OLAF
Ontology based-system
Ontology Use Cases Final Application
Knowledge sources
Text Corpus Seed Ontology
Ontology
Pipeline Building Pipeline Optimisation Pipeline Execution
C-value-based filtering
Linguistic-based filtering
TF-IDF value-based filtering
ConceptNet-based extraction
Grouping terms based on synonyms
Term cooccurrences-based extraction
Similarity-based extraction
Formal concept Analysis
Term subsumption algorithm
Hierarchical clustering
Rule-based axiom extraction
Inductive Logic Programming
OLAF : Ontology Learning Applied Framework
Marion SCHAEFFER (marion.schaeffer@insa-rouen.fr) - Matthias SESBOUE (matthias.sesboue@insa-rouen.fr)
Jean-Philippe KOTOWICZ - Nicolas DELESTRE - Cecilia ZANNI-MERK
Term Extraction
Term Enrichment
Concept/RelationExtraction
Hierarchisation
Axiom
Data preprocessing
Text Corpus
Ontology
OLAF
Activity Ressource
Artifact Optional
CAPTION
STATE OF THE ART
System Overview Pros and cons
Text2Onto,
2005, [1]
It is the reference in the field as it defines a
representation-agnostic structure with modular
steps and takes into account uncertainty. The
system is implemented as a GATE module.
Ontologies can be exported in
various formats. GATE system
adds great visualisations. But it is
not maintained since 2011.
OntoLearn
(Reloaded),
2013, [3]
It focuses on "lexicalised ontologies" and uses seed
knowledge. It implements 5 steps: terminology
extraction, hypernym graph construction, domain
filtering of hypernyms, hypernym graph pruning and
edge recovery.
It relies on WordNet and POS
tags and does not distinguish
between terms and concepts.
It implements different
adaptable approaches.
OntoGain,
2010, [2]
It focuses on multiword terms to construct a
"lexicalised ontology" by adapting an agglomerative
clustering and an FCA method. It implements 4
steps: text preprocessing, concept extraction (C/NC-
value), taxonomy construction, and non-taxonomic
relation acquisition (rule-based and probabilistic).
It considers only multiword
terms and relies on WordNet
and POS tags. It does not
distinguish between terms and
concepts and implements
different adaptable approaches.
Cimiano P, Völker J. Text2Onto. Natural Language Processing and Information Systems. Berlin, Heidelberg: Springer Berlin Heidelberg; 2005.p. 227-238. ISBN: 978-3-540-32110-1
Drymonas E, Zervanou K, Petrakis EGM. Unsupervised Ontology Acquisition from Plain Texts: The OntoGain System. Natural Language Processing and Information Systems. Berlin, Heidelberg:
Springer Berlin Heidelberg; 2010. p. 277-87. ISBN: 978-3-642-13881-2
Paola Velardi, Stefano Faralli, Roberto Navigli; OntoLearn Reloaded: A Graph-Based Algorithm for Taxonomy Induction. Computational Linguistics 2013; 39 (3): 665–707. DOI:
10.1162/COLI_a_00146
Muhammad Nabeel Asim, Muhammad Wasim, Muhammad Usman Ghani Khan, Waqar Mahmood, Hafiza Mahnoor Abbasi, A survey of ontology learning techniques and applications,
Database, Volume 2018, 2018, bay101, DOI: 10.1093/database/bay101
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Since the beginning of the century, research on ontology learning has gained popularity. Automatically extracting and structuring knowledge
relevant to a domain of interest from unstructured data is a major scientific challenge. We propose a new approach with a modular ontology
learning framework considering tasks from data pre-processing to axiom extraction. Whereas previous contributions considered ontology learning
systems as tools to help the domain expert, we developed the proposed framework with full automation in mind. An implementation as an open-
source and collaborative python library is available at https://gitlab.insa-rouen.fr/msesboue/ontology-learning.
Most ontology learning systems do not consider the targeted ontology-
based system. Though an ideal ontology should model a domain in an
application-independent manner, in practice, concepts and relations
represented largely depend on one or more business use cases. As
we designed our framework with industry application in mind, we need