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Knowledge Organization Course under the Department of Library and Organization

New Promise for the Universal Control of Recorded Knowledge

The article New Promise for the Universal Control of Recorded Knowledge celebrates the use of the Marc format for Archives and Manuscripts (AMC). The author, Richard P. Smiraglia, expresses his opinions in simple language to drive the main points. He acknowledges that the online archival catalog is gaining increased acceptance among archivists. Library catalogers need to be familiar with the basic techniques of archival collections management before making use of the archival control techniques.

Key points: archival and bibliographic control, similarities, differences

Bibliographic principles started from the need to create catalogs of library collections that would promote recreational reading by providing access to written works and other items. Archival and bibliographic control have a common goal of facilitating the best possible use of recorded knowledge (Smiraglia, 1990). Archival and bibliographic control can have physical, intellectual and information seeking differences. Moreover, the two can have similarities including sharing common goals and objectives, a common descriptive dichotomy, and having a common dilemma in information seeking.

Key points: bibliographic and archival communities

It is evident that both archival and bibliographic control share similar goals and objectives. However, the two manifest differences in their descriptive techniques, physical and intellectual properties of materials, and system design considerations.   The bibliographic and archival communities must acknowledge that control retrieval systems need to make efficient use of relational database designs. The two communities should work jointly in the future to develop universal knowledge control. The discovery of the online bibliographic networks has shown many communities the pathway to universal control. There is a need to develop standards that guide the common construction of descriptions for non-textual materials.

The progress of theory in knowledge organization summary

The article the progress of theory in knowledge organization elaborates more on the advancement of theory in knowledge organization. The author, Richard P. Smiraglia, expresses his opinions in simple language to drive the main points. He acknowledges that there has been a change in how the current generation of theory happens than it was years ago. The previous system focused on observation of the construction of retrieval tools while the current system focuses on the results of empirical research. The main idea in the article is that accomplishing knowledge organization entails using a complex set of bibliographic languages.

Key points: theory, catalog, ontology, epistemology

Theory forms an important part of any research carried out as it provides hypotheses and rein-forms observations made in empirical research. Analysts use hypothesis to dissect, anticipate, and control phenomena. Libraries have an easy way of classifying and storing information due to the continuous growth and development of knowledge organization (Smiraglia, 2002). A well-crafted catalog has to clearly identify works so that it can help the end user in making an informed decision. Ontology provides a general objective framework within which knowledge may be organized while epistemology allows for perception of the knowledge and its subjective role. The historical background of pragmatism and rationalism in knowledge organization gives a detailed explanation of how the organization tools got developed, by whom, and the contributing researchers.

Key points: theory generation, rationalism, historicism

A system of testable explanatory statements derived from research remains to be the true definition of theory. It is evident that the current form of theory generation relies more on facts and empirical research than on the reasoned principles. Rationalism and historicism can be beneficial in uncovering the ineluctable truths of the natural order of knowledge entities. The psychological parts of client’s conduct are as essential as the subject qualities of the records represented. Propelling the quest for learning requires an assortment of epistemic positions.

Towards a theory of aboutness, subject, topicality, theme, domain, field, content… and relevance summary

The article towards a theory of aboutness, subject, topicality, theme, domain, field, content… and relevance focuses on the importance of incorporating the nature and meaning of topicality concepts in information retrieval. Notably, information retrieval theories must specify synonymous concepts. Birger aims to show that Bruza et al. overlooked a large and important amount of literature by failing to consider any synonyms for aboutness.

Key points: subject and aboutness, lexical and ostensive definitions

Subject and aboutness is part of the real synonyms in information science. The proper way of defining a term is by considering its role in information science and information retrieval instead of using a common sense approach. Also, lexical and ostensive definitions are alternative ways of defining scientific terms (Hjørland, 2001). In this case, lexical definitions are dependent on other already defined concepts. There is a need to develop concepts that do not prioritize certain kinds of systems at the expense of others. As such, information retrieval theories need to specify synonymous and un-synonymous concepts.

Key points: information systems, information retrieval, information research

The theoretical trends in a field determine the subject matter and the relevance of a document. There is a need to design information systems to optimize the retrieval of both relevant documents, informative documents, and the informative potentialities of documents. The determination of the subject of a document has a close relation to questions of relevance, which plays an important part in IR (Information Retrieval) research as it evaluates the output of IR systems. As such, there is a need to distinguish the different layers that influence the relevance of a document to ensure that information research advances in the right direction.

Topic analysis of the research domain in knowledge organization: a latent Dirichlet allocation approach summary

The article topic analysis of the research domain in knowledge organization: a latent Dirichlet allocation approach explores topics in the research domain of knowledge organization. The authors, Soohyung Joo, Inkyung Choi, and Namjoo Choi, express their findings in a simple language to drive the main points. The main aim of the study is to give a better understanding of the research domain of the organization of knowledge. Moreover, the study employs LDA to examine the recent trends and developments of knowledge organization research.

Key points: LDA, knowledge organization, methods, results

Latent Dirichlet Allocation (LDA) is a generative probabilistic model for topic modeling used in discovering the prevailing themes in collections of contextual data. The three research questions derived guided the investigation and research process. The methods used involved collecting published research articles from the journal Knowledge Organization published in the past ten years. Term frequency modeling and LDA were the main analysis methods used. The results indicated that “knowledg,” infor,” classif,” and “organ” formed the top four most frequent terms (Joo, Choi & Choi, 2018). The LDA results indicated that domain analysis and ontologies are a popular area of interest among the KO community. Topic modeling results revealed a close connection of several articles with theories in knowledge organization.

Key points: LDA topic modelling, text mining, domain analysis

LDA topic modeling is an unsupervised machine learning technique that can help uncover hidden themes or topics underlying a large set of textual documents in a certain domain. Text mining is advantageous as it directly examines the contents of documents by analyzing relationships among observed terms objectively. Domain analysis essentially classifies and identifies different hierarchical structures of a certain domain consisting of multiple facets. It is possible to automatically cluster or classify documents when using correlation information among documents. Supervised machine learning techniques can help classify information objects in a particular domain into different categories automatically.

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