01850nas a2200217   4500008004100000245010200041210006900143260000900212300001400221490000600235520109100241653002801332653002601360653003401386653002001420653002001440100001701460700001701477700002101494856011701515       2010                        eng d00aExploiting multi-word similarity for retrieval in medical document collections: The TSRM approach0 aExploiting multiword similarity for retrieval in medical documen  c2010  a315 - 3210 v83 aIn 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.10aDocument representation10aInformation retrieval10aMedical information retrieval10aTerm extraction10aTerm Similarity1 aDrymonas, E.1 aZervanou, K.1 aPetrakis, E.G.M.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-79960229245&partnerID=40&md5=f9f58b511661960f2a01ed0691084e3301582nas a2200241   4500008004100000245008200041210006900123260000900192300001200201490000600213520077800219653004100997653002401038653002301062653002301085653001601108653002601124100001801150700001701168700002101185700001701206856011701223       2010                        eng d00aHierarchical clustering in medical document collections: The BIC-means method0 aHierarchical clustering in medical document collections The BICm  c2010  a71 - 770 v83 aHierarchical 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.10aBayesian information criterion (BIC)10aDocument clustering10aDocument retrieval10aDynamic clustering10aPerformance10aTermination criterion1 aHourdakis, N.1 aArgyriou, M.1 aPetrakis, E.G.M.1 aMilios, E.E.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-79960667355&partnerID=40&md5=87c5599ab55c27b1d1ae1f1f2e76cd6b01899nas a2200229   4500008004100000245009100041210006900132260000900201300001400210490000600224520117600230653002101406653000901427653001301436653001401449653001201463100002101475700001601496700002001512700002001532856011701552       2006                        eng d00aX-Similarity: Computing Semantic Similarity between concepts from different ontologies0 aXSimilarity Computing Semantic Similarity between concepts from   c2006  a233 - 2370 v43 aSemantic 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.10aContent analysis10aMeSH10aOntology10aSemantics10aWordNet1 aPetrakis, E.G.M.1 aVarelas, G.1 aHliaoutakis, A.1 aRaftopoulou, P.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-33845952568&partnerID=40&md5=5bd213d7f778b7c4e7b32133f4b456f8