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Introduction to KOS

Knowledge Organization Systems (KOS) is an umbrella term for all sorts of schemes relating to organizing information and promoting knowledge management. These systems consist of classification schemes that manage materials at different levels such as general level and authority files (Slavic, Siebes & Scharnhorst, 2021). Here, less-traditional schemes are also included that are semantic networks and ontologies. Being the mechanism behind organizing information, these systems have turned out to be at the core of the knowledge organizations. A KOS bridges the gap between the information needed by the user and the material that is available in the stock. With this in place, the user will be able to figure out a specific object of interest without any prior knowledge of its existence. KOS also permits the organizers to address questions related to the scope of a specific collection and what is required to round it out (Hjørland, 2021). Any digital library needs to discuss the central question of which KOS it should go for. It is to be noted that before incorporating any KOS it should be properly researched on whether the system well applies to the need of the organization. KOS is in general described based on its structures and associated main functions. Some common examples of KOS are lists, gazetteers, taxonomies, thesauri, ontologies, authority files, and more. In an organization like ours where several thousand research proposals are reviewed every year, a system as such is highly needed. Having KOS in place will make reviewing the research proposals much easy. Before proceeding with the types and categories of KOS there are a need to understand some related aspects about it (Slavic, Siebes & Scharnhorst, 2021). Such as controlled vocabularies and indexing languages are the two terms that are highly linked to KOS. Though both are highly linked to KOS, they have varied scopes. Controlled vocabularies are used for the purpose of denoting any controlled group of terms or a controlled list of terms that are included in the document description. On the other hand, indexing languages refer to defined sort of controlled vocabularies that denote formalized languages. These formalized languages are made used for describing the subject content of documents (Zhitomirsky-Geffet, 2019). Again, there are two prime types of indexing languages that are alphabetical and classifications. The key attribute of the indexing languages is that these are concerned primarily with the subject content of documents and that these consist of rules for applications. In some instances, these also contain syntax rules for the recombination of terms in the indexing process. Therefore, there is much to be explored in the context of KOS.

In this report, categories of KOS, different types of KOS, their usage, and other related aspects will be discussed. Based on this discussion, some KOSs will be recommended for the firm that can help it in its review process of research proposals. The way the company should go about the implementation of these KOS will also be provided. The issue of managing the process of review of the research proposals will be tried to be made easy with the implementation of a KOS.

Controlled vocabularies and indexing languages

In this section, categories of knowledge organization systems and the types under these categories have been provided. Based on the applicability of the types, the recommendations will be provided in the next section. There are three main categories of KOS that are Simple term lists, classifications and categories, and lastly relationship lists. The evaluation of the different KOS have been done in terms of there merits and challenges. The table given below shows comparison between the different KOS and thereafter a choice made as to which KOS should be used.

Simple term lists refer to limited sets of terms that are organized in a specific sequential order. The type of sequential order can be alphabetical, chronological, or even numerical (Joshi et al., 2021). Some of the types of term lists are as given below:

There is a list of terms that are made use of to control the names of the variant for a specific entity. This can also be for the domain value of a specific field. The examples of authority files consist of names of countries, organizations, and individuals as well. Here, there is a chance that the terms which are not preferred be associated with the preferred ones. There is no such deep organization or complicated structure for authority files (Joshi et al., 2021). Here, the presentation can be alphabetical or can also be defined by some shallow scheme of classification. Our research organization finds it difficult review different studies and thus navigation is something much needed. In this case, to make the navigation easy, a limited hierarchy can be applied. Some examples of authority files are the Getty Geographic Authority File and the Library of Congress Name Authority File.

This is commonly used and refers to a list of terms that come with definitions. These terms being mentioned can relate to a specific subject field or can also be that of a specific work (Salaba, 2020). For example, Environmental Protection Agency (EPA) is related to the environment. Research organization has to be specific with the glossaries so that the employees can relate to the subject at the start or rather they can get a brief idea about the terms and concepts.

These as the name suggests are alphabetical word lists and associated definitions. Dictionaries are taken to be more general in scope as compared to glossaries (Golub, Schmiede & Tudhope, 2019). In a research organization as ours, dictionaries are needed as these provide essential information related to the origin of a word and varied meanings across various disciplines.

