Knowledge Organization Systems Research Articles

Integrated use of KOS and deep learning for data set annotation in tourism domain

PurposeThe purpose of this paper is to propose a methodology for the enrichment and tailoring of a knowledge organization system (KOS), in order to support the information extraction (IE) task for the analysis of documents in the tourism domain. In particular, the KOS is used to develop a named entity recognition (NER) system.Design/methodology/approachA method to improve and customize an available thesaurus by leveraging documents related to the tourism in Italy is firstly presented. Then, the obtained thesaurus is used to create an annotated NER corpus, exploiting both distant supervision, deep learning and a light human supervision.FindingsThe study shows that a customized KOS can effectively support IE tasks when applied to documents belonging to the same domains and types used for its construction. Moreover, it is very useful to support and ease the annotation task using the proposed methodology, allowing to annotate a corpus with a fraction of the effort required for a manual annotation.Originality/valueThe paper explores an alternative use of a KOS, proposing an innovative NER corpus annotation methodology. Moreover, the KOS and the annotated NER data set will be made publicly available.

Cybersecurity knowledge graphs

Cybersecurity knowledge graphs, which represent cyber-knowledge with a graph-based data model, provide holistic approaches for processing massive volumes of complex cybersecurity data derived from diverse sources. They can assist security analysts to obtain cyberthreat intelligence, achieve a high level of cyber-situational awareness, discover new cyber-knowledge, visualize networks, data flow, and attack paths, and understand data correlations by aggregating and fusing data. This paper reviews the most prominent graph-based data models used in this domain, along with knowledge organization systems that define concepts and properties utilized in formal cyber-knowledge representation for both background knowledge and specific expert knowledge about an actual system or attack. It is also discussed how cybersecurity knowledge graphs enable machine learning and facilitate automated reasoning over cyber-knowledge.

Detecting interdisciplinary semantic drift for knowledge organization based on normal cloud model

To reduce the conceptual ambiguity in interdisciplinary knowledge organization systems (KOSs) and enhance interdisciplinary KOS management, this paper proposes a framework for interdisciplinary semantic drift (ISD) detection based on the normal cloud model (NCM). In this framework, we first analyze the features of interdisciplinary concepts and propose a novel interdisciplinary concept extraction method based on cross-discipline statistical information. Secondly, the high-performance knowledge representation model NCM is adopted to represent each interdisciplinary concept with uncertainty, and then a new ISD degree calculation method is proposed based on the similarity cloud algorithm. Thirdly, to identify the direction of ISD after the degree calculation, we propose an ISD direction identification method according to the theory of knowledge potential energy (KPE). Fourthly, based on the above procedure, we propose an ISD detection algorithm to identify and visualize the ISD process. Finally, we evaluate the proposed framework on the concept of “information entropy” and compare the performance with three baselines. Experimental results demonstrate that our framework outperforms[ all the baselines, and the result is comparable to experts’ judgments (0.808 on Spearman correlation, p<0.001). The research indicates the meaning of an interdisciplinary concept will drift from the high KPE discipline to the low KPE discipline as long as interdisciplinary knowledge potential differences (KPD) exist between these two related disciplines. We further identify three key factors that affect the degree of ISD: the length of the discipline chain of an interdisciplinary concept transfer, the number of source disciplines that an interdisciplinary concept comes from, and the knowledge distance between the source discipline and the target discipline.

Organising music’s structures: The classification of musical forms in Western art music

This article analyses the classification of musical forms in Western art music. It examines how some sources in the music domain classify musical forms, and the categorisations and complexities inherent within these classifications. It analyses the table of contents from music domain textbooks, treating them as knowledge organisation systems, as well as analysing music domain descriptions of the knowledge organisation of forms. Form is found to be a complicated type of information, with an intriguing relationship to genre. The analysis of domain classifications reveals five key categorisations: texture, sectionalisation, size of structure, definable-ness and medium. Various complexities about form are elicited, such as form-as-process, complicated whole-part form relationships, an interesting spectrum of definable-ness, and the dependency of form on medium and texture. This article examines a rarely discussed type of information, form, and its approach could be usefully extended to other subjects.

