Semantic Web mining for Personalized Public Services

Abstract

As citizens are confronted with increasing volumes of information, boundless choices and endless opportunities in the Web environment, the need for personalized public e-services is more compulsory than ever. This chapter explores the way Semantic Web Mining technologies can be incorporated into public e-services domain in order to better meet citizens and authorities requirements. It describes the various steps of personalization process and examines techniques in use today to support it. In sequence, it introduces a recommendation scenario for an e-city portal. Finally, the chapter illustrates current trends in the field suggesting directions that may lead to new scientific results in the area.

Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions

“…Association rules for classification applied to e-learning (Castro et al 2007) have been investigated in the areas of learning recommendation systems (Chu et al 2003;Zaiane 2002). For example: learning material organization (Tsai et al 2006), learner learning assessments (Hwang et al 2003;Kumar 2005;Matsui and Okamoto 2003;Resende and Pires 2001), course adaptation to the learners' behavior (Hsu et al 2003;Markellou et al 2005;Muñoz-Merino et al 2015), and evaluation of educational web sites (Dos Santos and Becker 2003) Wang (2002) develop a portfolio analysis tool based on associative material clusters and sequences among them. This knowledge allows teachers to study the dynamic browsing structure and to identify interesting or unexpected learning patterns.…”

“…• building recommender agents for on-line learning activities or shortcuts (Zaiane 2002), • automatically leading the learner's activities and intelligently recommend on-line learning activities or shortcuts in the course web site to the learners (Lu 2004), • identifying attributes of performance inconsistency between various groups of learners (Minaei-Bidgoli et al 2004), • discovering interesting learner's usage information in order to provide feedback to course author (Romero et al 2004), • finding out the relation among the learning materials from a large amount of material data (Yu et al 2001), • finding learners' mistakes that are often occur together (Merceron and Yacef 2004), • optimizing the content of an e-learning portal by determining the content of most interest to the learner (Ramli 2005), • deriving useful patterns to help educators and instructors evaluating and interpreting on-line course activities (Zaiane 2002), and • personalizing e-learning based on comprehensive usage profiles and a domain ontology (Markellou et al 2005).…”

“…In Matsui and Okamoto (2003) it can be found the description of a learning process assessment method that resorts to association rules, and the well-known ID3 DT learning method. A framework for the employ of Web usage mining to support the validation of learning site designs was defined in Dos Santos and Becker (2003), applying association and sequence techniques (Srivastava et al 2000) Markellou et al (2005) presented a framework for personalized e-learning based on aggregate usage profiles, domain ontology, and a combination of Semantic Web and Web mining methods. The Apriori algorithm for association rules was applied to capture relations among URL references based on the navigational patterns of learners.…”

“…Contrast rules help one to identify attributes characterizing patterns of performance difference between various groups of learners. Markellou et al (2005) propose an ontology-based framework and discover association rules, using the Apriori algorithm. The role of the ontology is to determine which learning materials are more suitable for recommending to the learner.…”

Data-Driven Personalized E-Government Services: Literature Review and Case Study

“…In a broad sense, personalization is understood as the process of selecting suitable electronic services for a citizen on any criteria related to the citizen at a given time. It can affect different sets of the government web portal functionality e.g., prefiltered search results, tailored recommendations, banners to match a person's interests, or links to other related to the topic of interest sites [11]. It is one of the most promising solutions that can be implemented in state electronic portals due to its' ability to establish a dialogue between citizens and the state.…”