Tag Relatedness Using Laplacian Score Feature Selection and Adapted Jensen-Shannon Divergence

Sergieh, Hatem Mousselly; Döller, Mario; Egyed-Zsigmond, Elöd; Gianini, G.; Kosch, Harald; Pinon, Jean-Marie

doi:10.1007/978-3-319-04114-8_14

Cited by 7 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance of each method was evaluated on the basis of correlation coefficient with JCN [2], which we refer to as CCJ in this section. JCN is a representative ontology-based ICDM method using WordNet and can provide ICDs close to human perception; so JCN has been often used as the basis for performance evaluation in previous works [6,8].…”

Section: Discussionmentioning

confidence: 99%

“…The text-based ICDM [4][5][6] counts the co-occurrence frequency of two text words u and v in a web-scale corpus and regards the inverse of the co-occurrence frequency as ICD (u, v). Since each concept is described by a text word, we can say the text-based methods are more direct than the image instance-based methods.…”

Section: Text-based Icdmmentioning

confidence: 99%

“…In the past decade, several methods for measuring inter-concept distances (ICD) have been proposed [2][3][4][5][6][7][8][9]. These methods can be roughly divided into three categories: ontology-based, text-based, and image instance-based.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Inter-Concept Distance Measurement with Adaptively Weighted Multiple Visual Features

Nakamura

Babaguchi

2015

Computer Vision - ACCV 2014 Workshops

View full text Add to dashboard Cite

Abstract. Most of the existing methods for measuring the inter-concept distance (ICD) between two concepts from their image instances use only a single kind of visual feature extracted from each instance. However, a single kind of feature is not enough for appropriately measuring ICDs due to a wide variety of perspectives for similarity evaluation (e.g., color, shape, size, hardness, heaviness, and functions); the relationships between different concept pairs are more appropriately modeled from different perspectives provided by multiple kinds of features. In this paper, we propose extracting two or more kinds of visual features from each image instance and measuring ICDs using these multiple features. Moreover, we present a method for adaptively weighting the visual features on the basis of their appropriateness for each concept pair. Experiments demonstrated that the proposed method outperformed a method using only a single kind of visual feature and one combining multiple kinds of features with a fixed weight.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Text-based Icdmmentioning

confidence: 99%

See 1 more Smart Citation

Inter-Concept Distance Measurement with Adaptively Weighted Multiple Visual Features

Nakamura

Babaguchi

2015

Computer Vision - ACCV 2014 Workshops

View full text Add to dashboard Cite

show abstract

“…The work of [10] proposes a new approach for measuring tag relatedness based on co-occurrence statistics. In [7] the authors adopt different similarity measures and techniques for synonymy detection.…”

Section: Related Workmentioning

confidence: 99%

A supervised learning approach to detect subsumption relations between tags in folksonomies

Rêgo

Marinho

Pires

2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

View full text Add to dashboard Cite

The lack of hierarchical relations in the tag space of social tagging systems may diminish the ability of users to find relevant resources. Many research works propose to overcome this problem by constructing hierarchies of tags automatically by means of heuristic algorithms. These hierarchies encode subsumption relations between pairs of tags and can be used for improving browsing and retrieval of resources. In this paper, we cast the problem of subsumption detection between pairs of tags as a pairwise classification problem. From the literature, we identified several similarity measures that are good indicators of subsumption, which are used as learning features. Under this setting, we observed severe class imbalance and class overlapping which motivated us to investigate and employ class imbalance techniques to overcome these problems. We conducted a comprehensive set of experiments on a large real-world dataset, showing that our approach outperforms the best performing heuristic-based baseline.

show abstract

“…Mousselly et al [87] proposed an approach called Adapative Jenses-Shannon Divergence (AJSD) for finding related tags and is based on calculating distance between tag distributions using Jenses-Shannon Divergence. Probability distribution for each tag is calculated using co-occurrence and Laplacian.…”

Section: Similarity/equivalencementioning

confidence: 99%

Semantics discovery in social tagging systems: A review

Jabeen

Khusro

Majid

et al. 2014

Multimed Tools Appl

View full text Add to dashboard Cite

Web 2.0 has brought many collaborative and novel applications which transformed the web as a medium and resulted in its exponential growth. Tagging systems are one of these killer applications. Tags are in free-form but represent the link between objective information and users' cognitive information. However, tags have ambiguity problem reducing precision. Hence search and retrieval pose a challenge on folksonomy systems which have flat, unstructured, non-hierarchical organization with unsupervised vocabulary. We present a brief survey of different approaches for adding semantics in folksonomies thus bringing structure and precision in search and navigation. We did comparative analysis to estimate the significance of each source of semantics. Then, we have categorized the approaches in a systematic way and summarized the feature set support. Based on the survey we end up with recommendations. Our survey and conclusion will prove to be relevant and beneficial for engineers and designers aiming to design and maintain well structured folksonomy with precise search and navigation results.

show abstract

Tag Relatedness Using Laplacian Score Feature Selection and Adapted Jensen-Shannon Divergence

Cited by 7 publications

References 8 publications

Inter-Concept Distance Measurement with Adaptively Weighted Multiple Visual Features

Inter-Concept Distance Measurement with Adaptively Weighted Multiple Visual Features

A supervised learning approach to detect subsumption relations between tags in folksonomies

Semantics discovery in social tagging systems: A review

Contact Info

Product

Resources

About