Subject-based knowledge organisation: An OER for supporting (digital) humanities research

Humanities scholars can today engage in research inquiry using data from a range of varied collections which are often characterised by poor subject access, often resulting in systems that underperform and even effectively prevent access to data, information and knowledge. In spite of the availability of professional standards and guidelines to provide quality-controlled subject access through knowledge organisation systems (KOS), subject access in such collections is rarely based on KOS. At the same time, KOS themselves may come with problems such as being slow to update, being rigidly structured and not incorporating end-users’ vocabulary. It may therefore be useful to consider methods for remediating these deficiencies in KOSs, such as collecting user-generated metadata via social tagging or complementing automated indexing techniques with manual ones. To help address the above problems, the paper discusses these challenges and points to possible solutions in different contexts. It does so by reflecting on an open educational resource (OER) devoted to this theme, titled Introduction to Knowledge Organisation Systems for Digital Humanities. It was developed as part of an EU project called DiMPAH (Digital Methods Platform for the Arts and Humanities), 2021–2023, creating seven OERs for inclusion in DARIAH Teach.

A Text Analytics Framework for Supplier Capability Scoring Supported by Normalized Google Distance and Semantic Similarity Measurement Methods

AbstractThe unstructured data available on the websites of manufacturing suppliers and contractors can provide valuable insights into their technological and organizational capabilities. However, since the capability data are often represented in an unstructured and informal fashion using natural language text, they do not lend themselves well to computational analysis. The objective of this work is to propose framework to enable automated classification and ranking of manufacturing suppliers based on their online capability descriptions in the context of a supplier search and discovery use case. The proposed text analytics framework is supported by a formal thesaurus that uses Simple Knowledge Organization System (SKOS) that provides lexical and structural semantics. Normalized Google Distance (NGD) is used as the metric for measuring the relatedness of terms when ranking suppliers based on their similarities with the queried capabilities. The proposed framework is validated experimentally using a hypothetical supplier search scenario. The results indicate that the generated ranked list is highly correlated with human judgment, especially when the search space is partitioned into multiple classes of suppliers with distinct capabilities. However, the correlation decreases when multiple overlapping classes of suppliers are merged together to form a heterogenous search space. The proposed framework can support supplier screening and discovery solutions by improving the precision, reliability, and intelligence of their underlying search engines.

Automated Knowledge Organization AI ML based Subject Indexing System for Libraries

The research study as reported here is an attempt to explore the possibilities of an AI/ML-based semi-automated indexing system in a library setup to handle large volumes of documents. It uses the Python virtual environment to install and configure an open source AI environment (named Annif) to feed the LOD (Linked Open Data) dataset of Library of Congress Subject Headings (LCSH) as a standard KOS (Knowledge Organization System). The framework deployed the Turtle format of LCSH after cleaning the file with Skosify, applied TF-IDF as a language model (backend algorithm), and selected Snowball as an analyzer. The training of Annif was conducted with a large set of bibliographic records populated with subject descriptors (MARC tag 650$a) and indexed by trained LIS professionals. The training dataset is first treated with MarcEdit to export it in a format suitable for OpenRefine, and then in OpenRefine it undergoes many steps to produce a bibliographic record set suitable to train Annif. The framework, after training, has been tested with a bibliographic dataset to measure indexing efficiencies, and finally, the automated indexing framework is integrated with a data wrangling software (OpenRefine) to produce suggested headings on a mass scale. The entire framework is based on open source software, open datasets, and open standards.

Institutional repositories and knowledge organization: A bibliographic study from Library and Information Science

This research presents an exploratory and descriptive study on the use of knowledge organization processes and systems in the context of repositories, published in journals indexed in databases between 2015 and 2020. The authors of these papers do not necessarily publish in specific events and journals in the Knowledge Organization area, but rather in the Library and Information Science arena. The study has been carried out in four steps: 1. Search, retrieval and selection of articles; 2. Development of a data codebook; 3. Identification and codification of topics; and, 4. Analysis of the data extracted. A final sample of 33 articles was defined. The methodology applied to determine the theme of journal articles is presented in detail, including the use of the Classification System for Knowledge Organization Literature (CSKOL). The illustrative data of disciplinarity and interdisciplinarity present in the articles object of this study are shown and discussed, regarding the predominance of certain ranges of CSKOL and regarding the diversity of the representative themes of the contents. It is possible to conclude that the use of CSKOL proves to be a suitable lens for analysing and understanding the literature on the field of knowledge organization in institutional repositories. It is shown that, in these 33 articles the themes of knowledge organization are combined with interdisciplinary themes from other areas of knowledge. In our opinion, this enriches and improves theoretical support for research development.

