Ontology Learning
I want to go through the Wikipedia series on Machine Learning and Data mining. Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
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Ontology learning (ontology extraction, ontology augmentation generation, ontology generation, or ontology acquisition) is the automatic or semi-automatic creation of ontologies, including extracting the corresponding domain's terms and the relationships between the concepts that these terms represent from a corpus of natural language text, and encoding them with an ontology language for easy retrieval. As building ontologies manually is extremely labor-intensive and time-consuming, there is great motivation to automate the process.
Typically, the process starts by extracting terms and concepts or noun phrases from plain text using linguistic processors such as part-of-speech tagging and phrase chunking. Then statistical or symbolic techniques are used to extract relation signatures, often based on pattern-based or definition-based hypernym extraction techniques.
Ontology learning (OL) is used to (semi-)automatically extract whole ontologies from natural language text. The process is usually split into the following eight tasks:
- Domain technology extraction - domain-specific terms are extracted, which are used in the following step to derive concepts.
- Concept discovery - Terms are grouped to meaning bearing units, which correspond to an abstraction of the world and therefore to concepts
- Concept hierarchy derivation - the OL system tries to arrange the extracted concepts in a taxonomic structure.
- Learning of non-taxonomic relations - relationships are extracted that do not express any sub- or supersumption.
- Rule discovery - axioms (formal descriptions of concepts) are generated for the extracted concepts
- Ontology population - the ontology is augmented with instances of concepts and properties
- Concept hierarchy extension - the OL system tries to extend the taxonomic structure of an existing ontology with further concepts
- Frame and Event Detection - the OL system tries to extract complex relationships from text
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