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The purpose of this paper is to merge the ontologies that remove the redundancy and improve the storage efficiency. The count of ontologies developed in the past few eras is noticeably very high. With the availability of these ontologies, the needed information can be smoothly attained, but the presence of comparably varied ontologies nurtures the dispute of rework and merging of data. The assessment of the existing ontologies exposes the existence of the superfluous information; hence, ontology merging is the only solution. The existing ontology merging methods focus only on highly relevant classes and instances, whereas somewhat relevant classes and instances have been simply dropped. Those somewhat relevant classes and instances may also be useful or relevant to the given domain. In this paper, we propose a new method called hybrid semantic similarity measure (HSSM)-based ontology merging using formal concept analysis (FCA) and semantic similarity measure.

The HSSM categorizes the relevancy into three classes, namely highly relevant, moderate relevant and least relevant classes and instances. To achieve high efficiency in merging, HSSM performs both FCA part and the semantic similarity part.

The experimental results proved that the HSSM produced better results compared with existing algorithms in terms of similarity distance and time. An inconsistency check can also be done for the dissimilar classes and instances within an ontology. The output ontology will have set of highly relevant and moderate classes and instances as well as few least relevant classes and instances that will eventually lead to exhaustive ontology for the particular domain.

In this paper, a HSSM method is proposed and used to merge the academic social network ontologies; this is observed to be an extremely powerful methodology compared with other former studies. This HSSM approach can be applied for various domain ontologies and it may deliver a novel vision to the researchers.

The HSSM is not applied for merging the ontologies in any former studies up to the knowledge of authors.

The purpose of this paper is to introduce a new hybrid method for reducing dimensionality of high dimensional data.

Literature on dimensionality reduction (DR) witnesses the research efforts that combine random projections (RP) and singular value decomposition (SVD) so as to derive the benefit of both of these methods. However, SVD is well known for its computational complexity. Clustering under the notion of concept decomposition is proved to be less computationally complex than SVD and useful for DR. The method proposed in this paper combines RP and fuzzy k‐means clustering (FKM) for reducing dimensionality of the data.

The proposed RP‐FKM is computationally less complex than SVD, RP‐SVD. On the image data, the proposed RP‐FKM has produced less amount of distortion when compared with RP. The proposed RP‐FKM provides better text retrieval results when compared with conventional RP and performs similar to RP‐SVD. For the text retrieval task, superiority of SVD over other DR methods noted here is in good agreement with the analysis reported by Moravec.

The hybrid method proposed in this paper, combining RP and FKM, is new. Experimental results indicate that the proposed method is useful for reducing dimensionality of high‐dimensional data such as images, text, etc.