Self-Explanation through Semantic Annotation: A Survey

AutorJohannes Fähndrich, Sebastian Ahrndt, and Sahin Albayrak
QuelleProc. of the 10th International Symposium Advances in Artificial Intelligence and Applications (AAIA 2015) 
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It is generally agreed, that semantic information is considered the foundation upon which modern approaches attempt to tackle the challenges of dynamic environments. Consider service orchestration or ontology matching as two close examples. Yet, as a matter of fact, many developers frequently desist from adding semantic information to data sets in order to save additional efforts. This problem is called the \"knowledge acquisition bottleneck\" in consequence this survey analyses tools wich are state of the art to broaden this bottleneck. Today, there are many approaches and tools available, each one able to support developers in adding semantic information to critical data. In this work we want to provide an overview on such approaches. In doing so, we put a particular focus on the `level of automation\' that is provided by the analysed approach. It is the purpose of this work to assess tools for creating self-explaining components. We then propose a approach mixing connectionist and symbolic representation of meaning. This merged interpretation is used to create an artificial conceptual representation of meaning.