Ontology Learning Using Formal Concept Analysis and WordNet
Bryar A. Hassan
Manual ontology construction takes time, resources, and domain specialists.
Supporting a component of this process for automation or semi-automation would
be good. This project and dissertation provide a Formal Concept Analysis and
WordNet framework for learning concept hierarchies from free texts. The process
has steps. First, the document is Part-Of-Speech labeled, then parsed to
produce sentence parse trees. Verb/noun dependencies are derived from parse
trees next. After lemmatizing, pruning, and filtering the word pairings, the
formal context is created. The formal context may contain some erroneous and
uninteresting pairs because the parser output may be erroneous, not all derived
pairs are interesting, and it may be large due to constructing it from a large
free text corpus. Deriving lattice from the formal context may take longer,
depending on the size and complexity of the data. Thus, decreasing formal
context may eliminate erroneous and uninteresting pairs and speed up idea
lattice derivation. WordNet-based and Frequency-based approaches are tested.
Finally, we compute formal idea lattice and create a classical concept
hierarchy. The reduced concept lattice is compared to the original to evaluate
the outcomes. Despite several system constraints and component discrepancies
that may prevent logical conclusion, the following data imply idea hierarchies
in this project and dissertation are promising. First, the reduced idea lattice
and original concept have commonalities. Second, alternative language or
statistical methods can reduce formal context size. Finally, WordNet-based and
Frequency-based approaches reduce formal context differently, and the order of
applying them is examined to reduce context efficiently.