Model-based documentation

Knowledge acquisition is becoming an integral part of the manufacturing industries, which rely on domain experts in various phases of product life cycle including design, analysis, manufacturing, operation and maintenance. It has the potential to enable knowledge reuse, however, poorly managed knowledge can cause information loss and inefficiency. If technical documentation is managed well in the manufacturing industries, intended piece of knowledge can easily be located, used and reused for purpose and as a result, the corresponding industry can be benefited. Some examples of technical documentation are design specification, operating manual and maintenance manual. Model-based Documentation (MBD) is a documentation approach that uses model to provide structure to the data of the documents. MBD can be thought of as a way to better organize knowledge thereby knowledge identification and retrieval become easier, faster and efficient. In this paper, we propose MBD and its extension as a potential solution to overcome the issues involved in the typical technical documentation approaches

WordNet Powered Faceted Semantic Search With Automatic Sense Disambiguation For Bioenergy Domain

WordNet is a lexicon widely known and used as an ontological resource hosting comparatively large collection of semantically interconnected words. Use of such resources produces meaningful results and improves users’ search experience through the increased precision and recall. This paper presents our facet-enabled WordNet powered semantic search work done in the context of the bioenergy domain. The main hurdle to achieving the expected result was sense disambiguation further complicated by the occasional fine-grained distinction of meanings of the terms in WordNet. To overcome this issue, a novel sense disambiguation approach based on automatically built domain specific ontologies, WordNet synset hierarchy and term (or word) sense ranks is proposed