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As we have discussed in previous works, glossaLAB project is devoted to the integration of an extensive encyclopedic corpus into a system of interdisciplinary glossaries -whose concept has been described thoroughly-. By that means, glossaLAB intends to offer a platform for the co-creation of interdisciplinary and transdisciplinary knowledge that revives sound endeavours to strengthen the capacity of systems science and information studies -including a panoply of related disciplines- based on the clarification of concepts, theories, metaphors and problems addressed by these disciplines. To this purpose, the first step consists in the transference of contents from the original non-standard formats -mostly focused on a particular way of presentation- to an interoperable coding through which the content can be delivered to multiple platforms, and particularly to the elucidation platform, based on MediaWiki technology in which the co-creation of contents is to be continued. The magnitude of the corpus -over six thousand articles in a hypertext of more than one hundred thousand links- requires the automation of the process. We describe the process developed to make the content interoperable and to deploy it into the glossaLAB platform set up for the continuation of the elucidation process. Furthermore, glossaLAB is devised as a federated system of interdisciplinary glossaries which evolve autonomously but cooperate in the development of a network of transdisciplinary concepts whose performance is intended to grow over time. This organisation also serves to the qualification of the integration of knowledge achieved through the elucidation process based on a bi-dimensional measurement of the diversity of the co-created knowledge and the effectiveness of the integration achieved.

In the last two decades of knowledge organization (KO) research, there has been an increasing interest in the context-dependent nature of human knowledge. Contextualism maintains that knowledge is not available in a neutral and objective way, but is always interwoven with the process of knowledge production and the prerequisites of the knower. As a first step towards a systematic organization of epistemic contexts, the concept of knowledge will be considered in its onto- logical (WHAT) and epistemological (WHO) including methodological (HOW) dimensions. In current KO research, however, either the contextualism is not fully implemented (classification-as-ontology) or the ambition for a context-transcending universal KOS seems to have been abandoned (classification-as-epistemology). Based on a combined ontology and epistemology it will be argued that a phenom- ena-based approach to KO as stipulated by the Leon Manifesto, for example, requires a revision of the underlying phenomenon concept as a relation between the known object (WHAT) and the knowing subject (WHO), which is constituted by the application of specific meth- ods (HOW). While traditional subject indexing of documents often relies on the organizing principle "levels of being" (WHAT), for a fu- ture context indexing, two novel principles are proposed, namely "levels of knowing" (WHO) and "integral methodological pluralism" (HOW).

Taxonomies play a central role in conceptual domain modeling having a direct impact in areas such as knowledge representation, ontology engineering, software engineering, as well as in knowledge organization in information sciences. Despite their key role, there is in the literature little guidance on how to build high-quality taxonomies, with notable exceptions such as the OntoClean methodology, and the ontology-driven conceptual modeling language OntoUML. These techniques take into account the ontological meta-properties of types to establish well-founded rules for forming taxonomic structures. In this paper, we show how to leverage on the formal rules underlying these techniques to build taxonomies which are correct by construction. We define a set of correctness-preserving operations to systematically introduce types and subtyping relations into taxonomic structures. To validate our proposal, we formalize these operations as a graph grammar. Moreover, to demonstrate our claim of correctness by construction, we use automatic verification techniques over the grammar language to show that: (i) all taxonomies produced by the grammar rules are correct; and (ii) the rules can generate all correct taxonomies.

The essay situates the academic subfield of the digital humanities (DH) as a notable example of late 20th century theories of knowledge (e.g. writings of Jean-Francois Lyotard and Fredric Jameson) that called for or predicted a shift from modern to postmodern cultures of knowledge work, knowledge organization, and knowledge production. The essay reviews DH as a case study of knowledge culture transitioning within the academic industry and suggests insights for how similar transitions may develop in other industries.

