Efstratios Kontopoulos

Ontology-based sentiment analysis of twitter posts

The emergence of Web 2.0 has drastically altered the way users perceive the Internet, by improving information sharing, collaboration and interoperability. Micro-blogging is one of the most popular Web 2.0 applications and related services, like Twitter, have evolved into a practical means for sharing opinions on almost all aspects of everyday life. Consequently, micro-blogging web sites have since become rich data sources for opinion mining and sentiment analysis. Towards this direction, text-based sentiment classifiers often prove inefficient, since tweets typically do not consist of representative and syntactically consistent words, due to the imposed character limit. This paper proposes the deployment of original ontology-based techniques towards a more efficient sentiment analysis of Twitter posts. The novelty of the proposed approach is that posts are not simply characterized by a sentiment score, as is the case with machine learning-based classifiers, but instead receive a sentiment grade for each distinct notion in the post. Overall, our proposed architecture results in a more detailed analysis of post opinions regarding a specific topic.

An ontology-based planning system for e-course generation

Researchers in the area of educational software have always shown great interest in 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 learners profile, preferences, needs and abilities.

EMERALD: a multi-agent system for knowledge-based reasoning interoperability in the semantic web

The Semantic Web aims at augmenting the WWW with meaning, assisting people and machines in comprehending Web content and better satisfying their requests Intelligent agents are considered to be greatly favored by Semantic Web technologies, because of the interoperability the latter will achieve One of the main problems in agent interoperation is the great variety in reasoning formalisms, as agents do not necessarily share a common rule or logic formalism This paper reports on the implementation of EMERALD, a knowledge-based framework for interoperating intelligent agents in the Semantic Web More specifically, a multi-agent system was developed on top of JADE, featuring trusted, third party reasoning services, a reusable agent prototype for knowledge-customizable agent behavior, as well as a reputation mechanism for ensuring trust in the framework Finally, a use case scenario is presented that illustrates the viability of the proposed framework.

Semantic web technologies in pervasive computing

Pervasive and sensor-driven systems are by nature open and extensible, both in terms of input and tasks they are required to perform. Data streams coming from sensors are inherently noisy, imprecise and inaccurate, with differing sampling rates and complex correlations with each other. These characteristics pose a significant challenge for traditional approaches to storing, representing, exchanging, manipulating and programming with sensor data. Semantic Web technologies provide a uniform framework for capturing these properties. Offering powerful representation facilities and reasoning techniques, these technologies are rapidly gaining attention towards facing a range of issues such as data and knowledge modelling, querying, reasoning, service discovery, privacy and provenance. This article reviews the application of the Semantic Web to pervasive and sensor-driven systems with a focus on information modelling and reasoning along with streaming data and uncertainty handling. The strengths and weaknesses of current and projected approaches are analysed and a roadmap is derived for using the Semantic Web as a platform, on which open, standard-based, pervasive, adaptive and sensor-driven systems can be deployed.

Deploying defeasible logic rule bases for the semantic web

Logic is currently the target of the majority of the upcoming efforts towards the realization of the Semantic Web vision, namely making the content of the Web accessible not only to humans, as it is today, but to machines as well. Defeasible reasoning, a rule-based approach to reasoning with incomplete and conflicting information, is a powerful tool in many Semantic Web applications. Despite its strong mathematical background, logic, in general, and defeasible logic, in particular, may overload the user with tons of additional complex semantic relationships among data and metadata of the Semantic Web. To this end, a comprehensible, visual representation of these semantic relationships (rules) would help users understand them and make more use of them. This paper presents VDR-DEVICE, a defeasible reasoning system, designed specifically for the Semantic Web environment. VDR-DEVICE is an integrated development environment for deploying and visualizing defeasible logic rule bases on top of RDF Schema ontologies. The system consists of a number of sub-components, which, though developed autonomously, are combined efficiently, forming a flexible framework. The system employs a defeasible reasoning system that supports direct importing and processing of RDF data and RDF Schema ontologies as well as a number of user-friendly rule base and ontology visualization modules.

