Answers.Body: <p><a href="https://en.wikipedia.org/wiki/Ontology_learning" rel="nofollow noreferrer">Ontology learning</a> is a relatively new field that aims to automatically (or semi-automatically) learn or create ontologies (using machine learning, text mining, knowledge representation and reasoning, information retrieval and natural language processing techniques) from some text or corpus.</p>
  
  <p>Ontology learning can be divided into different phases or tasks</p>
  
  <ol>
  <li><p>the acquisition of terms that refer to specific concepts (<a href="https://en.wikipedia.org/wiki/Named-entity_recognition" rel="nofollow noreferrer">named-entity
  recognition</a>)</p></li>
  <li><p>the recognition of synonyms among these terms</p></li>
  <li><p>the identification of taxonomic relations (such as the "is-a" relation)</p></li>
  <li><p>the establishment of non-hierarchical relations</p></li>
  <li><p>the derivation of new knowledge, i.e. knowledge that is not explicitly
  encoded by the ontology.</p></li>
  </ol>
  
  <p>See also <a href="https://pdfs.semanticscholar.org/9613/3e6f5d80c89d3ea31673dc9e467f63fd7366.pdf" rel="nofollow noreferrer">Ontology Learning from Text: An Overview</a> (2003) and <a href="https://academic.oup.com/database/article/doi/10.1093/database/bay101/5116160" rel="nofollow noreferrer">A survey of ontology learning techniques and applications</a> (2018) for more details.</p>
  
  <p>In the paper <a href="http://ceur-ws.org/Vol-1795/paper12.pdf" rel="nofollow noreferrer">Ontology Learning with Deep Learning: a case study on Patient Safety using PubMed</a> (2016), the authors investigate how <a href="https://arxiv.org/pdf/1301.3781.pdf" rel="nofollow noreferrer">continuous bag-of-words (CBOW) and skip-gram</a> (two <a href="https://en.wikipedia.org/wiki/Language_model" rel="nofollow noreferrer">language models</a> based on artificial neural networks) can be used to aid ontology development for patient safety, using <a href="https://www.ncbi.nlm.nih.gov/pubmed/" rel="nofollow noreferrer">PubMed</a> citations as a corpus.</p>
  
  <p><em>Latent Dirichlet allocation (LDA)</em> has also been used for ontology learning, for example, in the paper <a href="https://ksiresearchorg.ipage.com/seke/dms14paper/paper24.pdf" rel="nofollow noreferrer">Terminological ontology learning and population using latent Dirichlet allocation.</a> (2014).</p>
  
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Answers.CreationDate: 2019-09-08T22:43:33.517Z
Answers.LastActivityDate: 2019-09-12T12:03:37.307Z
Answers.LastEditDate: 2019-09-12T12:03:37.307Z
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Body: <p>I'm looking for more or less successful artificial intelligence usage examples to build an ontology or rationale why it can't be done. I found a lot of articles on how to use ontologies for AI, but not succeded vice versa.</p>
  
Tags: <applications><ontology>
Title: Examples of ontologies made with AI
UserDisplayName: AndriuZ
ThreadText: # Examples of ontologies made with AI
  
  (Asked by: username_0 on 2019-09-08)
  
  I'm looking for more or less successful artificial intelligence usage examples to build an ontology or rationale why it can't be done. I found a lot of articles on how to use ontologies for AI, but not succeded vice versa.
  
  **Tags:** applications, ontology
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2019-09-08. Score: 7 | Accepted answer
  
  [Ontology learning](https://en.wikipedia.org/wiki/Ontology_learning) is a relatively new field that aims to automatically (or semi-automatically) learn or create ontologies (using machine learning, text mining, knowledge representation and reasoning, information retrieval and natural language processing techniques) from some text or corpus.
  
  Ontology learning can be divided into different phases or tasks
  
  1. the acquisition of terms that refer to specific concepts ([named-entity
     recognition](https://en.wikipedia.org/wiki/Named-entity_recognition))
  2. the recognition of synonyms among these terms
  3. the identification of taxonomic relations (such as the "is-a" relation)
  4. the establishment of non-hierarchical relations
  5. the derivation of new knowledge, i.e. knowledge that is not explicitly
     encoded by the ontology.
  
  See also [Ontology Learning from Text: An Overview](https://pdfs.semanticscholar.org/9613/3e6f5d80c89d3ea31673dc9e467f63fd7366.pdf) (2003) and [A survey of ontology learning techniques and applications](https://academic.oup.com/database/article/doi/10.1093/database/bay101/5116160) (2018) for more details.
  
  In the paper [Ontology Learning with Deep Learning: a case study on Patient Safety using PubMed](http://ceur-ws.org/Vol-1795/paper12.pdf) (2016), the authors investigate how [continuous bag-of-words (CBOW) and skip-gram](https://arxiv.org/pdf/1301.3781.pdf) (two [language models](https://en.wikipedia.org/wiki/Language_model) based on artificial neural networks) can be used to aid ontology development for patient safety, using [PubMed](https://www.ncbi.nlm.nih.gov/pubmed/) citations as a corpus.
  
  *Latent Dirichlet allocation (LDA)* has also been used for ontology learning, for example, in the paper [Terminological ontology learning and population using latent Dirichlet allocation.](https://ksiresearchorg.ipage.com/seke/dms14paper/paper24.pdf) (2014).
  
  
  ---
  
  