A gazetteer refers to a list of place names. Traditional gazetteers are launched as books or come in the form of indexes or atlases (Gomes & da Cunha Frota, 2020). Here each entry made may be categorized by varied feature types such as a river, school, urban area, and more. This will help the research organization to classify the research materials as per their type or features. An example is the U.S. Code of Geographic Names. The gazetteers are in general classified making use of classification schemes.

Categories of KOS

Classification and categories are methods of grouping and organizing data with the aim that these may be compared with other data. The kind of classification system will be based on what the data are intended to measure. Some examples of these are given below:

This is the type of scheme that offer a set of measured terms to signify the subjects of items in a specific collection. The list of subject headings can be extensive and take under its purview a wide range of subjects. When in use in the research organization, subject headings are synchronized with certain rules as to how these can be combined to offer some specific concepts (Olawuyi, Akhigbe & Afolabi, 2018). An example is Medical Subject Headings (MeSH).

Classification schemes are often used in the names of taxonomies and categorization schemes. These sorts of KOSs will offer various ways to the research organization in which entities can be separated into "buckets”. These do not adhere to the rules for hierarchy that is otherwise needed in the ANSI NISO Thesaurus Standard. Classification schemes lack the unambiguous relationships offered in a thesaurus (Mazzocchi, 2018). Subject categories are in general made use of to group thesaurus terms into a comprehensive topic. Taxonomies again are being used highly to designate any grouping of objects relied on specific characteristics.

Relationship lists refer to complex and highly structured systems that emphasize the influences between various terms and concepts.

These are dependent on concepts and reveal relationships amidst the various terms. Relationships that are generally stated in a thesaurus consist of hierarchy, equivalence, and lastly association. The relationships being mentioned are signified by the notation broader term (BT), the related term (RT), the narrower term (NT), and lastly synonym (SY). Here it can be said that associative relationships are more thorough in certain schemes (Zeng, 2019). For instance, the Unified Medical Language System (UMLS) has set near about 40 relationships from which many are associative. The preferred terms for indexing and thereafter retrieval have also been identified. The entry terms highlight the preferred terms that are to be made use of for each of the concepts. Thesauri will be of much help to the research firm to be organized with their research papers.

This has become more common or many developments have been done in this after the development of natural language processing. These types of structured concepts of KOS and related terms are not taken to be hierarchies but are rather taken to be as a network. Concepts here are taken to be nodes and this is where the various relationships branch (Golub, Schmiede & Tudhope, 2019). These relationships are being spoken about going past the standard BT, NT, and RT. These might take into account whole-part, parent-child relationships, or cause-effect. Here in the context of the research organization, semantic networks can be used to manage and organization the research paper databases in a proper way.

These are the newest type of knowledge organization systems that is to be attached to some knowledge organization systems. The community of knowledge management is engaging in the development of ontologies as precise concept models. These can be taken to be a representation of complicated relationships amidst the objects and consist of the various rules and related axioms often missing out from the semantic networks (Biagetti, 2020). Ontologies describing knowledge in a defined arena are commonly linked with systems for data mining and knowledge management. With the use of ontologies, the research organization can be active in terms of data mining and knowledge management.

Simple term lists

All of the above-given examples of knowledge organization systems vary in their complex nature, structure, and function and can offer organizations improved access to digital libraries. In this context of the review of the proposal purpose, the use of knowledge organization systems can be justified. Management of research proposals is a difficult task indeed and thus it has to be figured out which of the above-mentioned KOS can be used at the firm. The merits and challenges of the different KOS are identified below:

Name of KOS

Merits

Challenges

Authority files

Scope present in alphabetical order or come up with some shallow scheme of classification.

In authority files, if a user wants to see more items by the same author, clicking on the name of the author will yield only results for that specific name spelling or variant (Zhu, 2019).

Glossaries

Easy to find out about the content by having a glance at the terms and their definitions provided in the glossary (Barité & Rauch, 2020).

In technical papers, glossaries can get on to become too lengthy thus decreasing the readability of the matter.

Dictionaries

This just does not stick to specific terms but rather will also provide information about the origin of the words as well (Danesh & Neamatollahi, 2020). That is these are much more informative than glossaries.

Too much information may turn off the reader. Thus, this advantage can in some cases turn out to be a disadvantage.

