@article {243,
	title = {Exploiting multi-word similarity for retrieval in medical document collections: The TSRM approach},
	journal = {Journal of Digital Information Management},
	volume = {8},
	year = {2010},
	note = {Cited By (since 1996):1Export Date: 10 July 2014},
	month = {2010},
	pages = {315 - 321},
	abstract = {In this paper, we investigate on potential improvements to Information Retrieval (IR) models related to document representation and conceptual, topic retrieval, in medical document collections. We propose the TSRM1 (Term Similarity and Retrieval Model) approach, where document representations are based on multi-word domain terms, rather than mere single key-words, typically applied in traditional IR. The proposed representation is semantically compact and more efficient, being reduced to a limited number of meaningful multi-word terms (phrases), rather than large vectors of single-words, part of which may be void of distinctive content semantics. In computing document similarity, contrary to other state-of-theart methods examined in this work, TSRM adopts a knowledge poor solution, namely an approach which does not require any existing knowledge resources, such as ontologies, or thesauri. The evaluation of TSRM is based on OHSUMED, a standard TREC collection of medical documents and illustrated the effciency of TSRM over other well established general purpose IR models.},
	keywords = {Document representation, Information retrieval, Medical information retrieval, Term extraction, Term Similarity},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960229245\&partnerID=40\&md5=f9f58b511661960f2a01ed0691084e33},
	author = {Drymonas, E. and Zervanou, K. and Petrakis, E.G.M.}
}
@article {221,
	title = {Hierarchical clustering in medical document collections: The BIC-means method},
	journal = {Journal of Digital Information Management},
	volume = {8},
	year = {2010},
	note = {Cited By (since 1996):1Export Date: 10 July 2014},
	month = {2010},
	pages = {71 - 77},
	abstract = {Hierarchical clustering of text collections is a key problem in document management and retrieval. In partitional hierarchical clustering, which is more efficient than its agglomerative counterpart, the entire collection is split into clusters and the individual clusters are further split until a heuristicallymotivated termination criterion is met. In this paper, we define the BIC-means algorithm, which applies the Bayesian Information Criterion (BIC) as a domain independent termination criterion for partitional hierarchical clustering. We evaluate the effectiveness of BIC-means in clustering and retrieval on medical document collections and we propose a dynamic version of the BIC-Means algorithm for adapting an existing clustering solution to document additions.},
	keywords = {Bayesian information criterion (BIC), Document clustering, Document retrieval, Dynamic clustering, Performance, Termination criterion},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960667355\&partnerID=40\&md5=87c5599ab55c27b1d1ae1f1f2e76cd6b},
	author = {Hourdakis, N. and Argyriou, M. and Petrakis, E.G.M. and Milios, E.E.}
}
@article {60,
	title = {X-Similarity: Computing Semantic Similarity between concepts from different ontologies},
	journal = {Journal of Digital Information Management},
	volume = {4},
	year = {2006},
	note = {Cited By (since 1996):25Export Date: 10 July 2014},
	month = {2006},
	pages = {233 - 237},
	abstract = {Semantic Similarity relates to computing the similarity between concepts (terms) which are not necessarily lexically similar. We investigate approaches for 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.},
	keywords = {Content analysis, MeSH, Ontology, Semantics, WordNet},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33845952568\&partnerID=40\&md5=5bd213d7f778b7c4e7b32133f4b456f8},
	author = {Petrakis, E.G.M. and Varelas, G. and Hliaoutakis, A. and Raftopoulou, P.}
}
