CG-PerLS: Conceptual Graphs for Personalized Learning Systems

Two of the most important standardization efforts for e-learning technologies are related to the definition of metadata describing educational resources and metadata describing the learner's profile. The internal details of systems that utilize these metadata is still an open issue since these efforts are primarily dealing with "what " and not "how". Under the light of these emerging efforts, we present CG-PerLS, a knowledge based approach for organizing and accessing educational resources. CG-PerLS is a model of a WWW portal for learning objects that encodes the learning technologies metadata in the Conceptual Graph knowledge representation formalism, and uses related inference techniques to provide advanced, personalized functionality. CG-PerLS allows learning resource creators to manifest their material, client-side learners to access these resources in a way tailored to their individual profile and educational needs, and dynamic course generation based on fine or coarse grained educational resources

COMFRESH: a common framework for expert systems and hypertext

Abstract--Intelligent hypertext is a promising approach to information systems, because it combines the power of inference of expert systems and the intuitive power of hypertext. In this paper we propose the "COMFRESH", a common framework for expert systems and hypertext. It is based on a Prolog interpreter and uses the conceptual graph knowledge representation formalism for browsing and reasoning. COMFRESH can be used as a knowledge based hypertext (intelligent hypertext) or as an expert system with hypertext capabilities

Mining domain-specific dictionaries of opinion words

The task of opinion mining has attracted interest during the last years. This is mainly due to the vast availability and value of opinions on-line and the easy access of data through conventional or intelligent crawlers. In order to utilize this information, algorithms make extensive use of word sets with known polarity. This approach is known as dictionary-based sentiment analysis. Such dictionaries are available for the English language. Unfortunately, this is not the case for other languages with smaller user bases. Moreover, such generic dictionaries are not suitable for specific domains. Domain-specific dictionaries are crucial for domain-specific sentiment analysis tasks. In this paper we alleviate the above issues by proposing an approach for domain-specific dictionary building. We evaluate our approach on a sentiment analysis task. Experiments on user reviews on digital devices demonstrate the utility of the proposed approach. In addition, we present NiosTo, a software that enables dictionary extraction and sentiment analysis on a given corpus. © Springer International Publishing Switzerland 2014

Learning patterns for discovering domain-oriented opinion words

Sentiment analysis is a challenging task that attracted increasing interest during the last years. The availability of online data along with the business interest to keep up with consumer feedback generates a constant demand for online analysis of user-generated content. A key role to this task plays the utilization of domain-specific lexicons of opinion words that enables algorithms to classify short snippets of text into sentiment classes (positive, negative). This process is known as dictionary-based sentiment analysis. The related work tends to solve this lexicon identification problem by either exploiting a corpus and a thesaurus or by manually defining a set of patterns that will extract opinion words. In this work, we propose an unsupervised approach for discovering patterns that will extract domain-specific dictionary. Our approach (DidaxTo) utilizes opinion modifiers, sentiment consistency theories, polarity assignment graphs and pattern similarity metrics. The outcome is compared against lexicons extracted by the state-of-the-art approaches on a sentiment analysis task. Experiments on user reviews coming from a diverse set of products demonstrate the utility of the proposed method. An implementation of the proposed approach in an easy to use application for extracting opinion words from any domain and evaluate their quality is also presented. © 2017, Springer-Verlag London

Manuscript An Ontology-based Planning System for e-Course Generation

Researchers in the area of educational software have always shown a great deal of interest for the automatic synthesis of learning curricula. During the recent years, with the extensive use of metadata and the emergence of the Semantic Web, this vision is gradually turning into a reality. A number of systems for curricula synthesis have been proposed. These systems are based on strong relations defined in the metadata of learning objects, which allow them to be combined with other learning objects, in order to form a complete educational program. This article presents PASER, a system for automatically synthesizing curricula using AI Planning and Semantic Web technologies. The use of classical planning techniques allows the system to dynamically construct learning paths even from disjoint learning objects, meeting the learner’s profile, preferences, needs and abilities. 2