Gazetteers

Each of the entries that are made here is classified by varied feature types.

Traditional ones come in form of a book and thus turn out to be too lengthy.

Subject headings

With this controlled terms can be presented to signify the subjects of items in a specific collection thus will be of much help in searching materials.

These are coordinated with specific rules that can act as a hindrance to the factor of flexibility.

Classification schemes

Easy to separate entities with classification schemes.

Problems can be faced as these do not adhere to the rules of hierarchy.

Thesauri

With this concepts can be made clear and apart from that the relationships amidst the different terms can be identified in a proper way (Namdar & Shen, 2018).

Use of preferred terms is mandatory in this.

Semantic networks

These are in form of a network or web and concepts can be taken as nodes. Relationship branches are clearly defined.

These are less expressive as compared to first-order logic in some instances.

Ontologies

Complicated relationships can be represented easily using ontologies. These again are also linked with systems for purpose of data mining and knowledge management. Thus, this integration can be said to be much better.

These are constructed with ad hoc mode and thus are not good when it comes to interoperability. Then again, the various ontological aspects also lose relevance in the process of modulation (Castanha & Wolfram, 2018). Thus, this can certainly turn out to be a problem when using ontologies as knowledge organization systems. The above-mentioned factor requires to be addressed when going for implementation.

The chosen KOSs for the research organization are semantic networks and ontologies. These two are the most updated KOS and will certainly help the organization in the purpose of reviewing research proposals. For instance, with Semantic networks, various terms in the proposals can be organized thus representing defined models (Roszkowski, 2020). The relationship between variables in the studies can be handled in a better way by making use of semantic networks.

Then again, using ontologies will help handle the research organization complex variables in the proposals in a better way. These can help in the context of data mining and manage the knowledge that is gained from these proposals. With the use of ontologies, the firms will be able to bring an increase in the quality of entity analysis (El Hadi, 2019). This being in support of increased use, reprocess, and lastly, maintainability of the information systems will help the organization with the review process of the proposals.

Thus, the above-mentioned KOS should be implemented in the organization and the reasons provided are enough to justify the choice. With every passing day, organizations are looking for systems that can integrate with other systems and the choices made are apt in that respect as well.

The organization should be specific about some things before going for implementing Knowledge Organization Systems. Firstly, the needs are to be noted down so that the choices made can be doubly checked or mapped with the requirements of the firm. Then the next step is drafting a proper plan for the infrastructure. The physical location of the KOS needs to be figured and the most important question if the system will be held externally or it will be held internally. The positives and negatives of these approaches should be evaluated in a proper way so that the project does not fall flat. Lastly, it needs to be taken care of that from the starting only, the method to maintain the knowledge organization system be decided. The organization should not ignore the maintenance part as that will affect its project in the long run. Again, not forget that maintenance of content and system needs to be given due priority. Lastly, version control of the KOS is equally significant as reloading an updated version from the provider of the system turns out to be one of the best ways of controlling the version. Thus, when implementing the system, the organization has to be specific about the above-mentioned factors.

The resources that are needed to implement KOS at the research firm are human resources, IT resources including both software and hardware, financial resource, and other such miscellaneous resources. The main resource is that of IT because the integration of the new system into the existing one depends on the IT team. They are the ones to evaluate the existing systems and analyses how the new systems can be integrated well. Apart from that financial resource is also important and thus the company has to be specific about the budget estimations done.

Authority files

Conclusion

The discussion suggests that certainly, the firm will be able to better manage the review of proposals if KOS is implemented. Once implemented the KOS will act as a bridge between the organization's information needs and the materials available in the stock. With this in place, the organization will be able to figure out an object of interest without any such previous knowledge of its presence. The entire process of reviewing proposals will become easy for the staff. KOS will also permit the organization to address questions related to the collection scope and what is required to round it out. As identified to solve the purpose of the firm, semantic networks and ontologies will be best suited. These are the modern ones and will help the firm in terms of scalability and flexibility. Apart from that, implementing these will also mean proper integration of the systems into the existing systems of the firm. In all ways, the present way of operations of the organization will be enhanced and the employees are certainly benefitted. With semantic networks and ontologies, something that will be best done is establishing relationships between variables of the study. This is much needed when reviewing proposals and thus can be said to be a significant reason to implement the KOSs. The negatives that have been identified need to be addressed based on the recommendations from some industry experts. The organization has to be sure that all of its direct and indirect stakeholders for this project of KOS implementation be highly involved in the decision-making. With this, the organization will be able to ensure that the project gets successful and proper use of KOS is done. The other main important thing is that after the implementation of KOS, the organization should be specific about its maintenance of it. Proper monitoring of the systems will ensure that the desired results from the system are obtained for long rather than being short-lived. Thus, in all ways, this has to be accepted that implementing a KOS is the need of the times and the organization should follow a specific process when going for the implementation of the same.

