Search (12 results, page 1 of 1)

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Abstract

In recent years, Hjørland has developed a typology of basic epistemological approaches to KO that identifies four basic positions - empiricism, rationalism, historicism/hermeneutics, and pragmatism -with which to characterize the epistemological bases and methodological orientation of KOSs. Although scholars of KO have noted that the design of a single KOS may incorporate epistemological-methodological features from more than one of these approaches, studies of concrete examples of epistemologico-methodological eclecticism have been rare. In this paper, we consider the phenomenon of epistemologico-methodological eclecticism in one theoretically significant family of KOSs - namely analytico-synthetic, or faceted, KOSs - by examining two cases - Julius Otto Kaiser's method of Systematic Indexing (SI) and Brian Vickery's method of facet analysis (FA) for document classification. We show that both of these systems combined classical features of rationalism with elements of empiricism and pragmatism and argue that such eclecticism is the norm, rather than the exception, for such KOSs in general.

Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

Type

a

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Abstract

This paper examines further a research hypothesis that syntactic variations are an interesting alternative to the clustering approach and they offer meaningful ways of highlighting and organising associated research topics in a corpus. A textmining and topic mapping system, TermWatch, has been developed based on this hypothesis. Preliminary results obtained an a large IR corpus are promising and call for further systematic investigation.

Type

a

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Abstract

A multiple-perspective cocitation analysis method is introduced for characterizing and interpreting the structure and dynamics of cocitation clusters. The method facilitates analytic and sense making tasks by integrating network visualization, spectral clustering, automatic cluster labeling, and text summarization. Cocitation networks are decomposed into cocitation clusters. The interpretation of these clusters is augmented by automatic cluster labeling and summarization. The method focuses on the interrelations between a cocitation cluster's members and their citers. The generic method is applied to a three-part analysis of the field of information science as defined by 12 journals published between 1996 and 2008: (a) a comparative author cocitation analysis (ACA), (b) a progressive ACA of a time series of cocitation networks, and (c) a progressive document cocitation analysis (DCA). Results show that the multiple-perspective method increases the interpretability and accountability of both ACA and DCA networks.

Type

a

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Abstract

Purpose - To propose a comprehensive and semi-automatic method for constructing or updating knowledge organization tools such as thesauri. Design/methodology/approach - The paper proposes a comprehensive methodology for thesaurus construction and maintenance combining shallow NLP with a clustering algorithm and an information visualization interface. The resulting system TermWatch, extracts terms from a text collection, mines semantic relations between them using complementary linguistic approaches and clusters terms using these semantic relations. The clusters are mapped onto a 2D using an integrated visualization tool. Findings - The clusters formed exhibit the different relations necessary to populate a thesaurus or ontology: synonymy, generic/specific and relatedness. The clusters represent, for a given term, its closest neighbours in terms of semantic relations. Practical implications - This could change the way in which information professionals (librarians and documentalists) undertake knowledge organization tasks. TermWatch can be useful either as a starting point for grasping the conceptual organization of knowledge in a huge text collection without having to read the texts, then actually serving as a suggestive tool for populating different hierarchies of a thesaurus or an ontology because its clusters are based on semantic relations. Originality/value - This lies in several points: combined use of linguistic relations with an adapted clustering algorithm, which is scalable and can handle sparse data. The paper proposes a comprehensive approach to semantic relations acquisition whereas existing studies often use one or two approaches. The domain knowledge maps produced by the system represents an added advantage over existing approaches to automatic thesaurus construction in that clusters are formed using semantic relations between domain terms. Thus while offering a meaningful synthesis of the information contained in the original corpus through clustering, the results can be used for knowledge organization tasks (thesaurus building and ontology population) The system also constitutes a platform for performing several knowledge-oriented tasks like science and technology watch, textmining, query refinement.

Type

a

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Abstract

We analyze the scientific discourse of researchers in a specialty field in Astronomy by examining the influence that geographic location may have on the development of this field. Using as a case study the Sloan Digital Sky Survey (SDSS) project, we analyzed texts from bibliographic records along three geographic axes: US-only publications, non-US publications and international collaboration. Each geographic region reflected authors affiliated to research institutions in that region. International collaboration refers to papers published by both US-based and non-US based institutions. Through clustering of domain terms used in titles and abstracts fields of the bibliographic records, we were able to automatically identify the topology of topics peculiar to each geographic region and identify the research topics common to the three geographic zones. The results showed that US-only and non-US research in SDSS shared more commonalities with international collaboration than with one another, thus indicating that the former two focused on rather distinct topics.

Type

a

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Abstract

The French conception of information science is often contrasted with the Anglophone one, which is perceived as different and rooted mainly in Shannon's mathematical theory of communication. While there is such a thing as a French conception of information science, this conception is not totally divorced from the Anglophone one. Unbeknownst to researchers from the two geographical and cultural regions, they share similar conceptions of the field and invoke similar theoretical foundations, in particular the socio-constructivist theory. There is also a convergence of viewpoints on the dual nature of information science, i.e., the fact that it is torn between two competing paradigms-objectivist and subjectivist. Technology is another area where a convergence of viewpoints is noticeable: Scholars from both geographic and cultural zones display the same suspicion toward the role of technology and of computer science. It would therefore be misleading to uphold the view that Anglophone information science is essentially objectivist and technicist while the French conception is essentially social and rooted in the humanities. This paper highlights converging analyses from authors based in both linguistic and geographical regions with the aim to foster a better understanding of the challenges that information science is facing worldwide and to help trace a path to how the global information science community can try to meet them.

