Document Space Adapted Ontology: Application in Query Enrichment

Tomassen, Stein L.; Gulla, Jon Atle; Strasunskas, Darijus

doi:10.1007/11765448_5

Cited by 14 publications

(8 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3 are implemented and individually tested. These components are all related to the Query enrichment component that was presented in a paper [7] at the NLDB 2006 conference 8 , depicted as paper 1 in Fig. 1.…”

Section: Preliminary Resultsmentioning

confidence: 99%

“…In our approach [7], we propose a query enrichment approach that uses contextually enriched ontologies to bring the queries closer to the user's preferences and the characteristics of the document collection. The idea is to associate every concept (classes and instances) of the ontology with a feature vector (ƒv) to tailor these concepts to the specific document collection and terminology used.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research on Ontology-Driven Information Retrieval

Tomassen

2006

On the Move to Meaningful Internet Systems 2006: OTM 2006 Workshops

Self Cite

View full text Add to dashboard Cite

An increasing number of recent information retrieval systems make use of ontologies to help the users clarify their information needs and come up with semantic representations of documents. A particular concern here is the integration of these semantic approaches with traditional search technology. The research presented in this paper examines how ontologies can be efficiently applied to large-scale search systems for the web. We describe how these systems can be enriched with adapted ontologies to provide both an in-depth understanding of the user's needs as well as an easy integration with standard vector-space retrieval systems. The ontology concepts are adapted to the domain terminology by computing a feature vector for each concept. Later, the feature vectors are used to enrich a provided query. The whole retrieval system is under development as part of a larger Semantic Web standardization project for the Norwegian oil & gas sector. 1 NFR project number 163457/S30. 2 http://www.iso.org/

show abstract

Section: Preliminary Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Ontology-Driven Information Retrieval

Tomassen

2006

On the Move to Meaningful Internet Systems 2006: OTM 2006 Workshops

Self Cite

View full text Add to dashboard Cite

show abstract

“…This ontology used for analysing data and interpreting user needs may allow data to be related across phases and disciplines, helping people collaborate and reducing costs and risks. Based on the work done on the IIP project, Tomassen et al (2006) proposed a method to improve information retrieval quality by using ontologies. The ontology used is the one developed in IIP, which is based on ISO 13628 and it will be modelled in ISO 15926.…”

Section: Kiritsismentioning

confidence: 99%

Semantic technologies for engineering asset life cycle management

Kiritsis

2013

International Journal of Production Research

View full text Add to dashboard Cite

The use of semantic technologies and ontologies is becoming more and more popular in engineering applications and particularly in product modelling. Still, the use is limited in academia and applications are of a small scale. In this paper we present the research work done by the closed-loop life cycle management (CL2M) team of the Laboratory for Computer-Aided Design and Production (LICP) at the Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland, on the use of ontology-based technologies for the life cycle management of products and engineering assets. This research has been performed through a number of PhD works partially financed by the European Framework Program for research. It aims at providing both a wider understanding of the benefits of applying such technologies in the complex environment of asset life cycle management (ALM) and at providing a platform for implementing ontology models in industrial environments.

show abstract

“…Unsupervised keyphrase extraction has the advantage of being more widely applicable, since the method does not require any knowledge of the domain or consultation of domain experts. On the other hand, supervised keyphrase extraction normally produces more relevant keyphrases and can with repeated training improve the quality of its own keyphrases (see for example [18,20,22]). A list of keyphrases gives a high-level summary of the document content.…”

Section: Ontology Learning With Unsupervised Keyphrase Extractionmentioning

confidence: 99%

“…As noted by Voorhees, it is not obvious that adding semantically related terms will improve the quality of the search application [21]. However, experiments with domain-dependent vocabularies -instead of Voorhees' WordNet approach -does indicate that careful semantic refinement of queries may be useful [18]. Mitra et al [13] is refining the query based on blind feedback, i.e.…”

Section: Related Workmentioning

confidence: 99%

Ontology Learning for Search Applications

Gulla

Borch

Ingvaldsen

On the Move to Meaningful Internet Systems 2007: CoopIS, DOA, ODBASE, GADA, and IS

Self Cite

View full text Add to dashboard Cite

Abstract. Ontology learning tools help us build ontologies cheaper by applying sophisticated linguistic and statistical techniques on domain text. For ontologies used in search applications class concepts and hierarchical relationships at the appropriate level of detail are vital to the quality of retrieval. In this paper, we discuss an unsupervised keyphrase extraction system for ontology learning and evaluate its resulting ontology as part of an ontology-driven search application. Our analysis shows that even though the ontology is slightly inferior to manually constructed ontologies, the quality of search is only marginally affected when using the learned ontology. Keyphrase extraction may not be sufficient for ontology learning in general, but is surprisingly effective for ontologies specifically designed for search.

show abstract

Document Space Adapted Ontology: Application in Query Enrichment

Cited by 14 publications

References 10 publications

Research on Ontology-Driven Information Retrieval

Research on Ontology-Driven Information Retrieval

Semantic technologies for engineering asset life cycle management

Ontology Learning for Search Applications

Contact Info

Product

Resources

About