Towards a moderate realist foundation for ontological knowledge organization systems: The question of the naturalness of classifications

Several authors emphasise the need for a change in classification theory due to the influence of a dogmatic and monistic ontology supported by an outdated essentialism. These claims tend to focus on the fallibility of knowledge, the need for a pluralistic view, and the theoretical burden of observations. Regardless of the legitimacy of these concerns, there is the risk, when not moderate, to fall into the opposite relativistic extreme. Based on a narrative review of the literature, we aim to reflectively discuss the theoretical foundations that can serve as a basis for a realist position supporting pluralistic ontological classifications. The goal is to show that, against rather conventional solutions, objective scientific-based approaches to natural classifications are presented to be viable, allowing a proper distinction between ontological and taxonomic questions. Supported by critical scientific realism, we consider that such an approach is suitable for the development of ontological Knowledge Organisation Systems (KOSs). We believe that ontological perspectivism can provide the necessary adaptation to the different granularities of reality.

Integrating a new knowledge organisation system for monoclonal antibodies for therapeutic use authorised in Europe into HeTOP terminology-ontology server

Monoclonal antibodies (MAs) are increasingly used in the therapeutic arsenal. Clinical Data Warehouses (CDWs) offer unprecedented opportunities for research on real-word data. The objective of this work is to develop a knowledge organization system on MAs for therapeutic use (MATUs) applicable in Europe to query CDWs from a multi-terminology server (HeTOP). After expert consensus, three main health thesauri were selected: the MeSH thesaurus, the National Cancer Institute thesaurus (NCIt) and the SNOMED CT. These thesauri contain 1,723 MAs concepts, but only 99 (5.7 %) are identified as MATUs.The knowledge organisation system proposed in this article is a six-level hierarchical system according to their main therapeutic target. It includes 193 different concepts organised in a cross lingual terminology server, which will allow the inclusion of semantic extensions. Ninety nine (51.3 %) MATUs concepts and 94 (48.7 %) hierarchical concepts composed the knowledge organisation system. Two separates groups (an expert group and a validation group) carried out the selection, creation and validation processes.Queries identify, for unstructured data, 83 out of 99 (83.8 %) MATUs corresponding to 45,262 patients, 347,035 hospital stays and 427,544 health documents, and for structured data, 61 out of 99 (61.6 %) MATUs corresponding to 9,218 patients, 59,643 hospital stays and 104,737 hospital prescriptions. The volume of data in the CDW demonstrated the potential for using these data in clinical research, although not all MATUs are present in the CDW (16 missing for unstructured data and 38 for structured data).The knowledge organisation system proposed here improves the understanding of MATUs, the quality of queries and helps clinical researchers retrieve relevant medical information. The use of this model in CDW allows for the rapid identification of a large number of patients and health documents, either directly by a MATU of interest (e.g. Rituximab) but also by searching for parent concepts (e.g. Anti-CD20 Monoclonal Antibody).