Type

a

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Abstract

We apply an automatic topic mapping system to records of publications in knowledge organization published between 1988-2008. The data was collected from journals publishing articles in the KO field from Web of Science database (WoS). The results showed that while topics in the first decade (1988-1997) were more traditional, the second decade (1998-2008) was marked by a more technological orientation and by the appearance of more specialized topics driven by the pervasiveness of the Web environment.

Type

a

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Abstract

Purpose - The object of this study is to develop methods for automatically annotating the argumentative role of sentences in scientific abstracts. Working from Medline abstracts, sentences were classified into four major argumentative roles: objective, method, result, and conclusion. The idea is that, if the role of each sentence can be marked up, then these metadata can be used during information retrieval to seek particular types of information such as novelty, conclusions, methodologies, aims/goals of a scientific piece of work. Design/methodology/approach - Two approaches were tested: linguistic cues and positional heuristics. Linguistic cues are lexico-syntactic patterns modelled as regular expressions implemented in a linguistic parser. Positional heuristics make use of the relative position of a sentence in the abstract to deduce its argumentative class. Findings - The experiments showed that positional heuristics attained a much higher degree of accuracy on Medline abstracts with an F-score of 64 per cent, whereas the linguistic cues only attained an F-score of 12 per cent. This is mostly because sentences from different argumentative roles are not always announced by surface linguistic cues. Research limitations/implications - A limitation to the study was the inability to test other methods to perform this task such as machine learning techniques which have been reported to perform better on Medline abstracts. Also, to compare the results of the study with earlier studies using Medline abstracts, the different argumentative roles present in Medline had to be mapped on to four major argumentative roles. This may have favourably biased the performance of the sentence classification by positional heuristics. Originality/value - To the best of one's knowledge, this study presents the first instance of evaluating linguistic cues and positional heuristics on the same corpus.

Type

a

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Abstract

In a scientific and technological watch (STW) task, an expert user needs to survey the evolution of research topics in his area of specialisation in order to detect interesting changes. The majority of methods proposing evaluation metrics (bibliometrics and scientometrics studies) for STW rely solely an statistical data analysis methods (Co-citation analysis, co-word analysis). Such methods usually work an structured databases where the units of analysis (words, keywords) are already attributed to documents by human indexers. The advent of huge amounts of unstructured textual data has rendered necessary the integration of natural language processing (NLP) techniques to first extract meaningful units from texts. We propose a method for STW which is NLP-oriented. The method not only analyses texts linguistically in order to extract terms from them, but also uses linguistic relations (syntactic variations) as the basis for clustering. Terms and variation relations are formalised as weighted di-graphs which the clustering algorithm, CPCL (Classification by Preferential Clustered Link) will seek to reduce in order to produces classes. These classes ideally represent the research topics present in the corpus. The results of the classification are subjected to validation by an expert in STW.

Type

a

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Abstract

In this paper, we propose a high-level analysis of the implications of big data for knowledge organisation (KO) and knowledge organisation systems (KOSs). We confront the current debates within the KO community about the relevance of universal bibliographic classifications and the thesaurus in the web with the ongoing discussions about the epistemological and methodological assumptions underlying data-driven inquiry. In essence, big data will not remove the need for humanly-constructed KOSs. However, ongoing transformations in knowledge production processes entailed by big data and Web 2.0 put pressure on the KO community to rethink the standpoint from which KOSs are designed. Essentially, the field of KO needs to move from laying down the apodictic (that which we know for all time) to adapting to the new world of social and natural scientific knowledge by creating maximally flexible schemas-faceted rather than Aristotelean classifications. KO also needs to adapt to the changing nature of output in the social and natural sciences, to the extent that these in turn are being affected by the advent of big data. Theoretically, this entails a shift from purely universalist and normative top-down approaches to more descriptive bottom-up approaches that can be inclusive of diverse viewpoints. Methodologically, this means striking the right balance between two seemingly opposing modalities in designing KOSs: the necessity on the one hand to incorporate automated techniques and on the other, to solicit contributions from amateurs (crowdsourcing) via Web 2.0 platforms.

Type

a

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Content

We investigate the influence of culture and identity (geographic location) on the constitution of a specific research field. Using as case study the Sloan Digital Sky Survey (SDSS) project in the Astronomy field, we analyzed texts from bibliographic records of publications along three cultural and geographic axes: US only publications, non-US publications and international collaboration. Using three text mining systems (CiteSpace, TermWatch and PEx), we were able to automatically identify the topics specific to each cultural and geographic region as well as isolate the core research topics common to all geographic zones. The results tended to show that US-only and non-US research in this field shared more commonalities with international collaboration than with one another, thus indicating that the former two (US-only and non-US) research focused on rather distinct topics.

Type

a