@conference {550,
	title = {DS4: A Distributed Social and Semantic Search System},
	booktitle = {Proceedings of 35th European Conference on Information Retrieval (ECIR)},
	year = {2013},
	month = {March},
	address = {Moscow, Russia},
	author = {Dionisis Kontominas and Paraskevi Raftopoulou and Christos Tryfonopoulos and E.G.M. Petrakis}
}
@conference {539,
	title = {DS4: Introducing Semantic Friendship in Distributed Social Networks.},
	booktitle = {Proceedings of the 21st International Conference on Cooperative Information Systems (CoopIS)},
	year = {2013},
	month = {September},
	address = {Graz, Austria},
	author = {Paraskevi Raftopoulou and Christos Tryfonopoulos and E.G.M. Petrakis and Nikos Zevlis}
}
@conference {594,
	title = {Peer Rewiring in Semantic Overlay Networks under Churn},
	booktitle = {Proceedings of the 18th International Conference on Cooperative Information Systems (CoopIS)},
	year = {2010},
	month = {October},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Crete, Greece},
	abstract = {Semantic overlay networks have been proposed as a way to organise peer-to-peer networks; peers that are semantically, thematically or socially similar are discovered and logically organised into groups. Efficient content retrieval is then performed by routing the query towards peer groups based on their likelihood to match the query. In this paper, we study the behaviour of semantic overlay networks that support full- fledged information retrieval in the presence of peer churn. We adopt a model for peer churn, and study the effect of network dynamics on peer organisation and retrieval performance. The overlay network is evaluated on a realistic peer-to-peer environment using real-world data and queries, and taking into account the dynamics of user-driven peer participation. Using this evaluation, we draw conclusions on the performance of the system in terms of clustering efficiency, communication load and retrieval accuracy in such a realistic setting.},
	author = {Paraskevi Raftopoulou and E.G.M. Petrakis}
}
@article {540,
	title = {Rewiring Strategies for Semantic Overlay Networks},
	journal = {Distributed and Parallel Databases (DAPD)},
	volume = {26},
	year = {2009},
	pages = {181{\textendash}205},
	abstract = {Semantic overlay networks cluster peers that are semantically, thematically or socially close into groups, by means of a rewiring procedure that is periodically executed by each peer. This procedure establishes new connections to similar peers and disregards connections to peers that are dissimilar. Retrieval effectiveness is then improved by exploiting this information at query time (as queries may address clusters of similar peers). Although all systems based on semantic overlay networks apply some rewiring technique, there is no comprehensive study showing the effect of rewiring on system{\textquoteright}s performance. In this work, a framework for studying the attribution of rewiring strategies in semantic overlay networks is proposed. A generic approach to rewiring is presented and several variants of this approach are reviewed and evaluated. We show how peer organisation is affected by the different design choices of the rewiring mechanism and how these choices affect the performance of the system overall (both in terms of communication overhead and retrieval effectiveness). Our experimental evaluation with real-word data and queries confirms the dependence between rewiring strategies and retrieval performance, and gives insights on the trade-offs involved in the selection of a rewiring strategy.},
	keywords = {Information retrieval, peer-to-peer networks, rewiring strategies, semantic overlay networks},
	author = {Paraskevi Raftopoulou and E.G.M. Petrakis and Christos Tryfonopoulos}
}
@conference {595,
	title = {iCluster: a Self-Organizing Overlay Network for P2P Information Retrieval},
	booktitle = {Proceedings of 30th European Conference on Information Retrieval (ECIR)},
	year = {2008},
	month = {March},
	address = {Glasgow, Scotland},
	abstract = {We present iCluster, a self-organizing peer-to-peer overlay network for supporting full-fledged information retrieval in a dynamic environment. iCluster works by organizing peers sharing common interests into clusters and by exploiting clustering information at query time for achieving low network traffic and high recall. We define the criteria for peer similarity and peer selection, and we present the protocols for organizing the peers into clusters and for searching within the clustered organization of peers. iCluster is evaluated on a realistic peer-to-peer environment using real-world data and queries. The results demonstrate significant performance improvements (in terms of clustering efficiency, communication load and retrieval accuracy) over a state-of-the-art peer- to-peer clustering method. Compared to exhaustive search by flooding, iCluster exchanged a small loss in retrieval accuracy for much less message flow.},
	author = {Paraskevi Raftopoulou and E.G.M. Petrakis}
}
@conference {549,
	title = {Information Retrieval and Filtering over Self-Organising Digital Libraries},
	booktitle = {Proceedings of the 12th European Conference on Research and Advanced Technology for Digital Libraries (ECDL)},
	year = {2008},
	month = {September},
	address = {Aarhus, Denmark},
	author = {Paraskevi Raftopoulou and E.G.M. Petrakis and Christos Tryfonopoulos and Gerhard Weikum}
}
@conference {596,
	title = {A Measure for Cluster Cohesion in Semantic Overlay Networks},
	booktitle = {Proceedings of the 6th International Workshop on Large-Scale Distributed Systems for Information Retrieval (LSDS-IR)},
	year = {2008},
	month = {October},
	publisher = {ACM},
	organization = {ACM},
	address = {Napa Valley, California},
