Panos Alexopoulos

IKARUS-Onto: a methodology to develop fuzzy ontologies from crisp ones

Fuzzy Ontologies comprise a relatively new knowledge representation paradigm that is being increasingly applied in application scenarios in which the treatment and utilization of vague or imprecise knowledge are important. However, the majority of research in the area has mostly focused on the development of conceptual formalisms for representing (and reasoning with) fuzzy ontologies, while the methodological issues entailed within the development process of such an ontology have been so far neglected. With that in mind, we present in this paper IKARUS-Onto, a comprehensive methodology for developing fuzzy ontologies from existing crisp ones that significantly enhances the effectiveness of the fuzzy ontology development process and the quality, in terms of accuracy, shareability and reusability, of the process’s output.

Facilitating dialogue - using semantic web technology for eparticipation

In this paper we describe the application of various Semantic Web technologies and their combination with emerging Web 2.0 use patterns in the eParticipation domain and show how they are used in an operational system for the Regional Government of the Prefecture of Samos, Greece. We present parts of the system that are based on Semantic Web technology and how they are merged with a Web 2.0 philosophy and explain the benefits of this approach, as showcased by applications for annotating, searching, browsing and cross-referencing content in eParticipation communities.

A Fuzzy Knowledge-based Decision Support System for Tender Call Evaluation

In the modern business environment, the capability of an enterprise to generate value from its business knowledge influences in an increasingly important way its competitiveness. Towards this direction, knowledge-based systems can be a very effective tool for enhancing the productivity of knowledge workers by providing them with advanced knowledge processing capabilities. In this paper we describe such a system which utilizes organizational and domain knowledge in order to support consultants in the process of evaluating calls for tender.

The PARLANCE mobile application for interactive search in English and Mandarin

We demonstrate a mobile application in English and Mandarin to test and evaluate components of the Parlance dialogue system for interactive search under real-world conditions.

A Metaontology for Annotating Ontology Entities with Vagueness Descriptions

The emergence in the last years of initiatives like the Linked Open Data LOD has led to a significant increase in the amount of structured semantic data on the Web. Central role to this development has been played by ontologies, as these enable the representation of real world domains in an explicit and formal way and, thus, the production of commonly understood and shareable semantic data. Nevertheless, the shareability and wider reuse of such data can be hampered by the existence of vagueness within it, as this makes the datas meaning less explicit. With that in mind, in this paper we present and evaluate the Vagueness Ontology, a metaontology that enables the explicit identification and description of vague entities and their vagueness-related characteristics in ontologies. The rationale is that such descriptions, when accompanying vague ontologies, may narrow the possible interpretations that the latters vague elements may assume by its users.

Exploiting ontological relations for automatic semantic tag recommendation

In this paper we propose a novel method for automatically generating and recommending semantic tags for text documents, namely terms that reflect the intended meaning of the document in an accurate and complete way. Our approach is based on the utilization of existing domain knowledge, in the form of ontologies, and particularly in the selection and exploitation of those ontological relations that are most appropriate for the given tagging scenario and domain. Experimental evaluation of the method with significant number of documents and high volume of ontological knowledge shows a high level of accuracy as far as tag identification is concerned.

Question Answering and Knowledge Graphs

In the Digital and Information Age, companies and government agencies are highly digitalized, as the information exchanges happening in their processes. They store information both as natural language text and structured data, e.g., relational databases or knowledge graphs. In this scenario, methods for organizing, finding, and selecting relevant information, beyond the capabilities of classic Information Retrieval, are always active topics of research and development.

Towards a Meta-Reasoning Framework for Reasoning about Vagueness in OWL Ontologies

When developing ontologies, knowledge engineers and domain experts often use predicates that are vague, i.e., predicates that lack clear applicability conditions and boundaries such as High, Expert or Bad. In previous works, we have shown how such predicates within ontologies can hamper the latters shareability and meaning explicitness and we have proposed Vagueness Ontology (VO), an OWL metaontology for representing vagueness-aware ontologies, i.e., ontologies whose (vague) elements are annotated by explicit descriptions of the nature and characteristics of their vagueness. A limitation of VO is that it does not model the way vagueness and its characteristics propagate when defining more complex OWL axioms (such as conjunctive classes), neither does it enforce any kind of vagueness-related consistency. For that, in this paper, we expand VO by means of formal inference rules and constraints that model the way vagueness descriptions of complex ontology elements can be automatically derived. More importantly, we enable the efficient execution of these rules by means of a novel meta-reasoning framework.

Answer Type Identification for Question Answering

Question Answering research has long recognised that the identification of the type of answer being requested is a fundamental step in the interpretation of a question as a whole. Previous strategies have ranged from trivial keyword matches, to statistical analyses, to well-defined algorithms based on shallow syntactic parses with user-interaction for ambiguity resolution. A novel strategy combining deep NLP on both syntactic and dependency parses with supervised learning is introduced and results that improve on extant alternatives reported. The impact of the strategy on QALD is also evaluated with a proprietary Question Answering system and its positive results analysed.

Towards Vagueness-Oriented Quality Assessment of Ontologies

Ontology evaluation has been recognized for a long time now as an important part of the ontology development lifecycle, and several methods, processes and metrics have been developed for that purpose. Nevertheless, vagueness is a quality dimension that has been neglected from most current approaches. Vagueness is a common human knowledge and linguistic phenomenon, typically manifested by terms and concepts that lack clear applicability conditions and boundaries such as high, expert, bad, near etc. As such, the existence of vague terminology in an ontology may hamper the latter’s quality, primarily in terms of shareability and meaning explicitness. With that in mind, in this short paper we argue for the need of including vagueness in the ontology evaluation activity and propose a set of metrics to be used towards that goal.