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5 results for “Knowledge Organization Systems”
Modelling knowledge organization systems and structures
<p>In the last few decades, knowledge organization systems (KOS), especially thesauri,<br> classification schemes and lists of subject headings, have largely followed or conformed<br> with the established data models defined by standards, recommendations or best practices.<br> This long list contains some widely used models, such as ISO5964 Part 1, ISO2788, Z39.19,<br> BS 5723 and BS 6723, (Dextre Clarke, 2008) IFLA Principles Underlying Subject Heading<br> Languages (SHLs), and MARC 21 Format for Classification Data.<br> The FRSAD (Functional Requirements for Subject Authority Data) conceptual model is<br> the third member of the FRBR family, developed under the auspices of IFLA. The report<br> was approved in 2010 and will be published in 2011. FRSAD is a general conceptual model<br> that focuses on the subject relationship and therefore provides a theoretical framework for<br> all KOS and their data models. In addition, it also assists in the assessment of the potential<br> for international sharing and (re)use of subject authority data both within the library sector<br> and beyond.<br> In this paper FRSAD is compared to SKOS and SKOS XL as data models (with implementa-<br> tion examples).</p>
Knowledge organization systems and their consequences for information retrieval
<p>Traditionally, research on knowledge organization systems (KOS) and information retrieval discussed the relative advantages or disadvantages of using controlled vocabularies versus free-text or intellectual indexing versus automatic indexing methods for indexing and search. Experiments and case studies variously showed the superiority of either approach without reaching a final conclusion on this seemingly basic question. As full-text indexing has become more possible and now prevalent, the discussion of the relative merits of KOS – not only as substitute but in combination with full-text – was not settled but continued with new challenges. With the advent of the Semantic Web, KOS (now appearing as ontologies) became important tools in new information retrieval applications and were pushed once again to the research forefront. With different disciplines working in the field, the terminology around KOS has become more and more ambiguous up to the point that tracing research in the literature is difficult – ironically something that traditional KOS have always tried to mitigate.<br> This paper summarizes recent discussions of the impact of KOS on information retrieval and attempts to show and unify different research strands from library science research on subject indexing, information retrieval and the Semantic Web. Whereas earlier impact studies on retrieval resulted in clearly measurable outcomes (for example changes in precision/recall), recent use of KOS in Semantic Web applications or other information systems has switched from pure search scenarios to exploration (browse) and contextualization, for which clear (and calculable) evaluation or quality standards and </p>
Knowledge organization systems as enablers to the conduct of science
<p>The sophistication of knowledge organization systems (KOS) has evolved rapidly over the past thirty years, largely driven by information technology innovations. Two key assumptions have been (a) that KOS-work is the preserve of information professionals acting as skilled intermediaries, and (b) that it is largely focused on enabling the finding and discovery of information. This paper challenges both assumptions with reference to the conduct of science in the 21st century, by describing the ways in which access to KOS skills and tools is already broadening beyond information professionals to scientists, and by describing how knowledge organization systems enable sense-making of trends within science and new knowledge creation, beyond simple access and discovery roles. It closes with remarks on the implications for information professionals engaged in KOS-related work.</p>
New Ways of Mapping Knowledge Organization Systems. Using a SemiAutomatic MatchingProcedure for Building Up Vocabulary Crosswalks
<p>Abstract: Crosswalks between different vocabularies are an indispensable prerequisite for integrated and high­quality search scenarios in distributed data environments. Offered through the web and linked with each other they act as a central link so that users could move back and forth between different data sources being online available.<br> In the past, crosswalks between different thesauri have been primarily developed manually. In the long run the intellectual updating of such crosswalks requires huge personnel expenses. Therefore, an integration of automatic matching procedures, as for example Ontology Matching Tools, seems pretty obvious.<br> On the basis of computer­generated correspondences between the Thesaurus for Economics (STW) and the Thesaurus for the Social Sciences (TheSoz) our contribution will explore cross­border approaches between IT­assisted tools and procedures on the one hand<br> and external quality measurements via domain experts on the other hand. Thus, we will present techniques to semi­automatically perform vocabulary crosswalks. Due to intellectually evaluated results of multiple matching tools in the forerun, quality statements concerning the reliability of further computer­generated crosswalks can be made. This way, the application of various tools and procedures gradually contributes to an increase in quality. Moreover, on the long­term it facilitates a continuous update of high­quality vocabulary crosswalks.</p>
ARENA_Hierarchical Organization of Distributed Semantic Knowledge in the Human Language System_Language Study Pt. 1: stimulus set
<p>P4_WP1_01 Language Study Pt. 1: stimulus set</p> <p> </p> <p>Folder structure: </p> <p>Raw_input<br>The raw_input contains the text for every chapter of the book to be used in the experiment (Moonwalk mit Einstein: Wie aus einem vergeßlichen Mann ein Gedächtnis-Champion wurde, by Joshua Foer, translated by Ulla Rahn-Huber. Published by Riemann Verlag (28 Mar. 2011)). <br>Additionally, in the folder there is the pretrained vector model for German words (model comes from https://fasttext.cc/docs/en/crawl-vectors.html), ratings for concreteness and word frequency (all references are included in the scripts). Moreover, there is a translated version of the Things labels for intersecting the single words with the THINGS dataset (Hebart MN, Dickter AH, Kidder A, Kwok WY, Corriveau A, et al. (2019) THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images. PLOS ONE 14(10): e0223792. https://doi.org/10.1371/journal.pone.0223792)</p> <p>Scripts<br>In this folder, there are 6 scripts to sample single words from the raw text. The scripts are numbered according to the intended order of use. <br>- 1_from_text_to_df.py: from raw text only nouns and verbs are extracted with their relative word frequency, concreteness, lemma form, and number of characters. </p> <p>- 2_cluster_words.py: cluster analysis of word vectors to sample the semantic space as broadly as possible. Loosely based on Pereira, F., Lou, B., Pritchett, B. et al. Toward a universal decoder of linguistic meaning from brain activation. Nat Commun 9, 963 (2018). https://doi.org/10.1038/s41467-018-03068-4. </p> <p>- 3_compute_orthographic_density.py: add information for OND20 for both word forms and lemma forms. </p> <p>- 4_syntactic_valency.py: this applies only to verbs. It counts the number of arguments necessary for a verb to saturate its syntactic valency (e.g., subject + object). </p> <p>- 5_sampling_nouns.py: it samples nouns by preserving the distributions of all variables. The number of characters for every word is kept under 10. Additionally, the extreme quantiles of concreteness are matched by all other variables to ensure that more concrete and more abstract words in the set are still match along the other ratings. </p> <p>- 6_sampling_verbs.py: same as above but for verbs. </p> <p>Stimuli <br>In this folder, the pool of words before sampling is included. Note that some words have been manually excluded for several reasons: e.g., parsed wrongly in their lemma form; offensive words; words coming from other languages. </p> <p> </p>
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