Résultat de la recherche
FCA-Based Ontology Learning From Unstructured Textual Data
Ontologies have been frequently used for representing a domain knowledge. It has a lot of applications in semantic knowledge extraction. However, learning ontologies especially from unstructured data is a difficult yet an interesting challenge. In this paper, we introduce a pipeline for learning ontology from a text corpora in a semi-automated fashion using Natural Language Processing (NLP) and Formal Concept Analysis (FCA). We apply our proposed method on a small given corpus that consists of some news documents in IT and pharmaceutical domain. We then discuss the potential applications of the proposed model and ideas on how to improve it even further.
A Methodology for Extracting Knowledge about Controlled Vocabularies from Textual Data using FCA-Based Ontology Engineering
We introduce an end-to-end methodology (from text processing to querying a knowledge graph) for the sake of knowledge extraction from text corpora with a focus on a list of vocabularies of interest. We propose a pipeline that incorporates Natural Language Processing (NLP), Formal Concept Analysis (FCA), and Ontology Engineering techniques to build an ontology from textual data. We then extract the knowledge about controlled vocabularies by querying that knowledge graph, i.e., the engineered ontology. We demonstrate the significance of the proposed methodology by using it for knowledge extraction from a text corpus that consists of 800 news articles and reports about companies and products in the IT and pharmaceutical domain, where the focus is on a given list of 250 controlled vocabularies.
Ontology extraction from MongoDB using formal concept analysis
Using formal concept analysis, we propose a method for engineering ontology from MongoDB to effectively represent unstructured data. Our method consists of three main phases: (1) generating formal context from a MongoDB, (2) applying formal concept analysis to derive a concept lattice from that formal context, and (3) converting the obtained concept lattice to the first prototype of an ontology. We apply our method on NorthWind database and demonstrate how the proposed mapping rules can be used for learning an ontology from such database. At the end, we discuss about suggestions by which we can improve and generalize the method for more complex database examples.