Challenges to Machine Learning: Relations Between Reality and Appearance

An overview of methods and tools for ontology learning from texts

The Knowledge Engineering Review ◽

10.1017/s0269888905000251 ◽

2004 ◽

Vol 19 (3) ◽

pp. 187-212 ◽

Cited By ~ 42

Author(s):

ASUNCIÓN GÓMEZ-PÉREZ ◽

DAVID MANZANO-MACHO

Keyword(s):

Machine Learning ◽

Development Process ◽

Ontology Development ◽

Ontology Learning ◽

Sources Of Information ◽

New Concepts ◽

Learning From Texts ◽

Speed Up ◽

Different Sources ◽

Learning Relations

Ontology learning aims at reducing the time and efforts in the ontology development process. In recent years, several methods and tools have been proposed to speed up this process using different sources of information and different techniques. In this paper, we have reviewed 13 methods and 14 tools for semi-automatically building ontologies from texts and their relationships with the techniques each method follows. The methods have been grouped according to the main techniques followed and three groups have been identified: one based on linguistics, one on statistics, and one on machine learning. Regarding the tools, the criterion for grouping them, which has been the main aim of the tool, is to distinguish what elements of the ontology can be learned with each tool. According to this, we have identified three kinds of tools: tools for learning relations, tools for learning new concepts, and assisting tools for building up taxonomies.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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