Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star

It is beneficial to automate the process of deriving concept hierarchies from corpora since a manual construction of concept hierarchies is typically a time-consuming and resource-intensive process. As such, the overall process of learning concept hierarchies from corpora encompasses a set of steps: parsing the text into sentences, splitting the sentences and then tokenising it. After the lemmatisation step, the pairs are extracted using formal context analysis (FCA). However, there might be some uninteresting and erroneous pairs in the formal context. Generating formal context may lead to a time-consuming process, so formal context size reduction is require to remove uninterested and erroneous pairs, taking less time to extract the concept lattice and concept hierarchies accordingly. In this premise, this study aims to propose two frameworks: (1) A framework to review the current process of deriving concept hierarchies from corpus utilising formal concept analysis (FCA); (2) A framework to decrease the formal context’s ambiguity of the first framework using an adaptive version of evolutionary clustering algorithm (ECA*). Experiments are conducted by applying 385 sample corpora from Wikipedia on the two frameworks to examine the reducing size of formal context, which leads to yield concept lattice and concept hierarchy. The resulting lattice of formal context is evaluated to the standard one using concept lattice-invariants. Accordingly, the homomorphic between the two lattices preserves the quality of resulting concept hierarchies by 89% in contrast to the basic ones, and the reduced concept lattice inherits the structural relation of the standard one. The adaptive ECA* is examined against its four counterpart baseline algorithms (Fuzzy K-means, JBOS approach, AddIntent algorithm, and FastAddExtent) to measure the execution time on random datasets with different densities (fill ratios). The results show that adaptive ECA* performs concept lattice faster than other mentioned competitive techniques in different fill ratios.

Disambiguating the Context of the Concept Terms using Concept Hierarchies

Latent Semantic Analysis (LSA) makes the machine clearly conceptualize the terms of the document by learning the context in which these terms are written. However, LSA suffers from the limitation of input data matrix size in terms of number of terms and number of documents of the considered dataset. When the size of the dataset is huge, LSA becomes inefficient towards learning the correct context and thereby is unable to produce the intended concepts by the machine. To overcome this problem, Context Disambiguation (ConDis) ontology is engineered for a domain which has the capability of evolving itself based on automatic learning of concepts and relations from the ever scaling documents over the web. The concept hierarchies from general to specific concepts combined with corresponding object relations specify the particular context for a term. These object relations based concept hierarchies clearly help disambiguate the context of the concept terms in an effective manner.