Analysis On The Ripple-effect Of Ontology Evolution Based On Semantic Relationship

With the rapid development of the Semantic Web,ontology engineering as its foundation has become an increasingly important research area.However,with the change of demand and the continuous deepening of people's understanding of knowledge,domain knowledge needs to be constantly updated and changed.In order to adapt to these changes,domain ontology must be iteratively updated in time,which has led to an important research topic of ontology evolution.Ontology evolution is a complex process that involves a series of technology and management activities to ensure ontology continues to meet user needs in an efficient and effective manner.It is usually necessary to make adjustments to multiple related entities during ontology evolution in order to ensure consistency of ontology.And so that the importance of related entities in ontologies can be used as an important quantitative norm in the evolutionary path.Ontologies are linked into complex network structures because of semantic relationships among entities and the importance of an entity is closely related to the number of other entities it can influence.However,the relationship between the connecting entities are different,and the strength of the semantic relations they contain varies.Therefore,this article,firstly,extracts the common types of semantic relations and ranks them according to their definitions.Secondly,it calculates their weight as the strength of the semantic relationship of the relationship by counting their statistical information such as the frequency of use and the degree of extensiveness in the larger complex ontology.Thirdly,this thesis uses the strength of semantic relationships to calculate the weight of the entity in the ontology.Finally,the concept of ontology evolution cost is introduced and an ontology evolution method based on minimum cost of ontology evolution is proposed.Experimental proofs that the proposed method provides an effective and feasible method for the quantitative quantification of the volatility of ontology evolution.