The Researcher Social Network: a social network based on metadata of scientific publications

Scientific journals can capture a scholar’s research career. A researcher’s publication data often reflects his/her research interests and their social relations. It is demonstrated that scientist collaboration networks can be constructed based on co-authorship data from journal papers. The problem with such a network is that researchers are limited within their professional social network. This work proposes the idea of constructing a researcher’s social network based on data harvested from metadata of scientific publications and personal online profiles. We hypothesize that data, such as, publication keywords, personal interests, the themes of the conferences where papers are published, and co-authors of the papers, either directly or indirectly represent the authors’ research interests, and by measuring the similarity between these data we are able to construct a researcher social network. Based on the four types of data mentioned above, social network graphs were plotted, studied and analyzed. These graphs were then evaluated by the researchers themselves by giving ratings. Based on this evaluation, we estimated the weight for each type of data, in order to blend all data together to construct one ideal researcher’s social network. Interestingly, our results showed that a graph based on publication’s keywords were more representative than the one based on publication’s co-authorship. The findings from the evaluation were used to propose a dynamic social network data model

Understanding the Semantics of Ambiguous Tags in Folksonomies

The use of tags to describe Web resources in a collaborative manner has experienced rising popularity among Web users in recent years. The product of such activity is given the name folksonomy, which can be considered as a scheme of organizing information in the users' own way. In this paper, we present a possible way to analyze the tripartite graphs - graphs involving users, tags and resources - of folksonomies and discuss how these elements acquire their meanings through their associations with other elements, a process we call mutual contextualization. In particular, we demonstrate how different meanings of ambiguous tags can be discovered through such analysis of the tripartite graph by studying the tag sf. We also discuss how the result can be used as a basis to better understand the nature of folksonomies

Tag Meaning Disambiguation through Analysis of Tripartite Structure of Folksonomies

Collaborative tagging systems are becoming very popular recently. Web users use freely-chosen tags to describe shared resources, resulting in a folksonomy. One problem of folksonomies is that tags which appear in the same form may carry multiple meanings and represent different concepts. As this kind of tags are ambiguous, the precisions in both description and retrieval of the shared resources are reduced. We attempt to develop effective methods to disambiguate tags by studying the tripartite structure of folksonomies. This paper describes the network analysis techniques that we employ to discover clusters of nodes in networks and the algorithm for tag disambiguation. Experiments show that the method is very effective in performing the task

Distributed human computation framework for linked data co-reference resolution

Distributed Human Computation (DHC) is a technique used to solve computational problems by incorporating the collaborative effort of a large number of humans. It is also a solution to AI-complete problems such as natural language processing. The Semantic Web with its root in AI is envisioned to be a decentralised world-wide information space for sharing machine-readable data with minimal integration costs. There are many research problems in the Semantic Web that are considered as AI-complete problems. An example is co-reference resolution, which involves determining whether different URIs refer to the same entity. This is considered to be a significant hurdle to overcome in the realisation of large-scale Semantic Web applications. In this paper, we propose a framework for building a DHC system on top of the Linked Data Cloud to solve various computational problems. To demonstrate the concept, we are focusing on handling the co-reference resolution in the Semantic Web when integrating distributed datasets. The traditional way to solve this problem is to design machine-learning algorithms. However, they are often computationally expensive, error-prone and do not scale. We designed a DHC system named iamResearcher, which solves the scientific publication author identity co-reference problem when integrating distributed bibliographic datasets. In our system, we aggregated 6 million bibliographic data from various publication repositories. Users can sign up to the system to audit and align their own publications, thus solving the co-reference problem in a distributed manner. The aggregated results are published to the Linked Data Cloud

From user behaviours to collective semantics

The World Wide Web has developed into an important platform for social interactions with the rise of social networking applications of different kinds. Collaborative tagging systems, as prominent examples of these applications, allow users to share their resources and to interact with each other. By assigning tags to resources on the Web in a collaborative manner, users contribute to the emergence of complex networks now commonly known as folksonomies, in which users, documents and tags are interconnected with each other. To reveal the implicit semantics of entities involved in a folksonomy, one requires an understanding of the characteristics of the collective behaviours that create these interconnections. This thesis studies how user behaviours in collaborative tagging systems can be analysed to acquire a better understanding of the collective semantics of entities in folksonomies. We approach this problem from three different but closely related perspectives. Firstly, we study how tags are used by users and how their different intended meanings can be identified. Secondly, we develop a method for assessing the expertise of users and quality of documents in folksonomies by introducing the notion of implicit endorsement. Finally, we study the relations between documents induced from collaborative tagging and compare them with existing hyperlinks between Web documents. We show that, in each of these scenarios, it is crucial to consider the collective behaviours of the users and the social contexts in order to understand the characteristics of the entities. This project can be considered as a case study of the Social Web, the research outcomes of which can be easily generalised to many other social networking applications. It also fits into the larger framework for understanding the Web set out by the emerging interdisciplinary field of Web Science, as the work involves analyses of the interactions and behaviour of Web users in order to understand how we can improve existing systems and facilitate information sharing and retrieval on the Web

A k-Nearest-Neighbour Method for Classifying Web Search Results with Data in Folksonomies

Traditional Web search engines mostly adopt a keyword-based approach. When the keyword submitted by the user is ambiguous, search result usually consists of documents related to various meanings of the keyword, while the user is probably interested in only one of them. In this paper we attempt to provide a solution to this problem using a k-nearest-neighbour approach to classify documents returned by a search engine, by building classifiers using data collected from collaborative tagging systems. Experiments on search results returned by Google show that our method is able to classify the documents returned with high precision

A Study of User Profile Generation from Folksonomies

Recommendation systems which aim at providing relevant information to users are becoming more and more important and desirable due to the enormous amount of information available on the Web. Crucial to the performance of a recommendation system is the accuracy of the user profiles used to represent the interests of the users. In recent years, popular collaborative tagging systems such as del.icio.us have aggregated an abundant amount of user-contributed metadata which provides valuable information about the interests of the users. In this paper, we present our analysis on the personal data in folksonomies, and investigate how accurate user profiles can be generated from this data. We reveal that the majority of users possess multiple interests, and propose an algorithm to generate user profiles which can accurately represent these multiple interests. We also discuss how these user profiles can be used for recommending Web pages and organising personal data

User-induced Links in Collaborative Tagging Systems

Collaborative tagging systems allow users to use tags to describe their favourite online documents. Two documents that are maintained in the collection of the same user and/or assigned similar sets of tags can be considered as related from the perspective of the user, even though they may not be connected by hyperlinks. We call this kind of implicit relations user-induced links between documents. We consider two methods of identifying user-induced links in collaborative tagging, and compare these links with existing hyperlinks on the Web. Our analyses show that user-induced links have great potentials to enrich the existing link structure of the Web. We also propose to use these links as a basis for predicting how documents would be tagged. Our experiments show that they achieve much higher accuracy than existing hyperlinks. This study suggests that by studying the collective behaviour of users we are able to enhance navigation and organisation of Web documents