George Bokos

Evaluating authoritative sources using social networks: an insight from Wikipedia

– The purpose of this paper is to present an approach to evaluating contributions in collaborative authoring environments, and in particular, Wikis using social network measures., – A social network model for Wikipedia has been constructed, and metrics of importance such as centrality have been defined. Data has been gathered from articles belonging to the same topic using a web crawler, in order to evaluate the outcome of the social network measures in the articles., – Finds that the question of the reliability regarding Wikipedia content is a challenging one and as Wikipedia grows, the problem becomes more demanding, especially for topics with controversial views such as politics or history., – It is believed that the approach presented here could be used to improve the authoritativeness of content found in Wikipedia and similar sources., – This work tries to develop a network approach to the evaluation of Wiki contributions, and approaches the problem of quality Wikipedia content from a social network point of view.

Disaster management approaches for academic libraries: an issue not to be neglected in Greece

Purpose – Although issues on disaster prevention have extensively been studied in the literature and have been embedded in everyday library practices all over the world, the vast majority of Greek libraries have not developed any specific measures. This paper seeks to review several disaster management approaches for academic libraries and to make suggestions for Greek academic libraries by analyzing the results of a nationwide survey.Design/methodology/approach – The literature regarding disaster management approaches for academic libraries is reviewed and accompanied by a survey conducted in July 2010 in order to study the level of risk and disaster preparedness in Greek academic libraries.Findings – In Greece, disaster management within academic libraries seems to be dealt with inefficiently, if not completely neglected. The fearsome economic crisis is further degrading the level of disaster preparedness due to a number of side effects, including the lack of personnel and equipment maintenance activiti...

Specific Selection of FFT Amplitudes from Audio Sports and News Broadcasting for Classification Purposes

In this paper we investigate the problem of classification between sports and news broadcasting. We detect and classify files that consist of speech and music or background noise (news broadcasting), and speech and a noisy background (sports broadcasting). More specifically, this study investigates feature extraction and training and classification procedures. We compare the Average Magnitude Difference Function (AMDF) method, which we consider more robust to background noise, with a novel proposed method. This method uses several spectral audio features which may be considered as specific semantic information. We base the extraction of these features on the theory of computational geometry using an Onion Algorithm (OA). We tested the classification procedure as well as the learning ability of the two methods using a Learning Vector Quantizer One (LVQ1) neural network. The results of the experiment showed that the OA method has a faster learning procedure, which we characterise as an accurate feature extraction method for several audio cases.

Towards Constructing a Bibliometric Cited Distance Factor for an Article Using an Elman Neural Network

In this paper we attempt to construct an ideal factor for the evaluation of an article, which combines objectivity with the ability to bridge many different bibliometric factors. The aim of this study is to determine a standard threshold value with which an independent, self-organizing system can determine the correlation between the value and the citation score of an article. This factor is called Cited Distance and is extracted via a well-fitted recurrent neural network. We use articles from the 10 cell biology journals in the Web of Science database with the highest impact factors for the years 1995–2007.

Social metadata for the impact factor

– The purpose of this research is to address the need for a definition of metadata descriptors for use in enhancing the accuracy of bibliometric instruments of scholarly evaluation, such as the impact factor., – A semantic vocabulary – COAP – is constructed, deployed on top of the Resource Description Framework (RDF), by extending the Friend‐of‐a‐Friend (FOAF) schema., – An extension of the FOAF vocabulary is considered as the ability to describe a publication record such as this paper in terms of scholar contributions and participations. In order to achieve that, the FOAF vocabulary is extended., – The application of this semantic vocabulary could be used as a way of enhancing the accuracy of source data for bibliometric evaluation instruments., – The paper discusses how metadata descriptors can contribute to the improvement of already established scholar evaluation instruments such as the impact factor. It will be of use in the development of digital libraries.

Semantic classification of the citations

In this study the semantic classification of the references/citations of a scientific article according to position within the article is investigated. For this purpose, the article is divided into two major sections: the Introduction/Background section and the rest section which contains the methodology, experimental part, results and conclusion parts. Additionally, the references of an article are divided into two categories: the Self-References and the Citations which are used for the semantic interpretation of the references in combination with the aforementioned geographic partition. For achievement of this, an algorithm was constructed which was implemented using Java programming language and was applied in a numerous articles of open springer journals. Finally, the classification results as well as the interpretation of these should create a new consideration about the contribution of each reference in the knowledge creation specifically in the self-citation case.

A methodology for binary encoding of citation metadata

In this paper we describe a methodology for binary encoding of citation metadata which targets the problems of versioning generated from the function of the web agents and/or crawlers on which Google Scholar and Citeseer systems operate. Typically citation indexes require a period of time in order to index the cited articles and update their counters, which is a contingency to allow processing of information. Furthermore, the storage of this information is based on text form, which requires large storage space. The proposed method is an online system based on the existence of a central relational database (in binary format) and the existence of two automated procedures, which carry out the acts of publishing and citation. In particular a detached authority provides a database which stores in the proposed binary coded format all the citations that a new article makes at the time that it is published. The communication between the database and the users is enabled by the use of XML files which can be stored in memory, making computation easier.