Semi-automatic Construction of Topic Ontologies

Fortuna, Blaž; Mladenić, Dunja; Grobelnik, Marko

doi:10.1007/11908678_8

Cited by 76 publications

(52 citation statements)

References 4 publications

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“…We encourage interactive model construction and labelling (as in Fortuna et al, 2006), leverage citation and textual information (as in Braam et al, 1991;Chen, 2006), and build on word profiles for characterising the content of document clusters (as in Braam et al, 1991;Chen, 2006;Fortuna et al, 2006;Janssens et al, 2008). In addition, we extend these functionalities by helps for personal productivity (storage) and sharing with others.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Intelligent scientific authoring tools: Interactive data mining for constructive uses of citation networks

Berendt

Krause

Kolbe-Nusser

2010

Information Processing & Management

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Many powerful methods and tools exist for extracting meaning from scientific publications, their texts, and their citation links. However, existing proposals often neglect a fundamental aspect of learning: that understanding and learning require an active and constructive exploration of a domain. In this paper, we describe a new method and a tool that use data mining and interactivity to turn the typical search and retrieve dialogue, in which the user asks questions and a system gives answers, into a dialogue that also involves sense-making, in which the user has to become active by constructing a bibliography and a domain model of the search term(s). This model starts from an automatically generated and annotated clustering solution that is iteratively modified by users. The tool is part of an integrated authoring system covering all phases from search through reading and sense-making to writing. Two evaluation studies demonstrate the usability of this interactive and constructive approach, and they show that clusters and groups represent identifiable sub-topics.

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Section: Motivation and Related Workmentioning

confidence: 99%

Intelligent scientific authoring tools: Interactive data mining for constructive uses of citation networks

Berendt

Krause

Kolbe-Nusser

2010

Information Processing & Management

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“…Concept and concept name suggestions play a central part in every ontology construction system. OntoGen provides unsupervised and supervised methods for generating such suggestions [1,2,6]. Unsupervised learning methods automatically generate a list of sub-concepts for a currently selected concept by using k-means clustering and latent semantic indexing (LSI) techniques to generate a list of possible sub-concepts.…”

Section: Termextractormentioning

confidence: 99%

“…While OntoGen originally used only the input documents for proposing concept suggestions and term extraction techniques for providing help at naming the concepts, it should be noted that the whole process can be significantly improved by constructing a predefined vocabulary from the domain of the ontology under construction. The vocabulary can be used to support the user during 1 Hereafter we name concepts the document clusters generated by the k-means clustering algorithm, while a topic is a description of the concept, e.g. a term of a set of terms that best identify the document cluster.…”

Section: Termextractormentioning

confidence: 99%

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Advancing Topic Ontology Learning through Term Extraction

Fortuna

Lavrač

Velardi

2008

PRICAI 2008: Trends in Artificial Intelligence

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This paper presents a novel methodology for topic ontology learning from text documents. The proposed methodology, named OntoTermExtraction is based on OntoGen, a semi-automated tool for topic ontology construction, upgraded by using and an advanced terminology extraction tool in an iterative, semiautomated ontology construction process. This process consists of (a) document clustering to find the nodes in the topic ontology, (b) term extraction from document clusters, (c) populating the term vocabulary and keyword extraction, and (d) choosing the concept names by comparing the best ranked terms with the extracted keywords. The approach is illustrated on a case study analysis of the ILPNet2 publications data. IntroductionOntoGen [1, 2] is a semi-automated, data-driven ontology construction tool, focused on the construction and editing of topic ontologies. In a topic ontology, each node is a cluster of documents, represented by keywords (topics), and nodes are connected by relations (typically, the the SubConcept-Of relation). The system combines text mining techniques with an efficient user interface aimed to reduce user's time and the complexity of ontology construction. In this way it presents a significant improvement in comparison with present manual, and relatively complex ontology editing tools, such as Protégé [3], whose use is hindered by the lack of ontology engineering skills of domain experts constructing the ontology. Concept naming suggestion (i.e. description of a document cluster through a set of relevant terms) plays a central part of the OntoGen system. Concept naming helps the user at evaluating clusters and organizing them hierarchically. This facility is provided by employing unsupervised and supervised methods for generating the suggestions. Despite the well-elaborated and user-friendly approach to concept naming, as currently provided by OntoGen, the approach was limited to single-word keyword suggestions, and by the use of very basic text lemmatization in the OntoGen text preprocessing phase. This paper aims at improving the ontology construction process through improved concept naming, using terminology extraction as implemented in the advanced TermExtractor tool [4,5]. The improved ontology construction process, proposed in this paper, consists of the following steps:• document clustering to find the nodes in the topic ontology, • terminology extraction from document clusters,

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“…Authors in [6] generate relations between topics by analyzing the HTML structure of Wikipedia documents. Categorization methods are used in [8] where similar topics are discovered by latent semantic indexing (LSI) and K-means clustering. Unsupervised methods serve as guidance in topic ontology building.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Automated Educational Course Metadata Generation Based on Semantics Discovery

Šimko

Bieliková

2009

Lecture Notes in Computer Science

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Abstract. The efficiency of an educational system is related to the ability to deliver personalized content to a student. The current educational systems use advanced mechanisms for adaptation by utilizing available knowledge about the domain area. However, describing a domain area in sufficient detail to allow accurate personalization is a tedious and timeconsuming task. Only few works are related to the support of teachers by discovering the knowledge from educational material. In this paper we present a method for automated metadata generation addressing the educational knowledge discovery problem. We employ several techniques of data mining with regards to the e-learning environment. We evaluate the method on the functional programming course.

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Semi-automatic Construction of Topic Ontologies

Cited by 76 publications

References 4 publications

Intelligent scientific authoring tools: Interactive data mining for constructive uses of citation networks

Intelligent scientific authoring tools: Interactive data mining for constructive uses of citation networks

Advancing Topic Ontology Learning through Term Extraction

Automated Educational Course Metadata Generation Based on Semantics Discovery

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