A Modal Defeasible Reasoner of Deontic Logic for the Semantic Web

Defeasible logic is a non-monotonic formalism that deals with incomplete and conflicting information, whereas modal logic deals with the concepts of necessity and possibility. These types of logics play a significant role in the emerging Semantic Web, which enriches the available Web information with meaning, leading to better cooperation between end-users and applications. Defeasible and modal logics, in general, and, particularly, deontic logic provide means for modeling agent communities, where each agent is characterized by its cognitive profile and normative system, as well as policies, which define privacy requirements, access permissions, and individual rights. Toward this direction, this article discusses the extension of DR-DEVICE, a Semantic Web-aware defeasible reasoner, with a mechanism for expressing modal logic operators, while testing the implementation via deontic logic operators, concerned with obligations, permissions, and related concepts. The motivation behind this work is to develop a practical defeasible reasoner for the Semantic Web that takes advantage of the expressive power offered by modal logics, accompanied by the flexibility to define diverse agent behaviours. A further incentive is to study the various motivational notions of deontic logic and discuss the cognitive state of agents, as well as the interactions among them.

A visual environment for developing defeasible rule bases for the semantic web

Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and inconsistent information. Such reasoning is useful for many applications in the Semantic Web. However, the RuleML syntax of defeasible logic may appear too complex for many users. Furthermore, the interplay between various technologies and languages, such as defeasible reasoning, RuleML, and RDF impose a demand for using multiple, diverse tools for building rule-based applications for the Semantic Web. In this paper we present VDR-Device, a visual integrated development environment for developing and using defeasible logic rule bases on top of RDF ontologies. VDR-Device integrates in a user-friendly graphical shell, a visual RuleML-compliant rule editor that constrains the allowed vocabulary through analysis of the input RDF ontologies and a defeasible reasoning system that processes RDF data and RDF Schema ontologies.

Rule-based approaches for energy savings in an ambient intelligence environment

Abstract This paper presents a novel real-world application for energy savings in a Smart Building environment. The proposed system unifies heterogeneous wireless sensor networks under a Semantic Web Service middleware. Two complementary and mutually exclusive rule-based approaches for enforcing energy-saving policies are proposed: a reactive agent based on production rules and a deliberative agent based on defeasible logic. The system was deployed at a Greek University, showing promising experimental results (at least 4% daily savings). Although the percentage of energy savings may seem low, the greatest merit of the method is ensuring that no energy is wasted by constantly enforcing the policies.

Visualizing defeasible logic rules for the semantic web

Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and conflicting information Such reasoning is useful in many Semantic Web applications, like policies, business rules, brokering, bargaining and agent negotiations Nevertheless, defeasible logic is based on solid mathematical formulations and is, thus, not fully comprehensible by end users, who often need graphical trace and explanation mechanisms for the derived conclusions Directed graphs can assist in confronting this drawback They are a powerful and flexible tool of information visualization, offering a convenient and comprehensible way of representing relationships between entities Their applicability, however, is balanced by the fact that it is difficult to associate data of a variety of types with the nodes and the arcs in the graph In this paper we try to utilize digraphs in the graphical representation of defeasible rules, by exploiting the expressiveness and comprehensibility they offer, but also trying to leverage their major disadvantage, by defining two distinct node types, for rules and atomic formulas, and four distinct connection types for each rule type in defeasible logic and for superiority relationships The paper also briefly presents a tool that implements this representation methodology.

Towards a Knowledge-Based Framework for Agents Interacting in the Semantic Web

The Semantic Web aims at making Web content understandable both for people and machines. Although intelligent agents can assist towards this vision, they do not have to conform to a common rule or logic paradigm. This paper reports on the first steps towards a framework for interoperating knowledge-based intelligent agents. A multi-agent system was extended with defeasible reasoning and a reusable agent model is proposed for customizable agents, equipped with a knowledge base and a Jess rule engine. Two use case scenarios display the integration of these technologies.