Iraklis Varlamis

SEWeP: using site semantics and a taxonomy to enhance the Web personalization process

Web personalization is the process of customizing a Web site to the needs of each specific user or set of users, taking advantage of the knowledge acquired through the analysis of the users navigational behavior. Integrating usage data with content, structure or user profile data enhances the results of the personalization process. In this paper, we present SEWeP, a system that makes use of both the usage logs and the semantics of a Web sites content in order to personalize it. Web content is semantically annotated using a conceptual hierarchy (taxonomy). We introduce C-logs, an extended form of Web usage logs that encapsulates knowledge derived from the link semantics. C-logs are used as input to the Web usage mining process, resulting in a broader yet semantically focused set of recommendations.

Bridging XML-schema and relational databases: a system for generating and manipulating relational databases using valid XML documents

Many organizations and enterprises establish distributed working environments, where different users need to exchange information based on a common model. XML is widely used to facilitate this information exchange. The extensibility of XML allows the creation of generic models that integrate data from different sources. For these tasks, several applications are used to import and export information in XML format from the data repositories. In order to support this process for relational repositories we developed the X-Database system. The base of this system is an XML-Schema file that describes the logical model of interchanged information. Initially, the system analyses the syntax of the XML-Schema file and generates the relational database. Then it handles the decomposition of valid XML files according to that Schema and the composition of XML documents from the information in the database. Finally the system offers a flexible mechanism for modifying and querying database contents using only valid XML documents, which are validated over the XML-Schema files rules.

Text relatedness based on a word thesaurus

The computation of relatedness between two fragments of text in an automated manner requires taking into account a wide range of factors pertaining to the meaning the two fragments convey, and the pairwise relations between their words. Without doubt, a measure of relatedness between text segments must take into account both the lexical and the semantic relatedness between words. Such a measure that captures well both aspects of text relatedness may help in many tasks, such as text retrieval, classification and clustering. In this paper we present a new approach for measuring the semantic relatedness between words based on their implicit semantic links. The approach exploits only a word thesaurus in order to devise implicit semantic links between words. Based on this approach, we introduce Omiotis, a new measure of semantic relatedness between texts which capitalizes on the word-to-word semantic relatedness measure (SR) and extends it to measure the relatedness between texts. We gradually validate our method: we first evaluate the performance of the semantic relatedness measure between individual words, covering word-to-word similarity and relatedness, synonym identification and word analogy; then, we proceed with evaluating the performance of our method in measuring text-to-text semantic relatedness in two tasks, namely sentence-to-sentence similarity and paraphrase recognition. Experimental evaluation shows that the proposed method outperforms every lexicon-based method of semantic relatedness in the selected tasks and the used data sets, and competes well against corpus-based and hybrid approaches.

BlogRank: ranking weblogs based on connectivity and similarity features

A large part of the hidden web resides in weblog servers; traditional search engines perform poorly on blogs. We present a method for ranking weblogs utilizing both link graph and similarity, and based on an enhanced and weighted graph of weblogs capturing crucial weblog features. Rankings are then assigned using our algorithm, BlogRank, which is a modified version of PageRank.To validate our method we ran experiments on a weblog dataset, processed and adapted to our search engine: http://spiderwave.aueb.gr/BlogwaveOur experiments suggest that our algorithm enhances the quality of returned results.

THESUS: Organizing Web document collections based on link semantics

Abstract.The requirements for effective search and management of the WWW are stronger than ever. Currently Web documents are classified based on their content not taking into account the fact that these documents are connected to each other by links. We claim that a page’s classification is enriched by the detection of its incoming links’ semantics. This would enable effective browsing and enhance the validity of search results in the WWW context. Another aspect that is underaddressed and strictly related to the tasks of browsing and searching is the similarity of documents at the semantic level. The above observations lead us to the adoption of a hierarchy of concepts (ontology) and a thesaurus to exploit links and provide a better characterization of Web documents. The enhancement of document characterization makes operations such as clustering and labeling very interesting. To this end, we devised a system called THESUS. The system deals with an initial sets of Web documents, extracts keywords from all pages’ incoming links, and converts them to semantics by mapping them to a domain’s ontology. Then a clustering algorithm is applied to discover groups of Web documents. The effectiveness of the clustering process is based on the use of a novel similarity measure between documents characterized by sets of terms. Web documents are organized into thematic subsets based on their semantics. The subsets are then labeled, thereby enabling easier management (browsing, searching, querying) of the Web. In this article, we detail the process of this system and give an experimental analysis of its results.

