An ontology-based cluster analysis framework

Lula, Paweł; Paliwoda‐Pękosz, Grażyna

doi:10.1145/1452567.1452574

Cited by 12 publications

(3 citation statements)

References 3 publications

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“…Ontologies allow performing the knowledge representation by means of terms and definitions about the concepts related to a given domain, promoting the representation of the semantic relationships, and not only the syntactic relationship of the data. When developing ontology it is necessary to describe the categories and objects as well as the relationships between the data involved (Lula & Paliwoda-Pękosz, 2008). Ontology has the exploration and representation capability of semantic and conceptual relationships of the terms belonging to a particular domain (Silva et al, 2009).…”

Section: Ontologymentioning

confidence: 99%

A Model to Support Decision Making in the Idea Management Domain

Sérgio¹,

Gonçalves²,

Souza³

2015

FSRJ

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Taking into account the global competitiveness, innovation has become a challenge for organizations. Idea management is an integral part of the innovation process and it is presented as an essential factor for achieving success. Due to the volume and sudden peaks in submissions of ideas, the appropriate analysis and the allocation of resources for investment are important issues to be addressed. The objective of this paper is to present a model for the management of ideas based on ontology and cluster analysis in order to maximize resources for investment in ideas. So as to demonstrate the model feasibility it was prepared a dataset comprised of fifty-five ideas collected from the Starbucks® site. These ideas were then stored in the domain ontology and were used as subsidies for the cluster analysis and for the building of a knowledge base. As a result, it was identified groups with similar ideas that, when analyzed, foster a greater potential for observation and may indicate patterns and trends that can assist in decision making.

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Section: Ontologymentioning

confidence: 99%

A Model to Support Decision Making in the Idea Management Domain

Sérgio¹,

Gonçalves²,

Souza³

2015

FSRJ

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“…Therefore, it is not surprising that the relationship between metadata and ontology has drawn researchers' attention for quite some time; for example, a recent case study of constructing ontology from metadata in the weather domain was reported (Chen & Plale, 2012). In particular, the uses of metadata and ontologies for document clustering and analysis have been addressed from various perspectives (e.g., Wong et al, 2003;Lula and Paliwoda-Pekosz, 2008).…”

Section: Relevance Of Metadata and Ontology To Document Clusteringmentioning

confidence: 99%

Multi-View Meets Average Linkage

Ravoori

Chen

2015

International Journal of Information Retrieval Research

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Inspired by the success of a recently developed algorithm MVSC-IR, the authors embed the idea of Multi-Viewpoint Based Similarity Measure for clustering (MVSC) into a hierarchical clustering method, i.e., average linkage clustering, to overcome the problem of initiation with random seeds, resulting in a new algorithm, referred to as MVSC-HAC. The improved performance of this new algorithm encouraged us to further explore the impact of metadata in document clustering. In this paper, after reviewing two existing algorithms, the authors describe their new algorithm and present experimental results on various sizes of data sets at two different levels: the one using the entire context of documents and the one using existing meta tags of the documents. The result shows MVSC-HAC excels at both levels. The authors analyze the results, and provide a discussion based on other observation on the role of metadata in document clustering.

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“…Our approach utilizes both content-based and collaborative filtering. The crucial point in this study is that the contentbased filtering in our approach uses semantic similarity measures on ontology-based metadata, based on the studies in (Maedche and Zacharias, 2002) and (Lula and Paliwoda-Pekosz, 2008), instead of naive Bayesian classifier (Mitchell, 1997) which is mentioned in (Melville et al, 2002). After that, collaborative filtering is performed using enhanced data to overcome over-specialization problem in contentbased filtering.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Content-Boosted Collaborative Filtering Using Semantic Similarity Measure

Ceylan¹,

Birtürk

2011

Proceedings of the 7th International Conference on Web Information Systems and Technologies

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Collaborative filtering is one of the most used recommendation approaches in recommender systems. However, collaborative filtering systems have some major problems such as sparsity, scalability and cold-start problems. In this paper we focus on the sparsity and item cold-start problems in collaborative filtering in order to improve the quality of recommendations. We propose an approach that uses semantic similarities between items based on a priori defined ontology-based metadata in the movie domain. According to the semantic similarities between items and past user preferences, recommendations are made. The results of the evaluation phase show that our approach improves the quality of recommendations.

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An ontology-based cluster analysis framework

Cited by 12 publications

References 3 publications

A Model to Support Decision Making in the Idea Management Domain

A Model to Support Decision Making in the Idea Management Domain

Multi-View Meets Average Linkage

Content-Boosted Collaborative Filtering Using Semantic Similarity Measure

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