Abstract. Morphological analysis is used to study the internal structure words by reducing the number of vocabularies used while retaining the semantic meaning of the knowledge in NLP system. Most of the existing algorithms are focusing on stemmatization instead of lemmatization process. Even with technology advancement, yet none of the available lemmatization algorithms able to produce 100 % accurate result. The base words produced by the current algorithm might be unusable as it alters the overall meaning it tried to represent, which will directly affect the outcome of NLP systems. This paper proposed a new method to handle lemmatization process during the morphological analysis. The method consists three layers of lemmatization process, which incorporate the used of Stanford parser API, WordNet database and adaptive learning technique. The lemmatized words yields from the proposed method are more accurate, thus it will improve the semantic knowledge represented and stored in the knowledge base.

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World Wide Web provides vast of resources to the public. Currently, many researches have been done on resources sharing among users through implementation of ontologies. Knowledge in an ontology are represented in the form of triple(s-p-o), where concepts are brought together by a relation. In a situation where there is a need to represent a resource which exist without IRI, blank node can be implemented in placed of the resource. Increase number of blank nodes implemented will increase the complexity of ontology structure. Since it is impossible to avoid blank nodes implementation in the ontology, increase used of it might lead to the intractable of data during the information retrieval.This paper presents a new clause-based structure that able to handle N-ary, container, collection and reified knowledge issues brought by the blank node application. The result shows that the structure able to store complicated knowledge without the need to implement blank node.

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