Using Term-Matching Algorithms for the Annotation of Geo-services

Grčar, Miha; Klien, Eva; Novak, Blaž

doi:10.1007/978-3-642-01891-6_8

Cited by 16 publications

(11 citation statements)

References 3 publications

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“…• The second technique did not use the ILPnet2 collection and relied on Google web search instead [9]. A query was generated from each term t by taking its words and attaching an extra keyword "ILP" to limit the search to ILP related web pages.…”

Section: Populating the Terms And Keyword Extractionmentioning

confidence: 99%

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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Section: Populating the Terms And Keyword Extractionmentioning

confidence: 99%

Advancing Topic Ontology Learning through Term Extraction

Fortuna

Lavrač

Velardi

2008

PRICAI 2008: Trends in Artificial Intelligence

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“…In the geographical information representation, an ontologies-based approach that is structured to allow feature and event attributes to represent meaning in class rules and relationships is required (Grossner, Goodchild, and Clarke 2008). In this sense, there is experience related to the capture of ontologies operational at the time of data production (Comber, Fisher, and Wadsworth 2005).…”

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confidence: 99%

“…Grcar and Klien define semantic annotation as "the task of matching an arbitrary word or term with the most appropriate concept in the corresponding domain ontology" (Grcar and Klien 2007). Klien and Lutz (2005) proposed a method to automate the semantic annotation process based on spatial relations, using the specific characteristics of geographical information.…”

mentioning

confidence: 99%

“…Klien and Lutz (2005) proposed a method to automate the semantic annotation process based on spatial relations, using the specific characteristics of geographical information. Automating this process facilitates the transition to managed semantic representations (Grcar and Klien 2007). Klien (2007) presented a rule-based strategy for the semantic annotation of geographical data that combines Semantic Web and Geospatial Web Services technology.…”

mentioning

confidence: 99%

“…Klien and Lutz (2005) proposed an automatic method of semantically annotating geographic data based on spatial relationships, taking advantage of the specific characteristics of geographic information. Automating this process facilitates the transition towards semantically managed representations (Grcar and Klien 2007). The Data-Representation Ontology (DRO) proposed by Larin Fonseca and Garea Llano (2011) is a special type of ontology to describe the semantics embedded in geographic data.…”

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confidence: 99%

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Ontology-based topological representation of remote-sensing images

Oliva-Santos

Pérez

Garea-Llano

2013

International Journal of Remote Sensing

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Discovering Types of Spatial Relations with a Text Mining Approach

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Roche

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Lecture Notes in Computer Science

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International audienceKnowledge discovery from texts, particularly the identification of spatial information is a difficult task due to the complexity of texts written in natural language. Here we propose a method combining two statistical approaches (lexical and contextual analysis) and a text mining approach to automatically identify types of spatial relations. Experiments conducted on an English corpus are presented

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Using Term-Matching Algorithms for the Annotation of Geo-services

Cited by 16 publications

References 3 publications

Advancing Topic Ontology Learning through Term Extraction

Advancing Topic Ontology Learning through Term Extraction

Ontology-based topological representation of remote-sensing images

Discovering Types of Spatial Relations with a Text Mining Approach

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