Stage: stereotypical trust assessment through graph extraction

Name: Stage: stereotypical trust assessment through graph extraction
Author: Şensoy, Murat, Yilmaz, B., Norman, T. J.

İsim	Stage: stereotypical trust assessment through graph extraction
Yazar	Şensoy, Murat, Yilmaz, B., Norman, T. J.
Basım Tarihi:	2016-02
Basım Yeri	- Wiley
Konu	Trust and reputation, Semantic Web, Graph mining
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1467-8640
Kayıt Numarası	a9b4c3e6-d09a-4c4d-986d-30b98c11655d
Lokasyon	Computer Science
Tarih	2016-02
Notlar	U.S. Army Research Laboratory ; U.K. Ministry of Defence ; U.S. Army Research Laboratory ; TÜBİTAK
Örnek Metin	Bootstrapping trust assessment where there is little or no evidence regarding a subject is a significant challenge for existing trust and reputation systems. When direct or indirect evidence is absent, existing approaches usually assume that all agents are equally trustworthy. This naive assumption makes existing approaches vulnerable to attacks such as Sybil and whitewashing. Inspired by real-life scenarios, we argue that malicious agents may share some common patterns or complex features in their descriptions. If such patterns or features can be detected, they can be exploited to bootstrap trust assessments. Based on this idea, we propose the use of frequent subgraph mining and state-of-the-art knowledge representation formalisms to estimate a priori trust for agents. Our approach first discovers significant patterns that may be used to characterize trustworthy and untrustworthy agents. Then, these patterns are used as features to train a regression model to estimate the trustworthiness of agents. Last, a priori trust for unknown agents (e.g., newcomers) is estimated using the discovered features based on the trained model. Through empirical evaluation, we show that the proposed approach significantly outperforms well-known trust approaches if trustworthiness of agents is correlated with patterns in their descriptions or social networks. Furthermore, we show that the proposed approach performs at least as good as the existing approaches if such correlations do not exist.
DOI	10.1111/coin.12046
Cilt	32

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Stage: stereotypical trust assessment through graph extraction

Yazar Şensoy, Murat, Yilmaz, B., Norman, T. J.

Basım Tarihi 2016-02

Basım Yeri - Wiley

Konu Trust and reputation, Semantic Web, Graph mining

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1467-8640

Kayıt Numarası a9b4c3e6-d09a-4c4d-986d-30b98c11655d

Lokasyon Computer Science

Tarih 2016-02

Notlar U.S. Army Research Laboratory ; U.K. Ministry of Defence ; U.S. Army Research Laboratory ; TÜBİTAK

Örnek Metin Bootstrapping trust assessment where there is little or no evidence regarding a subject is a significant challenge for existing trust and reputation systems. When direct or indirect evidence is absent, existing approaches usually assume that all agents are equally trustworthy. This naive assumption makes existing approaches vulnerable to attacks such as Sybil and whitewashing. Inspired by real-life scenarios, we argue that malicious agents may share some common patterns or complex features in their descriptions. If such patterns or features can be detected, they can be exploited to bootstrap trust assessments. Based on this idea, we propose the use of frequent subgraph mining and state-of-the-art knowledge representation formalisms to estimate a priori trust for agents. Our approach first discovers significant patterns that may be used to characterize trustworthy and untrustworthy agents. Then, these patterns are used as features to train a regression model to estimate the trustworthiness of agents. Last, a priori trust for unknown agents (e.g., newcomers) is estimated using the discovered features based on the trained model. Through empirical evaluation, we show that the proposed approach significantly outperforms well-known trust approaches if trustworthiness of agents is correlated with patterns in their descriptions or social networks. Furthermore, we show that the proposed approach performs at least as good as the existing approaches if such correlations do not exist.

DOI 10.1111/coin.12046

Cilt 32