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Figure 10. Rough timeline vision of the concept-searching evolution from 1960 till 2010. Three types of concept-base search systems are presented next: KeyConcept, the Automated Generated Thesaurus Approach, and the Semantic Web. 2.3.1. KeyConcept An example of a concept-based retrieval engine is KeyConcept [GAUCH 2002]. There, concepts are first processed by a traditional indexer using training documents under a tf.idf indexing method (Figure 11). The outputs of the tf.idf method are concepts where each concept is presented as a centroid of the training set of documents for the observed concept. Figure 11. The tf.itf indexing method. The conceptual indexer of KeyConcept processes new (non training) documents by using a Vector Space Model (VSM) [SALTON 1975]. Concept-based retrieval is done by processing similarity level values obtained from VSM, words/concepts and an L-factor (entered by the user). The KeyConcept system architecture is presented in Figure 12.
Figure 12. KeyConcept system architecture. Indexing is done by a fixed number of sample documents which are collected and processed through a Traditional indexer for each concept. The output of the indexer is a set of concepts in the Concept Database (CD) which is the essence of the Conceptual Indexer (CI). Each new document is processed through CI and the output of CI is a Word plus a Concept Index (WCI). The L-factor specifies the relative importance of concept matches to word matches and is provided by the user in a scale from 0 to 1. If L is 1, only concept matches are considered. If L is 0, only word matches are considered. When L is 0.5, concept and word matches contribute equally. 2.3.2. The Automated Generated Thesaurus Approach Thesauri (listing of words with similar, related, or opposite meanings) can be an efficient tool for concept-based retrieval in the text domain. The Automated Generated Thesaurus Approach (AGTA) [CHEN 1999] provides an ability to fine tune what keywords a user had in mind (or should have had in mind) when proposing a particular query (the initial step in knowledge retrieval). AGTA carries this out in several phases: • Document collection – specifying the set of documents in a specific subject domain(s) that is to serve as the thesaurus base. • Automatic indexing of the collection of terms in the document set using an automatic indexing technique [SALTON 1975]. • Co-occurrence analysis of term frequency, inverse document frequency, and cluster analysis to assign weights to each term in a document in order to represent the term’s level of the importance. • Associative retrieval treats each term in this network-like thesaurus as an active node or neuron and the asymmetric weight between any two terms is taken as the unidirectional, weighted connection between these terms. In order to consolidate related terms and to cast out confusing outliers, the Hopfield algorithm [HOPFIELD 1982] is introduced. It first takes user-supplied terms as input patterns and then activates term neighbors (i.e., strongly associated terms), combines weights from all associated neighbors by adding collective association strengths and repeats this process until term convergence is achieved. The Hopfield algorithm also causes a damping effect in which terms farther away from the initial terms receive gradually decreasing activation weights and eventually are excluded from further processing altogether. 2.3.3. The Semantic Web The Semantic Web [LEE 2001] represents an envisioned future evolution of the World Wide Web, where information will be human and computer understandable. It gathers together both use concepts and ontologies. As presented in Figure 13, the essence of the Semantic Web are Uniform Resource Identifier’s (URI’s) [W3C 2007], which can be formulated as concepts. Ontology vocabulary puts meaning into these concepts, and through XML [W3C 2007] and RDF [W3C 2007] enables a concept-based Web search. Examples of concept-based retrieval web-search engines may be found in [LINKS01].
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SEVENTH FRAMEWORK PROGRAMME Proposa
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Table of contents: 1. Scientific an
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a. TMG (Tensiomyography) method, wh
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algorithms). Once the concepts are
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h. Complex muscle asymmetry detecti
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c. Feedback-improved existing appli
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Activities are performed by well de
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Figure 4: List of past and current
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• Artificial neural networks exte
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procedures, and the operating syste
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• Number of participants at works
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It is generally impossible to measu
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22. c Hof AL, Van den Berg J. EMG t
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• WP5 Development of a PoC, as a
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WP5 Architecture and design in CMMI
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WP3 Definitions of RRG generators W
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WP2 SW infrastructure WP3 Definitio
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D: Report on HW built into the syst
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Task#03: The first annual symposium
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execution of research activities, i
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WP3 Adding intelligence RTD 1 70 M1
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D6.2 Report on testing results 6 in
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M2.2 M2.3 Checking on SW and HW arc
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M7.4 Checking on number WP7 M36 of
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Person-months participant: per 15 2
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Deliverables D3.1 (M9) - Procedure
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Description of work WP Leader: FERR
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Description of work (WP leader: BGD
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Description of work WP Leader: ORTO
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Description of work WP Leader: MEDI
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