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.

本文提供了一种从自由文本中学习概念层次的 Formal Concept Analysis 和 WordNet 框架，并通过减少正式上下文来加速思想格框架的推导。

利用形式概念分析和 WordNet 进行本体学习

Ontology Learning Using Formal Concept Analysis and WordNet

We consider the task of inferring is-a relationships from large text corpora.
For this purpose, we propose a new method combining hyperbolic embeddings and
Hearst patterns. This approach allows us to set appropriate constraints for
inferring concept hierarchies from distributional contexts while also being
able to predict missing is-a relationships and to correct wrong extractions.
Moreover -- and in contrast with other methods -- the hierarchical nature of
hyperbolic space allows us to learn highly efficient representations and to
improve the taxonomic consistency of the inferred hierarchies. Experimentally,
we show that our approach achieves state-of-the-art performance on several
commonly-used benchmarks.

本文提出了一种新方法， 结合了双曲嵌入与 Hearst 模式，用于从大型文本语料库中推断概念层次结构和 is-a 关系。实验表明，该方法在多个基准测试中取得了最先进的性能。