A model for information retrieval driven by conceptual spaces

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

Fuzzy concept analysis for semantic knowledge extraction

2010 - 2011Availability of controlled vocabularies, ontologies, and so on is enabling feature to provide some added values in terms of knowledge management. Nevertheless, the design, maintenance and construction of domain ontologies are a human intensive and time consuming task. The Knowledge Extraction consists of automatic techniques aimed to identify and to define relevant concepts and relations of the domain of interest by analyzing structured (relational databases, XML) and unstructured (text, documents, images) sources. Specifically, methodology for knowledge extraction defined in this research work is aimed at enabling automatic ontology/taxonomy construction from existing resources in order to obtain useful information. For instance, the experimental results take into account data produced with Web 2.0 tools (e.g., RSS-Feed, Enterprise Wiki, Corporate Blog, etc.), text documents, and so on. Final results of Knowledge Extraction methodology are taxonomies or ontologies represented in a machine oriented manner by means of semantic web technologies, such as: RDFS, OWL and SKOS. The resulting knowledge models have been applied to different goals. On the one hand, the methodology has been applied in order to extract ontologies and taxonomies and to semantically annotate text. On the other hand, the resulting ontologies and taxonomies are exploited in order to enhance information retrieval performance and to categorize incoming data and to provide an easy way to find interesting resources (such as faceted browsing). Specifically, following objectives have been addressed in this research work: Ontology/Taxonomy Extraction: that concerns to automatic extraction of hierarchical conceptualizations (i.e., taxonomies) and relations expressed by means typical description logic constructs (i.e., ontologies). Information Retrieval: definition of a technique to perform concept-based the retrieval of information according to the user queries. Faceted Browsing: in order to automatically provide faceted browsing capabilities according to the categorization of the extracted contents. Semantic Annotation: definition of a text analysis process, aimed to automatically annotate subjects and predicates identified. The experimental results have been obtained in some application domains: e-learning, enterprise human resource management, clinical decision support system. Future challenges go in the following directions: investigate approaches to support ontology alignment and merging applied to knowledge management.X n.s

FCAIR 2012 Formal Concept Analysis Meets Information Retrieval Workshop co-located with the 35th European Conference on Information Retrieval (ECIR 2013) March 24, 2013, Moscow, Russia

International audienceFormal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classifiation. The area came into being in the early 1980s and has since then spawned over 10000 scientific publications and a variety of practically deployed tools. FCA allows one to build from a data table with objects in rows and attributes in columns a taxonomic data structure called concept lattice, which can be used for many purposes, especially for Knowledge Discovery and Information Retrieval. The Formal Concept Analysis Meets Information Retrieval (FCAIR) workshop collocated with the 35th European Conference on Information Retrieval (ECIR 2013) was intended, on the one hand, to attract researchers from FCA community to a broad discussion of FCA-based research on information retrieval, and, on the other hand, to promote ideas, models, and methods of FCA in the community of Information Retrieval

Theories of information and uncertainty for the modelling of information retrieval : an application of situation theory and Dempster-Shafer's theory of evidence

Current information retrieval models only offer simplistic and specific representations of information. Therefore, there is a need for the development of a new formalism able to model information retrieval systems in a more generic manner. In 1986, Van Rijsbergen suggested that such formalisms can be both appropriately and powerfully defined within a logic. The resulting formalism should capture information as it appears in an information retrieval system, and also in any of its inherent forms. The aim of this thesis is to understand the nature of information in information retrieval, and to propose a logic-based model of an information retrieval system that reflects this nature. The first objective of this thesis is to identify essential features of information in an information retrieval system. These are: 0 flow, 0 intensionality, 0 partiality, 0 structure, 0 significance, and o uncertainty. It is shown that the first four features are qualitative, whereas the last two are quantitative, and that their modelling requires different frameworks: a theory of information, and a theory of uncertainty, respectively. The second objective of this thesis is to determine the appropriate framework for each type of feature, and to develop a method to combine them in a consistent fashion. The combination is based on the Transformation Principle. Many specific attempts have been made to derive an adequate definition of information. The one adopted in this thesis is based on that of Dretske, Barwise, and Devlin who claimed that there is a primitive notion of information in terms of which a logic can be defined, and subsequently developed a theory of information, namely Situation Theory. Their approach was in accordance with Van Rijsbergen' s suggestion of a logic-based formalism for modelling an information retrieval system. This thesis shows that Situation Theory is best at representing all the qualitative features. Regarding the modelling of the quantitative features of information, this thesis shows that the framework that models them best is the Dempster-Shafer Theory of Evidence, together with the notion of refinement, later introduced by Shafer. The third objective of this thesis is to develop a model of an information retrieval system based on Situation Theory and the Dempster-Shafer Theory of Evidence. This is done in two steps. First, the unstructured model is defined in which the structure and the significance of information are not accounted for. Second, the unstructured model is extended into the structured model, which incorporates the structure and the significance of information. This strategy is adopted because it enables the careful representation of the flow of information to be performed first. The final objective of the thesis is to implement the model and to perform empirical evaluation to assess its validity. The unstructured and the structured models are implemented based on an existing on-line thesaurus, known as WordNet. The experiments performed to evaluate the two models use the National Physical Laboratory standard test collection. The experimental performance obtained was poor, because it was difficult to extract the flow of information from the document set. This was mainly due to the data used in the experimentation which was inappropriate for the test collection. However, this thesis shows that if more appropriate data, for example, indexing tools and thesauri, were available, better performances would be obtained. The conclusion of this work was that Situation Theory, combined with the Dempster-Shafer Theory of Evidence, allows the appropriate and powerful representation of several essential features of information in an information retrieval system. Although its implementation presents some difficulties, the model is the first of its kind to capture, in a general manner, these features within a uniform framework. As a result, it can be easily generalized to many types of information retrieval systems (e.g., interactive, multimedia systems), or many aspects of the retrieval process (e.g., user modelling)