (owned resources or extracted from other repositories). For this particular use case, let us consider that the designer has these resources:
• the input ontology (aero.owl) in OWL about ‘Aeronautics and air-crafts’;
• an English glossary (aeroglo_en.rdf) represented in SKOS about the same domain;
• a French glossary (aeroglo_fr.rdfs) represented in RDFS about the same domain;
• a bilingual dictionary (aerodict_en_fr.tbx) represented in TBX about the same domain;
To generate an enriched ontology (concept having labels in two languages and associated to definitions in both languages) these are the steps to follow for fulfilling this task:
prefix tok: <http://cui.unige.ch/isi/onto/tok.owl#>
IMPORT:
aeroOnto_CH = tok:import_CH (aero.owl, ‘OWL’)
aeroGlo_en_CH = tok:import_CH (aeroglo_en.rdf, ‘SKOS’) aeroGlo_fr_CH = tok:import_CH (aeroglo_fr.rdfs, ‘RDFS’) aeroDict_en_fr_CH = tok:import_CH (aerodict_en_fr.tbx, ‘TBX’) ConceptH:
A_en.en_Aero_CH = tok:ConceptH_CH (aeroOnto_CH , aeroGlo_en_CH)
A_en.fr_Aero_CH = tok:ConceptH_CH (aeroOnto_CH , aeroGlo_fr_CH, aeroDict_en_fr_CH) MERGE:
aeroOnto_ext_CH = tok:merge_CH(aeroOnto_CH, aeroGlo_en_CH, A_en.en_Aero_CH ) aeroOnto_ext_CH = tok:merge_CH(aeroOnto_CH, aeroGlo_fr_CH, A_en.fr_Aero_CH ) EXPORT:
aeroext.owl = tok:export_CH(aeroOnto_ext_CH, ‘OWL’)
In the following sections we detail each part of this process and we give excerpts of the algorithms used for the implementation of the used operators.