Populating Ontologies in the eTourism Domain

Ruiz-Martínez, Juana María; Miñarro-Giménez, José Antonio; Guillén-Cárceles, Laura; Castellanos–Nieves, Dagoberto; Valencia‐García, Rafael; García‐Sánchez, Francisco; Fernández-Breis, Jesualdo Tomás; Martı́nez-Béjar, Rodrigo

doi:10.1109/wiiat.2008.278

Cited by 12 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The "Classification of Instances" phase uses the classifier generated in the "Construction of a Classifier" phase to associate instances to their corresponding classes, properties and non-taxonomic relationships. One of the main limitations identified in the analyzed approaches [4] [9] [11] [13] [17] [19] [23] is the dependence on a specific domain. Only one by them performs semi-automatic Ontology Population [17].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Analysing the Problem and Main Approaches for Ontology Population

Faria

Girardi

Nováis

2013

2013 10th International Conference on Information Technology: New Generations

View full text Add to dashboard Cite

Knowledge systems are a suitable computational approach to solve complex problems and to provide decision support. Ontologies are an approach for knowledge representation and Ontology Population looks for instantiating the constituent elements of an ontology, like properties and non-taxonomic relationships. Manual population by domain experts and knowledge engineers is an expensive and time consuming task. Thus, automatic or semi-automatic approaches are needed. This paper discusses the problem of Automatic Ontology Population and proposes a generic process specifying its phases and what kind of techniques can be used to perform the activities of each phase. Some techniques representing the state of the art of this field are also described along with the solutions they adopt for each phase of the AOP process with their advantages and limitations. This work is part of HERMES, a Brazil/Portugal research cooperation project looking for techniques and tools for automating the process of ontology learning and population.

show abstract

Section: Discussionmentioning

confidence: 99%

“…al. [19] approach aims at populating ontology. The "Identification of Candidate Instances" phase applies Named Entity Recognition and Gazetteer Lists NLP techniques on a tourism corpus.…”

Section: Automatic Ontology Population Approachesmentioning

confidence: 99%

Analysing the Problem and Main Approaches for Ontology Population

Faria

Girardi

Nováis

2013

2013 10th International Conference on Information Technology: New Generations

View full text Add to dashboard Cite

show abstract

“…A system for enriching an ontology by populating it with new concepts is also described in [127,126]. The system employs a linguistic learning approach and is composed of 4 sequential phases.…”

Section: Ontology Enrichmentmentioning

confidence: 99%

A Hybrid Concept Learning Approach to Ontology Enrichment

Kastrati

Imran

Yayilgan

2018

Innovations, Developments, and Applications of Semantic Web and Information Systems

View full text Add to dashboard Cite

Declaration of AuthorshipI, Zenun Kastrati, hereby declare that this thesis and the work presented in it is entirely my own. Where I have consulted the work of others, this is always clearly stated.Signed:(Zenun Kastrati) Date: SummaryWe are living in the age of internet where massive amount of information is produced from various digital resources on daily basis. The information of these resources is typically stored in unstructured textual format such as reports, news, e-mails, blogs, etc., therefore, a proper classification and organization of this huge amount of information is apparently needed. In this regard, an automatic classification, particularly ontology-based classification, plays an important role in helping people to classify and organize the information accordingly. The ontology-based classification system is an automatic system that utilizes the ontology in order to take advantages of organizing and classifying the knowledge in a more structural and formal way, thus providing better classification accuracy comparing to the traditional keyword-based classification system.The performance of an ontology-based document classification system can be affected by several aspects involved in the entire classification process that generally is constituted of steps such as document collection and preprocessing, document representation, dimensionality reduction, and the classifier. It is almost impossible to address all these research aspects in order to obtain performance improvement in a single dissertation research work, therefore we selected to work on the aspects that we consider are either rarely studied or have a crucial role on the ontology-based classification system. Document representation is one of the main aspects that affects the performance of ontology-based document classification, thus the first research aspect that we investigated is enriching document representation with semantics utilizing the background knowledge exploited by ontologies. The background knowledge derived from an ontology is embedded in a document using a matching technique. The idea behind this technique is mapping of terms that occur in a document with the relevant ontology concepts by searching only the presence of concepts labels in that document. Searching only the presence of concepts labels occurring in a document limits the capabilities of the classification system to capture and exploit the entire conceptualization involved in that document due to the semantic gap issue, the lack of an in depth-coverage of concepts, and the ambiguity problem. In this thesis, the focus is placed on the conceptual document representation, in which, a document is associated with a set of concepts not only by looking for the appearance of concept labels, but also through the acquisition of lexical information integrated (linked) to the ontology to enriching its coverage with new concepts. In this respect, an automatic ontology concept enrichment model is developed to enrich ontologies with new concepts in order to provide a broader co...

show abstract

“…The goal of Ontology Population is markedly different. Ontology Population has to do with the extraction and classification of instances of the concepts and relations that have been defined in the ontology (Ruiz-Martínez et al, 2008). Instantiating ontologies with new knowledge is a relevant step towards the provision of valuable ontology-based knowledge services.…”

Section: Ontology Populationmentioning

confidence: 99%

Populating Knowledge Based Decision Support Systems

García-Manotas

Lupiani

García‐Sánchez

et al. 2010

International Journal of Decision Support System Technology

View full text Add to dashboard Cite

Knowledge-based decision support systems (KBDSS) hold up business and organizational decision-making activities on the basis of the knowledge available concerning the domain under question. One of the main problems with knowledge bases is that their construction is a time-consuming task. A number of methodologies have been proposed in the context of the Semantic Web to assist in the development of ontology-based knowledge bases. In this paper, we present a technique for populating knowledge bases from semi-structured text which take advantage of the semantic underpinnings provided by ontologies. This technique has been tested and evaluated in the financial domain

show abstract

Populating Ontologies in the eTourism Domain

Cited by 12 publications

References 4 publications

Analysing the Problem and Main Approaches for Ontology Population

Analysing the Problem and Main Approaches for Ontology Population

A Hybrid Concept Learning Approach to Ontology Enrichment

Populating Knowledge Based Decision Support Systems

Contact Info

Product

Resources

About