[ieee 2009 international conference on advances in social network analysis and mining (asonam) -...

Identify implicit social network by RST/FL framework

Hong Feng Lai Department of Business Management, National United University

walden.lai@msa.hinet.net

Abstract The rapid growth of internet has transformed the way

relationships between companies and their customers. To find the implicit social network and form customer club (community) for customers is a critical requirement in some businesses. The social role analysis attempts to find explicit similarities between actors in the network. Traditional clustering methods based on attributes between actors lack for logic foundation. In this paper, we apply rough set theory to partition objects into equivalence classes interpreting the hidden community. This paper proposes a framework to find the implicit social network based on rough set theory and frame logic to extract and express the social structure and relationship. The development framework includes three levels, i.e. conceptual level, logical level and validation level. In conceptual level, we explore the properties of social network. The logic and validation is based on frame logic. The interface of different level is a mapping from a source model to a target model using a set of transformation rules. Finally, we apply FLORID tools to evaluate the correctness and the adequacy of the model. 1. Introduction

A social network is a set of actors that may have relationships with one another. Social network analysis (SNA) is a methodology applied extensively in various fields. The outcome of social network analysis is usually associated with index and property. These disclose the hidden role or relationship between the objects or actors. Recently, more and more social network data has been investigated for the purpose of further application [1]. Social network analysis is traditionally based on the explicit relationship. Instead of constructing explicit social relationship, we explore to retrieve those implicit relationships in a customer database [2] .

To formulate the internet information of social networks, we have to consider the attributes of objects and the relationships between objects. Predicting the characteristics of customers and providing the personalized services based on customers’ characteristics are the important issues. However, the research for offering the personalized services considering the customers’ characteristics is a relatively insufficient research field [3].

However, the input data of a social network is often still created manually unsupported by computer systems due to intrinsic complexity. How to construct hidden community by rough set theory is rarely emphasized by past studies. In real social networks, there are various types of relations. Each relation can be taken as a relation network [4]. An object-oriented and attribute based logic F-logic is suitable. Moreover, logical specifications describe system requirements formally. Through formal semantics, it supports deductive capabilities that make specifications executable [5]. Since the social network lacks for foundation and logic semantics, in this study we aim at constructing sufficient formality to allow formal analysis and to verify the properties of a social network. To embed deductive capability, we transform the rough set data model into a logical specification language, F-logic [6]. How to transform the rough set into formal specifications is investigated in this study. An example of a traveling agency will be exploited to validate the feasibility of the RST/FL framework. 2. The RST/FL framework

In this study, we take the implicit social network as hidden community, and represented by the equivalent class of an indiscernibility relation in rough set theory (RST). The rough set with frame logic (FL) development framework will be expressed in this section as follows.

This paper proposes a framework to find the implicit social network based on RST and FL to extract and express the social structure and relationship in customer databases.

In this study we aim at constructing sufficient formality to allow formal analysis and to verify the properties of the implicit social network, we transform the RST into a logical specification language, F-logic, which is proposed by Kifer et al. [6].

The transformation framework between the RST and F-logic is displayed in Fig. 1. The deductive RST consists of three components: customer object set, set operation rules, and the deductive rough set theory. 3. F-logic and transformation rules

F-logic extends predicate logic and provides a sound and complete resolution-based proof procedure [6]. F-logic is powerful in expressing object-oriented features. Elements are described by identification terms, which

2009 Advances in Social Network Analysis and Mining

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DOI 10.1109/ASONAM.2009.62

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consist of variables, functions, or constants, similar to terms in first order logic language.

Fig. 1. The RST/FL development framework.

To extract the implicit social network by rough set and frame logic involves a process of model transformation. Model transformation is a mapping from a source model to a target model using a set of transformation rules [5]. There is a natural correspondence between the rough set and F-logic.The following transformation rule express how to transform the rough set theory in F-logic form. Rule. The following rule is to find all of the

indiscernibility set of each object. ind_partition(P):set[partition->>Ind;Ind->>Xj] :- object_set:set[element->>Xi], Ind=ind(P,Xi):set[element->>Xj]. After implementing the deductive system, the logic-

based customer database system consist of a set of objects, a set of attributes, a set of structural assertions, and some deductive rules about these elements. Various types of queries can be evaluated and answered by FLORID. 4. Related works

To express and investigate the implicit social network, three types of approaches have been proposed: heuristic-based, probability-based and logic-based approaches. The heuristic-based approaches apply a set of heuristics to extract the implicit social network. The probability-based approaches include Bayes rule and regression approach. The logic-based using logic for inferring the implicit social network includes first order logic, description logic and RuleML/POSL (POsitional Slotted Language).

The logic-based approaches using rules and declarations define the interaction and relationship between objects. From object-oriented viewpoint, the social network systems could be taken as a set of interacting objects. To protect personal privacy in tabulated data under the disclosure of social network data, in [1] adopts description logic (DL) to represent formalism and the metrics of anonymity. To find an expert within a community, in [7] integrating RDF-based FOAF (Friend Of A Friend) project and OO jDREW (java Deductive Reasoning Engine for Web) with RuleML and

POSL. The scenarios of expert finding and collaboration decision are implemented by POSL that combines Prolog’s positional and F-logic’s slotted syntaxes for expressing the knowledge base including facts and rules in the Semantic Web. 5. Conclusions

We propose a new framework to extract implicit social network to interpret various features of social networks. To express the implicit social network, we propose rough set and frame logic to represent implicit relationship between objects by RST/FL framework.

We apply a social network example to validate the RST/FL methodology. With the help of FLORID, the complexity of social network analysis is reduced. Moreover, the extracting process and result can be investigated and validated by simulation of logic proof. References [1] D.-W. Wang, Liau Churn-Jung, and Hsu Tsan-

sheng, "Privacy Protection in Social Network Data Disclosure Based on Granular Computing," in 2006 IEEE International Conference on Fuzzy Systems, Vancouver, BC 2006, pp. 997-1003.

[2] S. Kinsella, J. Breslin, A. Passant, and S. Decker, "Applications of Semantic Web Methodologies and Techniques to Social Networks and Social Websites," in Reasoning Web,Lecture Notes in Computer Science. vol. 5224 Berlin / Heidelberg: Springer 2008, pp. 171-199.

[3] J. Hong, E.-H. Suh, J. Kim, and S. Kim, "Context-aware system for proactive personalized service based on context history," Expert Systems with Applications, vol. In Press, Corrected Proof, 2009.

[4] D. Cai, Z. Shao, X. He, X. Yan, and J. Han, "Community Mining from Multi-relational Networks," in Knowledge Discovery in Databases: PKDD 2005. vol. 3721: Springer Berlin / Heidelberg, 2005, pp. 445-452.

[5] G. W. Mineau, R. Missaoui, and R. Godinx, "Conceptual modeling for data and knowledge management," Data & Knowledge Engineering, vol. 33, pp. 137-168, 2000.

[6] M. Kifer, G. Lausen, and J. Wu, "Logical foundations of object-oriented and frame-based languages," Journal of the Association for Computing Machinery, vol. 42, pp. 741-843, 1995.

[7] J. Li, H. Boley, V. C. Bhavsar, and J. Mei, "Expert Finding for eCollaboration Using FOAF with RuleML Rules," in The Montreal Conference on eTechnologies Montreal, Canada, 2006, pp. 53-65.

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