Towards Knowledge Creation and Management ...

Towards Knowledge Creation and Management Model over Online Social Networks +

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Waleej Haider , Nouman M Durrani , Shardha Nand , Nadeem Kafi Khan , M Asad Abbasi* *

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Sir Syed University of Engineering and Technology, Karachi.

FAST National University of Computer & Emerging Sciences, Karachi.

Abstract — An online Social network (OSNs) is an active platform of user socialization belonging to different societies and has a subject of interest for researchers. In OSNs, relationships among users and groups are created to share information; and turned out a foundation of knowledge. However, user-generated contents are disseminated, unstructured, and dynamic in nature. As there is no central authority to own and maintain these large numbers of user-generated contents, it is difficult to process and extract knowledge from it. Moreover, it is far challenging to collect, manage, and classify randomly transmitted useful contents and to associate them for the creation of knowledge. Further, in complex networks where graph nodes belong to more than one context or groups, ubiquitous community detection is more challenging in such scattered data formats. In this paper, a model for knowledge creation over online social networks has been presented. The model explores the strength of social relationship, users’ online behavior by their interests and hobbies, and the frequency of users participated together in a particular context. It helps in smoother collaboration among users and groups, enhance learning by sharing information, and improve efficiency of the social graphs for community detection. Moreover, based on the available information in different context, the model facilitates efficient decisions, knowledge and innovations in OSNs. Keywords— Knowledge Management, Online Social Networks, Knowledge Creation, Community Detection

I.

INTRODUCTION

A social structure consists of a combination of social actors (individuals or organizations) which links through complex and dyadic ties is recognized as social network. Knowledge is not easy to define. Part of the complexity perhaps lies in the difference between data, information, and knowledge. Data, at the practical level, consists of raw words, numbers, images and facts acquired from observation or measurement, even as information refers to processed data in a meaningful form and pattern, and knowledge is understood as processed or authenticated information that has been incorporated into a logical framework of understanding. As this categorization has been widely used apparently in the field of information systems, at the theoretical level, framework can be rented to discriminate the difference between information, knowledge and understanding. The researcher makes it clear that information and Knowledge are important elements in all approaches of development, as the process of development is always based on processing of information and some level of knowledge. Two dimensions of knowledge have been described in [4] as explicit and tacit knowledge. Furthermore, strategy and process are two aspects, central to

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Knowledge Management (KM) in organization. First, in order to use the capabilities and knowledge resources of organization, the formation of KM strategies is important. two categories of KM strategy are (i) system strategy which underlines the potential to create, store, share out and apply the organization’s explicit knowledge, and (ii) human strategy which illustrate knowledge sharing through interpersonal interaction using exchange of ideas through social networks for instance teamwork [5]. The second aspect, process of KM is proposed by many scholars in different ways as there are fundamentally four important processes: [5, 6] (i) knowledge creation; (ii) storage; (iii) distribution; and (iv) application. Knowledge Management (KM) is ‘‘the process of significantly managing knowledge to meet existing needs, to identify and use existing knowledge and obtain knowledge assets to develop new prospects” [4]. Some latest powerful computational platforms [23] have become an essential research tool helping scientists to analyze complex networks. These analyses provide the extraordinary capability to visualize, trace, explain, analyze, and simulate the construction and performance of social networks. Now a day, a hot research area on social networks has, therefore, is the design and uses of information technology in social contexts and their effects on structures and how they outline the behavior of end-user [2]. In social networks, the researchers have categorized the research drawing of information system (IS) into the following levels: (i) network awareness at individual and organizational level, (ii) IS analysis of social networks, and (iii) management of fast evolving conceptual and technological platforms in social network [2]. A good collaboration depends on trust. The level of this trust provides the foundation to the degree of knowledge discovery, presenting, and distribution between the social communities. It is found that the Trust among persons of various communities has been an important and positive predictor for achieving objectives to share knowledge in an expert-sharing network [17]. Since, Knowledge is implicit and has different perspective and could be useless for the receptors unit, therefore it will have complexities in understanding its meaning, so one may be hesitate to transfer this knowledge. Receptors less capability to absorb shared knowledge can be another reason in late transferring knowledge to the other units [1]. Network analysis is used as a tool for knowledge management as well as strategic management. The use of KMS is explored in [3] on the basis of importance on social relationships by studying a company. To create the social relationships, social capital theory is applied to build its three dimensions: tie strength, shared norms, and trust. Tie strength describes the proximity and frequency of number of communication sessions between contributors and seekers of knowledge. Shared norms administer that how the members of community within an organization act, feel, take decisions, and how

