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Tanase, D. (2015)
Languages: English
Types: Doctoral thesis
Subjects: UOWSAT
A retrieval model describes the transformation of a query into a set of documents. The question is: what drives this transformation? For semantic information retrieval type of models this transformation is driven by the content and structure of the semantic models. In this case, Knowledge Organization Systems (KOSs) are the semantic models that encode the meaning employed for monolingual and cross-language retrieval. The focus of this research is the relationship between these meanings’ representations and their role and potential in augmenting existing retrieval models effectiveness. The proposed approach is unique in explicitly interpreting a semantic reference as a pointer to a concept in the semantic model that activates all its linked neighboring concepts. It is in fact the formalization of the information retrieval model and the integration of knowledge resources from the Linguistic Linked Open Data cloud that is distinctive from other approaches. The preprocessing of the semantic model using Formal Concept Analysis enables the extraction of conceptual spaces (formal contexts)that are based on sub-graphs from the original structure of the semantic model. The types of conceptual spaces built in this case are limited by the KOSs structural relations relevant to retrieval: exact match, broader, narrower, and related. They capture the definitional and relational aspects of the concepts in the semantic model. Also, each formal context is assigned an operational role in the flow of processes of the retrieval system enabling a clear path towards the implementations of monolingual and cross-lingual systems. By following this model’s theoretical description in constructing a retrieval system, evaluation results have shown statistically significant results in both monolingual and bilingual settings when no methods for query expansion were used. The test suite was run on the Cross-Language Evaluation Forum Domain Specific 2004-2006 collection with additional extensions to match the specifics of this model.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1.1 Semantic Web Stack . . . . . . . . . . . . . . . . . . . . . . . . 6
    • 1.2 Linguistic Linked Open Data cloud . . . . . . . . . . . . . . . . 8
    • 1.3 Constraints Space . . . . . . . . . . . . . . . . . . . . . . . . . . 12
    • 2.1 Selected entries from the CSA, TheSoz, and INION thesaurus 42
    • 2.2 Mixed entry obtained from merging information from multilingual thesauri TheSoz, CSA, INION Fautsch et al. [2007] . . . . 43
    • 3.1 Overview of types of KOSs and their degree of formality adapted from Brewster and Wilks [2004] . . . . . . . . . . . . . . . . . . 47
    • 3.2 CLIR Flow of Processes . . . . . . . . . . . . . . . . . . . . . . 55
    • 3.3 GEMET Climatic Change Concept . . . . . . . . . . . . . . . . 57
    • 3.4 SKOS concept climatic change definition with highlighted semantic annotations . . . . . . . . . . . . . . . . . . . . . . . . . 64
    • 3.5 Climatic change Phrase Identification . . . . . . . . . . . . . . 66
    • 3.6 SPARQL query to serialize annotations and translations for the climatic change . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
    • 4.1 WordNet descriptions of the different synsets of word change 75
    • 4.2 The Hasse Diagram for Kchange context . . . . . . . . . . . . . . 76
    • 4.3 The concept lattice for KClimateEN FR . . . . . . . . . . . . . . . 81
    • 4.4 The concept lattice for KClimateEN DE . . . . . . . . . . . . . . . 82
    • 5.1 Connected documents through the Semantic Model . . . . . . 87
    • 5.2 An interpretation within the Semantic Web space of Dahlerberg's meaning triangle . . . . . . . . . . . . . . . . . . . . . . 91
    • 6.1 Classic vs SIR Processes . . . . . . . . . . . . . . . . . . . . . . 117
    • 6.2 TheSoz school SKOS Concept . . . . . . . . . . . . . . . . . . . 118
    • 6.3 Sample of GIRT German Document . . . . . . . . . . . . . . . . 124
    • 6.4 Sample of GIRT English Document . . . . . . . . . . . . . . . . 124
    • 6.5 Formal concepts frequency per documents . . . . . . . . . . . 125
    • 6.6 Example CLEF Topic . . . . . . . . . . . . . . . . . . . . . . . . 126
    • 6.7 Monolingual retrieval performance with weighting model DLH13136
    • 6.8 Monolingual retrieval performance with weighting model PL2 136
    • 6.9 Monolingual retrieval performance with weighting model TF-IDF137
    • 6.10Bilingual retrieval performance with weighting model DLH13 137
    • 6.11Bilingual retrieval performance with weighting model PL2 . . 139
    • 6.12Bilingual retrieval performance with weighting model TF-IDF 139
    • 6.13Monolingual German retrieval considering with parameters and path length . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
    • 7.1 From query to documents, a transformation process driven by conceptual spaces . . . . . . . . . . . . . . . . . . . . . . . . . . 155
    • 3.1 GEMET VoID summary description . . . . . . . . . . . . . . . . 59
    • 4.1 Cross table for context Kchange where the objects are the synonyms of word change, the attributes are its WordNet senses, and the incidence relation is the semantic relation from WordNet 77
    • 4.2 Cross table for context KClimateEN FR where the objects and attribtutes are determined based on Google Translate forward and back-translation in English and French starting with the word climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
    • 4.3 Overview of lattice-based IR models . . . . . . . . . . . . . . . 84
    • 5.1 Paths through the Semantic Model . . . . . . . . . . . . . . . . 89
    • 5.2 Cross table for context Kskos:exactMatch with G=M={GEMET SKOS concepts} and I=skos:exactMatch . . . . . . . . . . . . . . . . 92
    • 5.3 SKOS datasets as Formal Contexts . . . . . . . . . . . . . . . . 94
    • 5.4 Associative and Hierarchical Branching . . . . . . . . . . . . . 94
    • 5.5 Term-Document Matrix . . . . . . . . . . . . . . . . . . . . . . . 95
    • 5.6 Cross table for Kskos:broader . . . . . . . . . . . . . . . . . . . . . 98
    • 5.7 Cross table for Kskos:narrower . . . . . . . . . . . . . . . . . . . . 98
    • 5.8 Cross table for Kskos:related . . . . . . . . . . . . . . . . . . . . . 98
    • A2 Detailed description of TheSoz's structural relations and correspondence to SKOS specification . . . . . . . . . . . . . . . . . 161
    • A3 CHiC Ad-Hoc Multilingual Official Runs . . . . . . . . . . . . . 165
    • A4 Summary Results of the Monolingual EN & IT Unofficial Runs 166
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