SVETLAN’ Or How to Classify Words Using Their Context

Chalendar, Gaël de; Grau, Brigitte

doi:10.1007/3-540-39967-4_15

Cited by 8 publications

(8 citation statements)

References 8 publications

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“…(Chalendar & Grau, 2000) Supporting tool to build an ontology by learning noun hierarchies, receiving semantic domains with thematic units, building structured domains to classify nouns according to same relations to same verbs. (Hahn & Schnattinger, 1998;Hahn & Romacker, 2001;Hahn & Marko, 2002) Incremental learning of words, concepts and relations, based on text understanding on both sentence level and text level, using the linguistic and conceptual "quality" of various forms of evidence underlying the generation and refinement of concept hypotheses.…”

Section: Doddle IImentioning

confidence: 99%

“…HASTI (Shamsfard, 2003;Shamsfard & Barforoush, 2000;2002a; Learning words, concepts, relations and axioms in both incremental and non-incremental modes, starting from a small kernel (learning from scratch), using a hybrid symbolic approach, a combination of logical, linguistic-based, templatedriven and heuristic methods. (Chalendar & Grau, 2000) Supporting tool to build an ontology by learning noun hierarchies, receiving semantic domains with thematic units, building structured domains to classify nouns according to same relations to same verbs. (Hahn & Schnattinger, 1998;Hahn & Romacker, 2001;Hahn & Marko, 2002) Incremental learning of words, concepts and relations, based on text understanding on both sentence level and text level, using the linguistic and conceptual "quality" of various forms of evidence underlying the generation and refinement of concept hypotheses.…”

Section: Doddle IImentioning

confidence: 99%

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The state of the art in ontology learning: a framework for comparison

Shamsfard

Barforoush

2003

The Knowledge Engineering Review

132

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In recent years there have been some efforts to automate the ontology acquisition and construction process. The proposed systems differ from each other in some factors and have many features in common. This paper presents the state of the art in Ontology Learning (OL) and introduces a framework for classifying and comparing OL systems. The dimensions of the framework concern what to learn, from where to learn it and how it may be learnt. They include features of the input, the methods of learning and knowledge acquisition, the elements learned, the resulting ontology and also the evaluation process. To extract this framework, over 50 OL systems or modules thereof that have been described in recent articles are studied here and seven prominent ones, which illustrate the greatest differences, are selected for analysis according to our framework. In this paper after a brief description of the seven selected systems we describe the dimensions of the framework. Then we place the representative ontology learning systems into our framework. Finally, we describe the differences, strengths and weaknesses of various values for our dimensions in order to present a guideline for researchers to choose the appropriate features to create or use an OL system for their own domain or application.

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Section: Doddle IImentioning

confidence: 99%

Section: Doddle IImentioning

confidence: 99%

The state of the art in ontology learning: a framework for comparison

Shamsfard

Barforoush

2003

The Knowledge Engineering Review

132

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“…a scientific application may need short, axiomatized ontology to solve its problems) and (iii) degree of automation. In their survey, seven prominent ontology learning systems namely ASIUM ( 7 ), Doodle II ( 8 ), Hasti ( 9 ), Svetlan ( 10 ), Syndikate ( 11 ), Text-to-Onto ( 12 ) and WebKB ( 13 ) were analyzed. Critical analysis of this survey leads to following conclusions: (i) this survey highlighted research on extraction of taxonomic relations but did not explore non-taxonomic relations extraction, (ii) most of the explored ontology learning systems needed prior domain knowledge in form of base ontology to extract ontologies from unstructured text and (iii) the authors did not mention any automatic ontology learning system.…”

Section: Summary Of Previous Surveysmentioning

confidence: 99%

A survey of ontology learning techniques and applications

et al. 2018

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Ontologies have gained a lot of popularity and recognition in the semantic web because of their extensive use in Internet-based applications. Ontologies are often considered a fine source of semantics and interoperability in all artificially smart systems. Exponential increase in unstructured data on the web has made automated acquisition of ontology from unstructured text a most prominent research area. Several methodologies exploiting numerous techniques of various fields (machine learning, text mining, knowledge representation and reasoning, information retrieval and natural language processing) are being proposed to bring some level of automation in the process of ontology acquisition from unstructured text. This paper describes the process of ontology learning and further classification of ontology learning techniques into three classes (linguistics, statistical and logical) and discusses many algorithms under each category. This paper also explores ontology evaluation techniques by highlighting their pros and cons. Moreover, it describes the scope and use of ontology learning in several industries. Finally, the paper discusses challenges of ontology learning along with their corresponding future directions.

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“…Efforts in ontology enrichment mainly focus on associating concepts with an already known relation [14,15,16,17] or adding a new concept along with a new discovered relation [18,19,20,21,22] that associates the new concept with an already known concept of the initial ontology. These efforts extract knowledge from textual corpora.…”

Section: Related Workmentioning

confidence: 99%