MSLE: An ontology for materials science laboratory equipment – Large-scale devices for materials characterization

This paper introduces a new ontology for Materials Science Laboratory Equipment, termed MSLE. A fundamental issue with materials science laboratory (hereafter lab) equipment in the real world is that scientists work with various types of equipment with multiple specifications. For example, there are many electron microscopes with different parameters in chemical and physical labs. A critical development to unify the description is to build an equipment domain ontology as basic semantic knowledge and to guide the user to work with the equipment appropriately. Here, we propose to develop a consistent ontology for equipment, the MSLE ontology. In the MSLE, two main existing ontologies, the Semantic Sensor Network (SSN) and the Material Vocabulary (MatVoc), have been integrated into the MSLE core to build a coherent ontology. Since various acronyms and terms have been used for equipment, this paper proposes as an approach to use a Simple Knowledge Organization System (SKOS) to represent the hierarchical structure of equipment terms. Equipment terms were collected in various languages and abbreviations, and coded into the MSLE using the SKOS model. The ontology development was conducted in close collaboration with domain experts and focused on the large-scale devices for materials characterization available in our research group. Competency questions are expected to be addressed through the MSLE ontology. Constraints are modeled in the Shapes Query Language (SHACL); a prototype is shown and validated to prove the value of the modeling constraints.

Implementation Experience Report for Controlled Vocabularies Used with the Audubon Core Terms subjectPart and subjectOrientation

The Audubon Core vocabulary terms subjectPart and subjectOrientation are used to describe the depicted part of an organism and its orientation in an image. We describe the criteria and process for developing controlled vocabularies for these two terms. The vocabularies take the form of Simple Knowledge Organization System (SKOS) concept schemes and their terms are categorized using SKOS collections to allow users to select from particular sets of values appropriate for particular organism groups and their parts. We also report the results of implementation testing used to determine the usability of the proposed terms with actual images of living organisms and preserved specimens.

Periodization

This article first focuses on the emergence of a scholarly discourse on periodization. That discourse includes historians' efforts to diversify criteria for individuating periods, and philosophers' analyses of periodization as a form of historiographical theorizing. Next the article turns to the dynamic interaction between scholarly periodization and the broader institutionalization of periodizations. This is followed by a brief review of arguments against periodization. The article ends with a look at how periodizations are treated in knowledge organization systems (KOS).

Education in Knowledge Organization (KO)

This article provides analyses, describes dilemmas, and suggests way forwards in the teaching of knowl­edge organization (KO). The general assumption of the article is that theoretical problems in KO must be the point of departure for teaching KO. Section 2 addresses the teaching of practical, applied and professional KO, focusing on learning about specific knowl­edge organization systems (KOS), specific standards, and specific methods for organizing knowl­edge, but provides arguments for not isolating these aspects from theoretical issues. Section 3 is about teaching theoretical and academic KO, in which the focus is on examining the bases on which KOSs and knowl­edge organization processes such as classifying and indexing are founded. This basically concerns concepts and conceptual relations and should not be based on prejudices about the superiority of either humans or computers for KO. Section 4 is about the study of education in KO, which is considered important because it is about how the field is monitoring itself and about how it should be shaping its own future. Section 5 is about the role of the ISKO Encyclopedia of Knowl­edge Organization in education of KO, emphasizing the need for an interdisciplinary source that may help improve the conceptual clarity in the field. The conclusion suggests some specific recommendations for curricula in KO based on the author’s view of KO.

A Glossary for Knowledge Organization Systems Terminology

This paper describes the creation of a terminological glossary for the subject domain of knowledge organization systems (KOS). The glossary addresses the need for unambiguous conceptual information about the subject matter for students majoring in information science. It aims to create a glossary of definitions for concepts of different types of KOS. The theoretical and methodological foundations are based on corpus linguistics for the construction of the glossary. The methodology is characterized as an exploratory, descriptive, and applied study, using the ISKO Encyclopedia of Knowledge Organization (IEKO) as a source for data collection and terminology. The cut-off was based on the KOSs listed in NISO Z39.19 (2005; R2010) and ISO 25964 (2011; 2013). It is hoped that the terminological glossary created will improve students’ understanding of the different KOSs, by compiling definitions and giving them greater semantics.

Knowledge Organization System for Partial Automation to Improve the Security Posture of IoMT Networks

Remote patient monitoring is a healthcare delivery model that uses technology to collect and transmit patient data from a remote location to healthcare providers for analysis and treatment. Remote patient monitoring systems rely on a network infrastructure to gather and transmit data from patients to healthcare providers through a network. While these systems become more prevalent, they may also become targets for cyberattacks. This paper deals with the development of a domain ontology to facilitate partial automation to improve the security posture of IoT networks used in remote patient monitoring. For this purpose, it captures the semantics of the concepts and properties of the main security aspects of IoT medical devices. This is complemented by a comprehensive ruleset, evaluated by using SPARQL queries, and automated reasoning over the aggregated knowledge.

