1,720,970 research outputs found
Related scientific information: a study on user-defined relevance.
Full text linkThis dissertation presents an investigation into the manifestations of relevance observed in the context of related scientific information. The main motivation is to observe if researchers, in the context of knowledge discovery, use different criteria to judge the relevance of the information presented. Additionally, the effects that discipline and research experience background may have on these manifestations are investigated. The scenario selected to carry out the observation is that of Literature Based Discovery (LBD). LBD is a trial-error interactive search strategy, developed by Swanson (1986a), which supports the finding and retrieving of complementary bodies of literature “ sets of articles that are bibliographically non-interactive yet logically connected. Research scientists from three different disciplines and research experience backgrounds are observed while they interact with an LBD system built for the purposes of this study. Their cognitive processes and interactions are recorded and analysed. To aid in the analysis of the data, the concept of relevance criteria profiles is developed. Relevance criteria profiles are a technique to count and group the expressions of relevance criteria as observed during the search sessions. These offer the possibility of aggregating the observations into group profiles as well as the ability to measure the (dis)similarities that may arise in between profiles. As relevance criteria profiles provide a global view of the criteria used to judge relevance, a complementary visualisation technique is also developed. This technique displays the relevance judgement processes, as well as the interactions, in a sequential fashion allowing the researcher to perform temporal analyses on the session data. The results show that researchers do use a variety of criteria when judging the relevance of information in the context of LBD. Moreover, individuals use these criteria in different frequencies; both discipline and research experience background seem to influence these frequencies however they may not be the only intervening factors. The observed interaction patterns suggest that researchers approach the problem in two stages: i) an initial more exploratory stage followed by ii) a more focused and engaged stage. The main contribution of this thesis is the observation of these manifestations of relevance together with the interaction patterns. The final recommendation offered is that the multi-dimensional nature of relevance in this context should be addressed when evaluating LBD systems. Additionally, it is acknowledged that certain interaction behaviours may also be used during the design and testing of such systems
Hybrid algorithms for distributed constraint satisfaction.
Full text linkA Distributed Constraint Satisfaction Problem (DisCSP) is a CSP which is divided into several inter-related complex local problems, each assigned to a different agent. Thus, each agent has knowledge of the variables and corresponding domains of its local problem together with the constraints relating its own variables (intra-agent constraints) and the constraints linking its local problem to other local problems (inter-agent constraints). DisCSPs have a variety of practical applications including, for example, meeting scheduling and sensor networks. Existing approaches to Distributed Constraint Satisfaction can be mainly classified into two families of algorithms: systematic search and local search. Systematic search algorithms are complete but may take exponential time. Local search algorithms often converge quicker to a solution for large problems but are incomplete. Problem solving could be improved through using hybrid algorithms combining the completeness of systematic search with the speed of local search. This thesis explores hybrid (systematic + local search) algorithms which cooperate to solve DisCSPs. Three new hybrid approaches which combine both systematic and local search for Distributed Constraint Satisfaction are presented: (i) DisHyb; (ii) Multi-Hyb and; (iii) Multi-HDCS. These approaches use distributed local search to gather information about difficult variables and best values in the problem. Distributed systematic search is run with a variable and value ordering determined by the knowledge learnt through local search. Two implementations of each of the three approaches are presented: (i) using penalties as the distributed local search strategy and; (ii) using breakout as the distributed local search strategy. The three approaches are evaluated on several problem classes. The empirical evaluation shows these distributed hybrid approaches to significantly outperform both systematic and local search DisCSP algorithms. DisHyb, Multi-Hyb and Multi-HDCS are shown to substantially speed-up distributed problem solving with distributed systematic search taking less time to run by using the information learnt by distributed local search. As a consequence, larger problems can now be solved in a more practical timeframe
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Ontology driven information retrieval.
Full text linkOntology-driven information retrieval deals with the use of entities specified in domain ontologies to enhance search and browse. The entities or concepts of lightweight ontological resources are traditionally used to index resources in specialised domains. Indexing with concepts is often achieved manually and reusing them to enhance search remains a challenge. Other challenges range from the difficulty in merging multiple ontologies for use in retrieval to the problem of integrating concept-based search into existing search systems. We mainly encounter these challenges in enterprise search environments, which have not kept pace with Web search engines and mostly rely on full-text search systems. Full-text search systems are keyword-based and suffer from well-known vocabulary mismatch problems. Ontologies model domain knowledge and have the potential for use in understanding the unstructured content of documents. In this thesis, we investigate the challenges of using domain ontologies for enhancing search in enterprise systems. Firstly, we investigate methods for annotating documents by identifying the best concepts that represent their contents. We explore ways to overcome the challenges of insufficient textual features in lightweight ontologies and introduce an unsupervised method for annotating documents based on generating concept descriptors from external resources. Specifically, we augment concepts with descriptive textual content by exploiting the taxonomic structure of an ontology to ensure that we generate useful descriptors. Secondly, the need often arises for cross-ontology reasoning when using multiple ontologies in ontology-driven search. Once again, we attempt to overcome the absence of rich features in lightweight ontologies by exploring the use of background knowledge for the alignment process. We propose novel ontology alignment techniques which integrate string metrics, semantic features, and term weights for discovering diverse correspondence types in supervised and unsupervised ontology alignment. Thirdly, we investigate different representational schemes for queries and documents and explore semantic ranking models using conceptual representations. Accordingly, we propose a semantic ranking model that incorporates the knowledge of concept relatedness and a predictive model to apply semantic ranking only when it is deemed beneficial for retrieval. Finally, we conduct comprehensive evaluations of the proposed methods and discuss our findings
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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