Research Objectives


Ontologies:

Research Themes:

  • Engineering of ontologies: We emphasize on human-centered and collaborative O.E methodologies and integrated environments that support them.
  • Coordination of ontologies: We aim to the development of methods and frameworks for automateing the alignment, mapping, merging of domain ontologies using all types of ontological information i.e. lexical, structural, semantic.
  • Ontology Learning: This is a major theme concerning the automatic learning of ontologies from textual sources (corpora, user tags, etc)

Applications:

  • Semantic Search in:
    • Heterogeneous documents’ repositories (retrieve documents from heterogeneous sources using ontology mapping and NLP techniques)
    • Digital Libraries (ontology-based annotation of multimedia content, ontology learning from multimedia content)
    • Peer-to-peer systems via the emergency of semantics (using ontologies to strengthen the search capabilities of peers via semantic information processing)
  • Semantic Grid (applying ontology matching techniques for the retrieval of resources and the semantic matchmaking of web services in Grid/Open Cloud environments)
  • Semantic Wikis and Portals for Collaboration in open settings (e.g. for the collaborative engineering of ontologies)
  • Semantic Portal (use semantic web technologies for the development of Web portals that make use of the semantic capabilities of ontology-based annotated content)
  • Ontology-based Documents’ Summarization (Evaluating and producing summaries with the use of ontologies)
  • Taming Information and Knowledge

Agents and Multi-Agent Systems:

Research Themes:

  • Mental view of plan management for collaborative agents: This research aims to a specific framework for building collaborative cognitive agents with advanced plan management abilities to adjust their behavior to the requirements and opportunities of the changing world.
  • Adaptive agents’ organizations: The formation and adaptation of agents’ organizational elements is a major theme in the MAS community. Agents need to form organizations regarding specific needs and adjust these organizations when these needs change. Towards this goal we investigate the ad-hoc formation of organizations in constrained settings, and the organized adaptation of these organizations.
  • Information Sharing in MAS settings: Information needs to reach the “right” agents, and agents need the “proper” information to fulfill their tasks successfully. Towards these targets we investigate the effective sharing of information in large and dynamic settings of heterogeneous agents.


Applications:

  • Agents in Ambient Intelligent Settings and Human Organizations: Helping humans to achieve their goals.
  • Intelligent Interface Agents (in Social Settings, in E-Commerce, in e-Learning)
  • Grid and Cloud Computing (Forming adaptive agent organizations for achieving specific tasks with respect to certain constraints)
  • Agent Based P2P Systems
  • Intelligent Assistants in Human Organizations
  • Cognitive Robotics

Text Mining:

Research Themes:

  • Text categorization and clustering methods. We build algorithms and tools for the identification of stylistically homogeneous categories.
  • Writing style representation. We study the effectiveness of several features that capture stylistic properties of documents including low-level features like character n-grams and more elaborated features using the output of natural language processing tools.
  • Intelligent information retrieval. We are interested in measuring document similarity based on stylistic criteria and identifying parts of a single document with separate writing styles.

Applications:

  • Authorship attribution
  • Text and webpage genre detection
  • Plagiarism analysis

Data Mining:

Research Themes:

  • Bayesian networks: We emphasize on reasoning under conditions of uncertainty in complex, changing environments.
  • Text Mining: We endeavor to use ontologies with standard linear algebra and pattern recognition techniques to reveal significant text patterns.
  • Inforainment Data Mining: We focus on creating intelligent, interactive games and other entertaining environments by analysing user behavior during gameplay activity,.
  • Privacy-Preserving data mining: We focus on keeping sensitive information hidden from mining tasks without deteriorating the mining performance.
  • High-scale, parallel data mining: We emphasize on dealing with high-dimensionality problems in an effective and efficient manner.

Applications:

  • Modeling of intelligent sensor networks.
  • Modeling of multimedia databases in marketing applications.
  • Prediction of Financial Markets by incorporating stock indices with financial news.
  • Creation of artificial intelligence engines within action video games.
  • Privacy-Preserving Classification of horizontally and vertically partitioned datasets.
  • Nvidia CUDA based Radial Basis Function classification algorithm for large databases.

Image Processing and Computer Vision:

Research Themes:

  • Interactive Systems. We build tools appropriate to improve the image processing performance by using human feedback.
  • Historical Document Processing. We use intelligent systems in combination with classical document processing techniques intending to face common problems of historical in order to extract useful information from them.
  • Word Spotting. We investigate in the field of word spotting by the use of image matching techniques in order to face the failure of OCR and perform information retrieval in difficult cases of Document Images, e.g Historical Documents.
  • Binarization. We investigate in the field of Image Binarization Techniques. We have proposed appropriate techniques for Document Images. We have built appropriate database and we suggested objective ways for the evaluation of the binarization algorithms. We organized the IEEE ICFHR 2010 Contest: Quantitative Evaluation of Binarization Algorithms.

Applications:

  • Information Retrieval
  • Genealogical Tree
  • Image Segmentation
  • Image Cleaning and Enhancement
  • Algorithm Evaluation

Events

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