they recognize the other social networks. Strong ties provide a steady flow of new ideas, innovation and operational support. Thus, to maintain a high innovative potential over time required a network architecture that permits combining dense networks and structural holes [1, 3]. Relationships created to share knowledge has become an important source of new knowledge. Thus, Knowledge creation may be depends on one or both ways (i) by exploring, which involves searching for completely new knowledge and (ii) by exploiting, which process existing knowledge. [1] Research has pointed out a close relationship between knowledge and innovation. Innovating is obtained by applying knowledge to create new knowledge and social relationships play an excellent part in this process. Knowledge creation is also defined as ‘a social process involving relationships among both organizations and individuals with different backgrounds, resources, tendency and approaches. [1, 4] Besides creating a business interaction or finding a romantic relationship, social networking cater services to combine social networks for sharing content as bookmarks, photos ,documents, reviews as well as personal information of individuals or organizations. The idea of knowledge sharing over the IP networks is based on the sociological theory which states that social contact creates similarity as friends can attained or produce similar interests [10]. an ambient system is designed and implemented In [12] to contribute in decision making for prevention of childhood fatness and related long-lasting diseases by increasing relationships between the Semantic Web with proper standards and the Social Web, with its ubiquitous features. This system generates medical and social ontological knowledge. Data mining algorithms and tools are used for managing large amount of data to extract patterns, and gain perceptive knowledge to resolve real-world problems [11, 14]. The knowledge discovery process from data generally involves the following tasks: data preprocessing, data mining, and post processing [17]. Numerous studies based on Online Social Network (OSN) users to address many problems have been accomplished by using data mining techniques [13, 15, and 16]. Many other techniques for instance association rule mining, visual analytics feature selection, instance selection, and anomaly detection are also helpful in information recovery and knowledge management as well as to study human behavior using social network data [19, 20, 21, 22]. Recently, social media is considered to play a contributory role in easing mass movements and Occupy Wall Street. In this paper, a knowledge creation and management model over online social networks is proposed. This paper is organized as, evolution of Knowledge Management Systems (KMS) in social networks and knowledge creation, sharing and management techniques are discussed in section 2, proposed knowledge creation and management model over online social networks is presented in 3 and finally this paper ends with conclusion and future work in section 4. II.

RELATED WORK

In this section, we discuss the state of the art work presented in the field of knowledge management in the complex networks. The section starts with the evolution of Knowledge Management Systems (KMS) in social networks and discusses various approaches used to create, manage and share information or knowledge between individuals, groups, organizations or communities.

Tacit

Socialization Tacit Field Building

Explicit

Dialogue

Explicit

Externalization Writing it down Creating metaphors and analogies Modeling

Sharing experiences Observing, imitating Brainstorming without criticism

Internalization

Combination

Access to codified knowledge Goal based training

Sorting, adding categorizing Methodology creation Best practices

Linking Explicit Knowledge

Learning by Doing

Fig. 1: Four modes of knowledge conversion [4] A. Knowledge Creation Using KMS: Social capital theory based solution is proposed in [3] which focused on social relationship concept. Structural, cognitive, and relational factors are thought as dimensions of social capital surrounded within a social relationship play a vital role in knowledge creation and improve knowledge sharing. Social relationships are produced by tie strength, shared norms, and trust. B. Transmission, sharing and combination of knowledge: Literature review based on social capital theory that describes the relationships between social networks, knowledge and innovative performance is depicted in [1]. The aim of this study is to categorize literature that analyses how the knowledge insertion in social networks influenced the innovative performance. Innovative performance is achieved by transfer, exchange and combination of knowledge. Moreover, different theories on social capital are presented, which defines various dimensions regarding access to information and knowledge shared by the participators in social networks. Structural, Relational and Cognitive dimensions characterized general pattern, type such as trust and identity and mutual understanding of relationships between actors.

C. Knowledge creation through the conversion modes: Knowledge creation by using the conversion modes is a continuous and dynamic interaction between tacit and explicit knowledge – represented as knowledge spiral (shown in Figure 1). The knowledge conversion and how it can advantage the organizations is related to the conversations within the KM, was the burning issue in the last decade. This is particularly due to the dynamic changes in markets and industries, globalization, and new trends of competition have become the source of rapidly development of organizational knowledge. [4] D. Knowledge creation through the conversion modes: A model is adopted in [7] which reflect continual knowledge building (see Figure 2). Knowledge creation is the first KM process describes how organizations build up new content or replace the existing content. Combination process can also be created new knowledge whereby participators transfer their explicit knowledge to another explicit knowledge as well as when the individuals share their work experience.

Knowledge Application

Knowledge Creation

Knowledge Distribution

Knowledge Storage

Fig. 2: Knowledge Creation Model [7]

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Second step, knowledge storage, is an effort towards avoid losing the acquired knowledge which can be retrieved. Knowledge storage has two viewpoints: (i) individual and (ii) organizational. Knowledge distribution is aims to provide the right knowledge to the right person at the right time. Lastly, the application of knowledge is the most vital process of KM as knowledge supports to organizational performance when it is being considered for decision-making as well as performing an action [24]. It activates the complete cycle of KM processes and participate in project executions [7].

