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of methods from the area of information extraction on the one hand and from the area of machine learning on the other hand still needs improvement. Here, a deeper understanding of the interplay of these methods with the semantic structures as provided by ontologies is needed. In essence, such an understanding would provide guidelines for a more ne-grained guidance on how to use these automatic methods depending, for example, on the nature of resources available or the usage behaviour of the application users.  Design patterns: Analogous to the development of design patterns in software engineering, the engineering of ontologies has to be improved by the development of pattern libraries that provide ontology engineers with well engineered and application proven ontology patterns that might be used as building blocks. Whereas initial proposals for such patterns exist, a more systematic evaluation of ontology structures and engineering experiences is required to come up with a well-de ned library that meets the needs of the ontology builders.  Design rationales and provenance: With respect to maintaining and reusing ontologies, methodologies have to provide a more comprehensive notion of design rationales and provenance. When thinking of networked scenarios where ontologies are reused in settings that had not been envisioned by the initial ontology developers, providing such kinds of metainformation about the respective ontology is a must. Here, there is a tight dependency with regard to the above-mentioned use of automatic methods, since, for example, provenance information has to be provided along with the generated ontology and metadata elements.  Economic aspects: In commercial settings, one needs well-grounded estimations for the effort one has to invest for building up the required ontologies in order to be able to analyse and justify that investment. Up to now, only very preliminary methods exist to cope with these economic aspects, typically constrained to centralized scenarios. Since good estimations depend on many parameters that have to be set for a concrete application scenario, improvement in this area also heavily depends on collecting experience in real-life projects, comparable to the experience that is the basis for these kind of estimations in the software engineering area. Thus, although the engineering of ontologies is a research area already receiving considerable attention, there still exist a signi cant amount of open issues that have to be solved for really meeting the needs of developers of ontology-based applications.
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Since ontologies encode a view of a given domain that is common to a set of individuals or groups in certain settings for speci c purposes, the
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mechanisms to tailor ontologies to the need of a particular user in his working context are required. The ef cient dealing with a user s context posts several research challenges:  Formal representation of context: Context representation formalisms for ontologies should be compliant with most of the current approaches of contextual modeling from more traditional logical formalisms to modern probabilistic representations. Such formalisms should also support descriptions of temporal contexts in order to deal with context evolution.  Context reasoning: Reasoning processes can be used to, among other things, infer the same conclusions from different ontologies using different contexts, to draw different conclusions from the same ontologies using different contexts, or to adapt an ontology with regard to a context and to deal with such a modi ed ontology. Practical reasoning with contexts should encompass methods for reasoning with logical representations (such as description logic) on one side and probabilistic representations (such as Bayesian networks) on the other side of the spectrum. Special attention should be given to the scalability of the approaches.  Context mapping: Interoperability between different contexts in which an ontology is used can be achieved by the speci cation of mappings that formalize the relationships between contexts. The formal speci cation of such context mappings might support the automatic analysis of these context dependencies, like, for example, consistency. Using terminological correlations, term coreferences, and other linguistic and data analysis methods it might be possible to at least partially automate the creation of mappings between contexts, thus decreasing the required human involvement in the creation and use of contextualized ontologies. A promising application area of contextual information is user pro ling and personalization. Furthermore, with the use of mobile devices and current research on ubiquitous computing, the topic of context awareness is a major issue for future IT applications. Intelligent solutions are needed to exploit context information, for example, to cope with the fuzziness of context information and rapidly changing environments and unsteady information sources. Advanced methodologies for assigning a context to a situation have to be developed, which pave the way to introduce ontology-based mechanisms into context-aware applications.
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