Nick Bassiliades

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.

A Defeasible Logic Reasoner for the Semantic Web

Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and inconsistent information. Such reasoning is, among others, useful for ontology integration, where conflicting information arises naturally; and for the modeling of business rules and policies, where rules with exceptions are often used. This paper describes these scenarios in more detail, and reports on the implementation of a system for defeasible reasoning on the Web. The system is called DR-DEVICE and is capable of reasoning about RDF metadata over multiple Web sources using defeasible logic rules. The system is implemented on top of CLIPS production rule system and builds upon R-DEVICE, an earlier deductive rule system over RDF metadata that also supports derived attribute and aggregate attribute rules. Rules can be expressed either in a native CLIPS-like language, or in an extension of the OO-RuleML syntax. The operational semantics of defeasible logic are implemented through compilation into the generic rule language of R-DEVICE. The paper also briefly presents a semantic web broker example for apartment renting.

An Integrated Approach to Automated Semantic Web Service Composition through Planning

The paper presents an integrated approach for automated semantic web service composition using AI planning techniques. An important advantage of this approach is that the composition process, as well as the discovery of the atomic services that take part in the composition, are significantly facilitated by the incorporation of semantic information. OWL-S web service descriptions are transformed into a planning problem described in a standardized fashion using PDDL, while semantic information is used for the enhancement of the composition process as well as for approximating the optimal composite service when exact solutions are not found. Solving, visualization, manipulation, and evaluation of the produced composite services are accomplished, while, unlike other systems, independence from specific planners is maintained. Implementation was performed through the development and integration of two software systems, namely PORSCE II and VLEPPO. PORSCE II is responsible for the transformation process, semantic enhancement, and management of the results. VLEPPO is a general-purpose planning system used to automatically acquire solutions for the problem by invoking external planners. A case study is also presented to demonstrate the functionality, performance, and potential of the approach.

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.

DR-NEGOTIATE - A system for automated agent negotiation with defeasible logic-based strategies

This paper reports on a system for automated agent negotiation. It uses the JADE agent framework, and its major distinctive feature is the use of declarative negotiation strategies. The negotiation strategies are expressed in a declarative rules language, defeasible logic and are applied using the implemented defeasible reasoning system DR-DEVICE. The choice of defeasible logic is justified. The overall system architecture is described, and a particular negotiation case is presented in detail.

CoLan: a functional constraint language and its implementation

Abstract This paper is about the definition of CoLan, a high-level declarative Constraint Description Language, for use with an Object-Oriented Database (OODB). CoLan has features of both first-order logic and functional programming and is based on Daplex. CoLan expressions are translated into Prolog code that implements the operational semantics of the constraint. Pieces of generated code are cached inside the class descriptor of the ‘host’ class attached to appropriate slots. The pieces of code are retrieved along an inheritance path when an update on the database is attempted. If the update violates any of the retrieved constraints then it is rejected with an informative message. Thus constraints are expressed declaratively and they can even be retracted individually. However, they are implemented efficiently as code-generated methods, triggered selectively by an update. The implementation is described for the ADAM OODB, which uses meta-classes of the CoLan system to generate class descriptions.

DR-DEVICE: A Defeasible Logic System for the Semantic Web

This paper presents DR-DEVICE, a system for defeasible reasoning on the Web. Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and inconsistent information. Such reasoning is, among others, useful for ontology integration, where conflicting information arises naturally; and for the modeling of business rules and policies, where rules with exceptions are often used. In this paper we describe these scenarios in more detail along with the implementation of the DR-DEVICE system, which is capable of reasoning about RDF data over multiple Web sources using defeasible logic rules. The system is implemented on top of CLIPS production rule system and builds upon R-DEVICE, an earlier deductive rule system over RDF data that also supports derived attribute and aggregate attribute rules. Rules can be expressed either in a native CLIPS-like language, or in an extension of the OO-RuleML syntax. The operational semantics of defeasible logic are implemented through compilation into the generic rule language of R-DEVICE. The paper includes a use case of a semantic web broker that reasons defeasibly about renting apartments based on buyer’s requirements (expressed RuleML defeasible logic rules) and seller’s advertisements (expressed in RDF).

A Rule-Based Object-Oriented OWL Reasoner

In this paper, we describe O-DEVICE, a memory-based knowledge-based system for reasoning and querying OWL ontologies by implementing RDF/OWL entailments in the form of production rules in order to apply the formal semantics of the language. Our approach is based on a transformation procedure of OWL ontologies into an object-oriented schema and the application of inference production rules over the generated objects in order to implement the various semantics of OWL. In order to enhance the performance of the system, we introduce a dynamic approach of generating production rules for ABOX reasoning and an incremental approach of loading ontologies. O-DEVICE is built over the CLIPS production rule system, using the object-oriented language COOL to model and handle ontology concepts and RDF resources. One of the contributions of our work is that we enable a well-known and efficient production rule system to handle OWL ontologies. We argue that although native OWL rule reasoners may process ontology information faster, they lack some of the key features that rule systems offer, such as the efficient manipulation of the information through complex rule programs. We present a comparison of our system with other OWL reasoners, showing that O-DEVICE can constitute a practical rule environment for ontology manipulation.

A process-oriented ontology-based knowledge management system for facilitating operational procedures in public administration

Public organizations produce daily a great volume of administrative documents, in order to fulfill their mission. This requires the use of a certain, unique for each procedure, legal framework. This article adopts a process oriented approach, through a web-based knowledge management system that provides this legal framework in an up-to-date and accurate manner. The system also supports the interpretation of the legal framework, supplying civil servants, citizens and businesses with precedents and opinions. The system employs an ontology in OWL for representing the public administration structure and any kind of document that flows among administrative units, during the execution of the procedures, which are mapped into OWL-S service models.

An adaptive personalized news dissemination system

With the explosive growth of the Word Wide Web, information overload became a crucial concern. In a data-rich information-poor environment like the Web, the discrimination of useful or desirable information out of tons of mostly worthless data became a tedious task. The role of Machine Learning in tackling this problem is thoroughly discussed in the literature, but few systems are available for public use. In this work, we bridge theory to practice, by implementing a web-based news reader enhanced with a specifically designed machine learning framework for dynamic content personalization. This way, we get the chance to examine applicability and implementation issues and discuss the effectiveness of machine learning methods for the classification of real-world text streams. The main features of our system named PersoNews are: (a) the aggregation of many different news sources that offer an RSS version of their content, (b) incremental filtering, offering dynamic personalization of the content not only per user but also per each feed a user is subscribed to, and (c) the ability for every user to watch a more abstracted topic of interest by filtering through a taxonomy of topics. PersoNews is freely available for public use on the WWW (http://news.csd.auth.gr).