	abstract = {Semantic overlay networks cluster peers that are semantically, thematically or socially close into groups by means of a rewiring procedure that is periodically executed by each peer. Rewiring proceeds by establishing new connections to similar peers, and by discarding connections that are out- dated or pointing to dissimilar peers. This process aims at improving cluster quality (how well peers with similar con- tent are clustered together) and by this, at improving the flow of information in the network by reducing the number of messages that are exchanged. Therefore, measuring the quality of clustering is an important issue by itself. This is exactly the issue this work is dealing with. In this paper, we introduce a new clustering measure that takes into account the whole neighbourhood of a peer (rather than its direct neighbours) thus, providing better insight on the quality of the underlying clustered organisation. Our experimental evaluation with real-word data and queries confirms our assumption that the new measure is better suited for measuring clustering quality than other known measures, such as the (generalised) clustering coefficient.},
	author = {Paraskevi Raftopoulou and E.G.M. Petrakis}
}
@conference {597,
	title = {Design and Evaluation of Semantic Similarity Measures for Concepts Stemming from the Same or Different Ontologies},
	booktitle = {Proceedings of the 4th Workshop on Multimedia Semantics (WMS)},
	year = {2006},
	month = {June},
	address = {Chania, Crete, Greece},
	abstract = {Semantic Similarity relates to computing the similarity between concepts (terms) which are not necessarily lexically similar. We investigate approaches to computing semantic similarity by mapping terms to an ontology and by examining their relationships in that ontology. More specifically, we investigate approaches to computing the semantic similarity between natural language terms (using WordNet as the underlying reference ontology) and between medical terms (using the MeSH ontology of medical and biomedical terms). The most popular semantic similarity methods are implemented and evaluated using WordNet and MeSH. The focus of this work is also on cross ontology methods which are capable of computing the semantic similarity between terms stemming from different ontologies (WordNet and MeSH in this work). This is a far more difficult problem (than the single ontology one referred to above) which has not been investigated adequately in the literature. X-Similarity, a novel cross-ontology similarity method is also a contribution of this work. All methods examined in this work are integrated into a semantic similarity system which is accessible on the Web.},
	author = {E.G.M. Petrakis and Giannis Varelas and Angelos Hliaoutakis and Paraskevi Raftopoulou}
}
@article {592,
	title = {X-Similarity: Computing Semantic Similarity between Concepts from Different Ontologies},
	journal = {Journal of Digital Information Management (JDIM)},
	year = {2006},
	month = {December},
	abstract = {Semantic Similarity relates to computing the similarity between concepts (terms) which are not necessarily lexically similar. We investigate approaches to computing semantic similarity by mapping terms to an ontology and by examining their relationships in that ontology. More specifically, we investigate approaches to computing the semantic similarity between natural language terms (using WordNet as the underlying reference ontology) and between medical terms (using the MeSH ontology of medical and biomedical terms). The most popular semantic similarity methods are implemented and evaluated using WordNet and MeSH. The focus of this work is also on cross ontology methods which are capable of computing the semantic similarity between terms stemming from different ontologies (WordNet and MeSH in this work). This is a far more difficult problem (than the single ontology one referred to above) which has not been investigated adequately in the literature. X-Similarity, a novel cross-ontology similarity method is also a contribution of this work. All methods examined in this work are integrated into a semantic similarity system which is accessible on the Web.},
	author = {E.G.M. Petrakis and Giannis Varelas and Angelos Hliaoutakis and Paraskevi Raftopoulou}
}
@conference {598,
	title = {Semantic Similarity Methods in WordNet and their Application to Information Retrieval on the Web},
	booktitle = {Proceedings of 7th ACM International Workshop on Web Information and Data Management (WIDM)},
	year = {2005},
	month = {November},
	publisher = {ACM},
	organization = {ACM},
	address = {Bremen, Germany},
	abstract = {Semantic Similarity relates to computing the similarity be- tween concepts which are not lexicographically similar. We investigate approaches to computing semantic similarity by mapping terms (concepts) to an ontology and by examining their relationships in that ontology. Some of the most popular semantic similarity methods are implemented and evaluated using WordNet as the underlying reference ontology. Building upon the idea of semantic similarity, a novel information retrieval method is also proposed. This method is capable of detecting similarities between documents containing semantically similar but not necessarily lexicographically similar terms. The proposed method has been evaluated in retrieval of images and documents on the Web. The experimental results demonstrated very promising performance improvements over state-of-the-art information retrieval methods.},
	keywords = {Information retrieval, Semantic Similarity, WordNet, World Wide Web},
	author = {Giannis Varelas and Epimenides Voutsakis and Paraskevi Raftopoulou and E.G.M. Petrakis and Evangelos Milios}
}