Ethical Issues of the Organization and Management of Research Information

The paper offers a comprehensive examination of the ethical considerations surrounding the management of research information within an organization. It includes a summary of survey findings and an overview of ongoing research efforts. Special attention is given to ethical standards pertaining to data quality, data models, and data formats, which are essential factors in organizing research knowledge. As open science continues to gain prominence, research ethics - such as integrity, openness, and transparency - become increasingly crucial. Adhering to these open science principles will play a pivotal role in determining future research funding. Hence, it is crucial to understand how current research information systems (CRIS), a particular type of knowledge organization systems, deal with matters concerning scientific misconduct and integrate ethical guidelines for both individuals and institutions into their data structures.

The Domain Analysis Clinic

Domain analysis is a primary approach to the representation of shared ontological bases in knowledge organization. The growth of domain analysis as a paradigm is demonstrated over a period of two decades. The Domain Analysis Clinic, or DAC is a core methodology generated for focused concept discovery combining meta-analytical theoretical research with the formation of domain-specific knowledge organization systems. The emergence of the DAC as a tool for meta-analysis and discourse analysis has demonstrated the efficacy of focused concept formation for taxonomic representation as well as for theoretical discovery in KO. The evidence presented here strongly directly suggests the continued honing and maturity of domain analysis as a paradigm in KO.

Cultural information bubbles: A new approach for automatic ethical evaluation of digital artwork collections based on Wikidata

Abstract Large digital repositories created and maintained by art museums provide open access to millions of works of art and make them available to new audiences with diverse backgrounds, views, and needs. Digitization of cultural collections by art museums has opened an opportunity to correct the historical injustices and imbalances in information representation. The first step toward this goal is a systematic critical evaluation of digital cultural collections from an ethical perspective. In this study, we propose and apply a new automated methodology for evaluation of digital cultural collections, based on a recently proposed ethical framework for evaluation of knowledge organization systems. The developed approach utilizes Wikidata for automatic creation of a unified ontological scheme comprised of ethically marked properties of cultural heritage items. These properties are used to automatically measure and compare the compliance of a database with a set of ethical criteria, on a large scale, in a database-agnostic manner. The findings, based on two prominent art museums—the Metropolitan Museum of Art and the Rijksmuseum—as well as the Wikidata artwork collection, indicate the presence of biases and a Western cultural information bubble. The Met artwork database’s scores are relatively close to Wikidata and more inclusive and balanced than those of the Rijksmuseum.

Проблеми масштабування семантичних інформаційних ресурсів зі складною структурою

We analyze scaling problems arising in modern intelligent information systems (IISs) and classify main reasons for their occurrence in their practical solutions. IISs integrate various elements of artificial intelligence (AI) for acquisition of knowledge relevant to actual user tasks. Important properties of these IISs are use of data with complex structure and orientation on semantic information resources (IRs). Therefore we analyze main features of the Data-Centric AI and opportunities for acquiring domain knowledge in various representations from Big Data. Knowledge organization systems (KOS) provide models and methods for effective store, retrieval and use of information processed by the Web-oriented IISs, and we consider existing approaches for their software platforms.We analyse the specifics of the scaling for systems focused on the semantic information processing and its differences from traditional data and Big Data scaling. This specifics is caused by complexity of data structure, number of various semantic relations between information objects into IR and complexity of semantic queries executed by KOS. On example of e-VUE – the Wiki-portal of the Great Ukrainian Encyclopedia – we analyze various situations that arise in process of practical development of semantic information resources with large volume and complex structure. Various ways of semantic retrieval into this information resource that use possibilities of the Semantic MediaWiki plugin are considered from the point of view of scaling aspects (such as increase of information objects, their relations and complication of their structure and characteristics). On base of this analysis we generate a set of recommendations aimed at ensuring more efficient development of such resources and their efficient functioning for practical use.