The Present and Future of Standards for E-Learning Technologies

This paper studies the e-learning technologies from the standardization aspect with a glimpse on future changes. Our aim is to thoroughly review the existing standards, the e-Learning process workflow and the market needs and trends and indicate the best path for achieving a global standard for e-learning activities. The generic model of e-learning is presented without emphasis on specific software and hardware solutions. We focus on the major necessities like reusability or interoperability of content and technologies and revise the current standards regarding these two aspects. The most popular infrastructure models are presented in details and the related committees and organizations involved in the standardization process are referenced. As an epilogue to this presentation we provide our insights for a global standard, which will cover all aspects of elearning and will be supported by all related organizations, vendors, institutions and individual educators. We illustrate the steps for the successful configuration and deployment of a globally accepted standard and display the merits of this approach.

A Trust-Aware System for Personalized User Recommendations in Social Networks

Social network analysis has recently gained a lot of interest because of the advent and the increasing popularity of social media, such as blogs, social networking applications, microblogging, or customer review sites. In this environment, trust is becoming an essential quality among user interactions and the recommendation for useful content and trustful users is crucial for all the members of the network. In this paper, we introduce a framework for handling trust in social networks, which is based on a reputation mechanism that captures the implicit and explicit connections between the network members, analyzes the semantics and dynamics of these connections, and provides personalized user recommendations to the network members.

A Study on Social Network Metrics and Their Application in Trust Networks

Social network analysis has recently gained a lot of interest because of the advent and the increasing popularity of social media, such as blogs, social networks, micro logging, or customer review sites. Such media often serve as platforms for information dissemination and product placement or promotion. In this environment, influence and trust are becoming essential qualities among user interactions. In this work, we perform an extensive study of various metrics related to the aforementioned elements, and their effect in the process of information propagation in the virtual world. In order to better understand the properties of links and the dynamics of social networks, we distinguish between permanent and transient links and in the latter case, we consider the link freshness. Moreover, we distinguish between local and global influence and compare suggestions provided by locally or globally trusted users.

An experimental study on unsupervised graph-based word sense disambiguation

Recent research works on unsupervised word sense disambiguation report an increase in performance, which reduces their handicap from the respective supervised approaches for the same task. Among the latest state of the art methods, those that use semantic graphs reported the best results. Such methods create a graph comprising the words to be disambiguated and their corresponding candidate senses. The graph is expanded by adding semantic edges and nodes from a thesaurus. The selection of the most appropriate sense per word occurrence is then made through the use of graph processing algorithms that offer a degree of importance among the graph vertices. In this paper we experimentally investigate the performance of such methods. We additionally evaluate a new method, which is based on a recently introduced algorithm for computing similarity between graph vertices, P-Rank. We evaluate the performance of all alternatives in two benchmark data sets, Senseval 2 and 3, using WordNet. The current study shows the differences in the performance of each method, when applied on the same semantic graph representation, and analyzes the pros and cons of each method for each part of speech separately. Furthermore, it analyzes the levels of inter-agreement in the sense selection level, giving further insight on how these methods could be employed in an unsupervised ensemble for word sense disambiguation.

THESUS, a closer view on Web content management enhanced with link semantics

With the unstoppable growth of the world wide Web, the great success of Web search engines, such as Google and AltaVista, users now turn to the Web whenever looking for information. However, many users are neophytes when it comes to computer science, yet they are often specialists of a certain domain. These users would like to add more semantics to guide their search through world wide Web material, whereas currently most search features are based on raw lexical content. We show how the use of the incoming links of a page can be used efficiently to classify a page in a concise manner. This enhances the browsing and querying of Web pages. We focus on the tools needed in order to manage the links and their semantics. We further process these links using a hierarchy of concepts, akin to an ontology, and a thesaurus. This work is demonstrated by an prototype system, called THESUS, that organizes thematic Web documents into semantic clusters. Our contributions are the following: 1) a model and language to exploit link semantics information, 2) the THESUS prototype system, 3) its innovative aspects and algorithms, more specifically, the novel similarity measure between Web documents applied to different clustering schemes (DB-Scan and COBWEB), and 4) a thorough experimental evaluation proving the value of our approach.