References

Barité, M., & Rauch, M. (2020, November). Cultural Warrant: Old and New Sights from Knowledge Organization. In Knowledge Organization at the Interface (pp. 31-40). Ergon-Verlag.

Biagetti, M. T. (2020). Ontologies (as knowledge organization systems). ISKO Encyclopedia of Knowledge Organization.

Castanha, R. C. G., & Wolfram, D. (2018). The domain of knowledge organization: A bibliometric analysis of prolific authors and their intellectual space. KO KNOWLEDGE ORGANIZATION, 45(1), 13-22.

Danesh, F., & Neamatollahi, Z. (2020). Clustering the concepts and emerging events of knowledge organization. Library and Information Sciences, 23(2), 53-85.

El Hadi, W. M. (2019). Cultural Frames of Ethics, a Challenge for Information and Knowledge Organization. Zagadnienia Informacji Naukowej–Studia Informacyjne, 57(2 (114)), 23-39.

Golub, K., Schmiede, R., & Tudhope, D. (2019). Recent applications of Knowledge Organization Systems: introduction to a special issue. International Journal on Digital Libraries, 20(3), 205-207.

Gomes, P., & da Cunha Frota, M. G. (2020). Knowledge organization from a social perspective: Thesauri and the commitment to cultural diversity. KO KNOWLEDGE ORGANIZATION, 46(8), 639-646.

Hjørland, B. (2021). Information retrieval and knowledge organization: A perspective from the philosophy of science. Information, 12(3), 135.

Joshi, A., Morales, L. G., Klarman, S., Stellato, A., Helton, A., Lovell, S., & Haczek, A. (2021, June). A knowledge organization system for the united nations' sustainable development goals. In European Semantic Web Conference (pp. 548-564). Springer, Cham.

Mazzocchi, F. (2018). Knowledge organization system (KOS): an introductory critical account. KO KNOWLEDGE ORGANIZATION, 45(1), 54-78.

Namdar, B., & Shen, J. (2018). Knowledge organization through multiple representations in a computer-supported collaborative learning environment. Interactive learning environments, 26(5), 638-653.

Olawuyi, N. J., Akhigbe, B. I., & Afolabi, B. S. (2018, July). Knowledge Organization System interoperability: the cogitation of user interfaces for better interactivity. In Challenges and Opportunities for Knowledge Organization in the Digital Age (pp. 955-958). Ergon-Verlag.

Roszkowski, M. (2020). The Sociological and Ontological Dimensions of the Knowledge Organization Domain on Google Scholar Citations. KO KNOWLEDGE ORGANIZATION, 47(2), 160-172.

Salaba, A. (2020, November). Knowledge organization requirements in LIS graduate programs. In Knowledge organization at the interface (pp. 384-393). Ergon-Verlag.

Schöpfel, J., Farace, D., Prost, H., & Zane, A. (2020). Data papers as a new form of knowledge organization in the field of research data. KO KNOWLEDGE ORGANIZATION, 46(8), 622-638.

Slavic, A., Siebes, R., & Scharnhorst, A. (2021, October). Publishing a Knowledge Organization System as Linked Data. The Case of the Universal Decimal Classification. In Linking Knowledge (pp. 69-98). Ergon-Verlag.

Zeng, M. L. (2019). Interoperability. KO Knowledge Organization, 46(2), 122-146.

Zhitomirsky-Geffet, M. (2019). Towards a diversified knowledge organization system: An open network of inter-linked subsystems with multiple validity scopes. Journal of Documentation.

Zhu, L. (2019). The future of authority control: issues and trends in the linked data environment. Journal of Library Metadata, 19(3-4), 215-238.

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