E. Data integration across multiple and heterogeneous online data sources: The conceptual as well as practical aspects of approach are presented in literature [9]. An ontology is illustrated with the practice of knowledge modeling which is then served as a standard across multiple heterogeneous online data sources for data integration. Semantic lifting of data from these sources is aimed as the practical part of this approach which includes straightforward lifting of structured data. Extraction of semantics from unstructured and semi-structured data is another challenge to be faced [9]: •

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Requirements for Modeling Online Posts: This modeling comprises four major steps (a) to support and re-use on hand standards mainly the W3C submission for SIOC. b) Combination within traditional PIM knowledge models. c) Semantic Disintegration into autonomous sub-posts d) illustration of promising online Social Web practices. DLPO, an overview: DLPO is a brief ontology that detains all features of dynamic personal information contributed by the online posts, and their variety of derived links to the data clouds of personal and global semantic. The D L P O

tisfying all of the requirements mentioned in previous section. First, the superclass dlpo:LivePost is used as an extension of the general sioc:Post that inherit all properties of SIOC that apply (e.g. sioc:has creator, sioc:hasTopic). Two sub properties of the fundamental SIOC properties introduces by the DLPO are sioc:reply Of and sioc:has reply. dlpo:hasReply and dlpo:replyOf are used within the DLPO context. Secondly, it is guaranteed that the information characterized by DLPO is definitely combined in the broader context of distributed modeling of personal information [9]. Top to bottom architecture of Flink: Flink system is presented with ontology of research topics for the extraction (from web pages, publication archives, emails, and FOAF profiles), aggregation and visualization of online social networks [10]. Flink architecture is divided into three layers (top to bottom) focused on metadata acquisition, storage and visualization. At the Acquisition layer, four types of knowledge sources are used by the Flink as HTML pages, emails, Semantic Web FOAF (Friend Of A Friend) profiles, and bibliographic data. Web mining is applied for co-occurrence analysis to data extraction and finding topic interests applying associations on a social network. Inference and storage layer resides in the middle for enhancing and storing data using reasoning. Network ties, interest relations and metadata are characterized in the RDF using foaf:knows for relationships and foaf:topic interest for research interests. The aggregated RDF data is collected and stored in a Sesame server. Finally, Visualization layer provides the Java web application based user interface. The main idea of Model - View-Controller (MVC) design is to split-up of the components responsible for the data, the application logic and the web interface [10].

III.

KNOWLEDGE CREATION AND MANAGEMENT MODEL

In this section, we are discussing Knowledge creation and management framework over online Social Networks, and show that how proposed framework facilitates in Best decision making and increase speed of innovation by reuse of information and knowledge.

i s s a

Fig. 3: The live post ontology [9]

Data Sources Operations/ Actions Data

Processes, Algorithms, data and text mining, data aggregation techniques, data integration/ merging/ transformation,

Experience Information

Knowledge Sharing Block

Algorithms, Probabilistic models, combinations, filtering, community detection, clustering

Rethink Knowledge

Knowledge sharing mechanisms, formal actions, ideas, practice, machine learning algorithms (Social Graph,)

Innovation

Rethinking

Social Communities

Wisdom Figure 4: Knowledge Creation and Management Model Data over online social networks are mostly user-generated contents. This data is huge, unstructured, scattered, and dynamic. The proposed model comprises knowledge extraction from online data being transmitted over social networks by using different type of operations at each stage of this model and defining the communities on the basis of similarities of various attributes. Social contacts are generated when similar interests are shared by the users of different communities and associations between the users depend upon the number of same opinions about the shared contents. The proposed model is shown in the figure 2 which comprises of four stages of operation combined with related actions to generate input of the next stage. A. Stage I: Data Acquirement At this stage, crowd sourced heterogeneous data is being transmitted from different sources is acquired and stored in the social database [25]. After attaining this huge data, web mining and data analyzing techniques are used to refine this data.

A web content mining tool known as context focused crawler (CFC) is proposed which performs crawling in two phases by using context graphs.in first step, context graphs and classifiers are constructed using a set of seed documents as a training set. In the second step, crawling is performed using the classifiers to guide it. The context graphs are updated as the crawl takes place. Supervised algorithms are used to build classification models from the training data and then use the learned models for prediction. K-Nearest Neighbor Algorithm is applies in order to mine the data base as there is a need to classify some new users having different habits. After applying the coding to assign the distance values according to the new user, we find the Euclidian distance (K) for new tuple to the training set using the following formula K” ¥no. of training sets and find the nearest neighbor. Subsequently, we find the habit of new user in the social network. New participants or users on the social networks consistent with data contents, transmitted by these users are assigned to predefined segments known as clusters as shown in the figure 5.

It is a non-agreed class from nature with unknown label and is based on the principal describes as the cluster maximize the intra-class similarity and minimize the interclass similarity. Clustering can also helpful in development of taxonomy and to form new classes. K-means algorithm is useful to assign a cluster to the users according to their behavior and online activities on the social network. K-means is chosen due to its low computational complexity as compared to hierarchical clustering algorithms. Hereafter classification algorithms are applied to help these groups in addition to new incoming users for assignment of predefined clusters. B. Stage II: Information Retrieval Data aggregation is applied to generalize a large task relevant data in a descriptive manner. This will help in developing a set of models and to predict the behavior of a new dataset. Experience of active users are also gathered and applied on the processed data. Typically, association rules are discarded as uninteresting if they do not satisfy both a minimum support Fig. 5: cluster of nodes on social network Table 1. Frequency of appearing together and sharing same contents Context

1 2 3 4 5 6 7 8 9 10 11 12

Association Rules

Support% P(cond& result)

Confidence% P(cond&result)/p(cond)

M M P P C C M,P P,C C,M C M P

25 20 15 25 20 15 5 5 5 5 5 5

0.55 0.44 0.35 0.58 0.5 0.375 0.2 0.333 0.25 0.125 0.111 0.117

P C C M M P C M P P,C P,C C,M

The context information is continuously stored in the cloud and reused in the other context. Frequently generated data discovers the pattern which helps us in prediction. Frequent pattern occur frequently in data and a frequent content set typically refers to a combination of contents that frequently appear together on the social network. Frequent pattern mining becomes a source to discover remarkable associations and correlations within data. Unsupervised techniques as Association rule is applied at the second stage of proposed model. As it can apply on historical data, we got a small data set of social network related to election in America 2012 and find the cooccurrence matrix table by applying a data mining technique known as Market Basket Analysis and generate association rules to find confidence and support as shown in the table 1. This data set is based on a questionnaire. We assume that in a given context, three users C, M and P have participated in the communication session on a social network. The frequency of users appearing together or the frequencies of sharing same contents are shown in the table 1.

Improvement P(cond&result)/p(cond) x p(res) 1.30 1.11 0.882 1.30 1.11 0.882 0.5 0.740 0.588 0.5 0.740 0.588

threshold and a minimum confidence threshold. Additional analysis can be performed to uncover interesting statistical correlations between associated attribute-value pairs. C. Stage III knowledge extraction Users in the social communities play role as knowledge workers in the proposed model. Knowledge sharing mechanisms are used to extract new knowledge when different events occurs in specific contexts. Data sharing by online users varies time to time and data transmitted from one source could be information for other and generated knowledge in one context can be used as data in another context as shown in the figure 4. Frequency of effective data sharing depends on the trust and tie strength among individuals belong to a particular community. D. Communities Detection and Innovation At this stage, heterogeneous data from different sources may be combined or sorted using filtering techniques. Moreover, different communities are generated according to specified mechanisms. The stochastic block based models will be

used to detect or create communities from a perspective on node. In these types of models, each node is assigned to a block, group or community. Another possibility is to use probabilistic mixture based models which generate a network from a perspective on edge and assigns each given edge to one or more of blocks, groups or communities with a probability [14]. The members of each community will work as knowledge workers in our proposed framework and help in creating new knowledge by sharing previously applied knowledge. IV.

RESULTS

Figure 6 shows the frequency of shared contents in an online session. Three types of data are frequently shared and analyzed that most of the users distributed text data as (65%), images (25%) and videos (10%). New users are assigned cluster using K nearest neighbors (KNN) technique. Generated clusters are depicted in the figure 5. When a classification is to be made for a new user, its distance to each user in the community must be determined. Only the K closest individuals of a group are considered. The new user then assigned a class that contains more users from this set of K closest users as shown in the figure 5.

Fig. 6: Type of Frequently Shared Contents V. CONCLUSION The proposed model is a contribution towards knowledge extraction and management using the data shared by the online social network users and to generate online communities. After processing the data set, it is analyzed that frequency of useful data is shared among individuals or groups is high if they belong to the same organization, similar habits, field of interest, having same opinion about an issue or have blood relation. The future work will be the practical implementation of this model using self-acquired dataset and the concept of context focused crawler (CFC) would also be implemented. VII. REFERENCES [1] Julia Nieves ,Javier Osorio, The role of social networks in knowledge creation, Knowledge Management Research & Practice, 1–16, 2012. [2] Mirta Amalia and Yanuar Nugroho, An innovation perspective of knowledge management in a multinational subsidiary, Journal Of Knowledge Management, ISSN 1367-3270, VOL. 15 NO. 1, pp. 7187, 2011.

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