Learner models are constructed from learner understanding states regarding learning materials, obtained by observing learner behaviors. A learner model collects information for adaptive tutoring and uses this information to determine system actions such as hints, problem presentations, and explanations appropriate to learner understanding. A learner model thus plays an essential role in intelligent tutoring systems (ITSs). Our previously developed ITSs have adopted a simple learner model that considers once-solved problems as learner-acquired solutions for problem solving. However, such models cannot grasp learner proficiency for solutions, which is an important aspect of constructing learner models because learners often make problem-solving mistakes despite having already learned the solution. In such situations, it is effective for an ITS to apply scaffolding based on learner proficiency regarding the solution. However, excess scaffolding decreases comprehension, a phenomenon known as the “assistance dilemma.” A learner model that adaptively scaffolds based on analysis of learner proficiency in solutions can effectively mitigate the assistance dilemma. In this study, we develop and evaluate an ITS with a learner model that considers learner proficiency in solutions. Experimental results indicate that the developed learner model can reduce total feedback without changing learner comprehension.

Concept maps are significant tools able to support several tasks in the educational area such as curriculum design, knowledge organization and modeling, students’ assessment and many others.

For an information retrieval project, we are in the process of developing an ontology of the Tunisian archeology, a termino-ontological resource that ensures (a) the model role of the domain that list all the relevant concepts and (b) the link between the concepts and the way they are named in the archaeologist’s documents. This double function allows the annotation and the indexing of images of an archaeological site and the search for information in annotated images. The development of ontology will be realized in 6 steps among of which we describe the principal ones in this paper. This project shows that (1) the viability of such resource presupposes a precise articulation of the concepts and terms, and (2) such prerequisite can be attained by the implementation of procedures based on the architecture of meta-modeling, which enables to model the set of the Knowledge Organization Systems and the structures of the necessary knowledge.

How does creative problem solving work in natural cognitive systems? How could it be implemented in artificial cognitive systems? To answer these questions, one has to start by taking a stance on a question which is more foundational in nature.

Considering the complexity and dynamic nature of cyberthreats, the automation of data-driven analytics in cyberthreat intelligence is highly desired. However, the terminology of cyberthreat intelligence varies between methods, techniques, and applications, and the corresponding expert knowledge is not codified, making threat data inefficient, and sometimes infeasible, to process by semantic software agents. Therefore, various data models, methods, and knowledge organization systems have been proposed over the years, which facilitate knowledge discovery, data aggregation, intrusion detection, incident response, and comprehensive and automated data analysis. This chapter reviews the most influential and widely deployed cyberthreat classification models, machine-readable taxonomies, and machine-interpretable ontologies that are well-utilized in cyberthreat intelligence applications.

As an efficient model for knowledge organization, the knowledge graph has been widely adopted in several fields, e.g., biomedicine, sociology, and education. And there is a steady trend of learning embedding representations of knowledge graphs to facilitate knowledge graph construction and downstream tasks. In general, knowledge graph embedding techniques aim to learn vectorized representations which preserve the structural information of the graph. And conventional embedding learning models rely on structural relationships among entities and relations. However, in educational knowledge graphs, structural relationships are not the focus. Instead, rich literals of the graphs are more valuable. In this paper, we focus on this problem and propose a novel model for embedding learning of educational knowledge graphs. Our model considers both structural and literal information and jointly learns embedding representations. Three experimental graphs were constructed based on an educational knowledge graph which has been applied in real-world teaching. We conducted two experiments on the three graphs and other common benchmark graphs. The experimental results proved the effectiveness of our model and its superiority over other baselines when processing educational knowledge graphs.

Based on the research of Dunhuang resources and the Silk Road culture and history, this paper discusses applying digital culture to humanities research. With advanced computing technology and network technology, this paper aims to create an open collaboration environment for academic resources, and it finally proposes a new knowledge organization and management paradigm which is helpful for “Belt and Road Initiative” studies and Dunhuang studies.

Currently, computing education, especially programming education has become more important. Meanwhile, programming education has many difficulties such as should learn many concepts and skills. Researches of Intelligent Tutoring System (ITSs) have been attempted to reduce these difficulties. ITSs are educational systems that able to adaptively pedagogical behavior and feedback and aim to supply adaptive tutoring to learner’s profiles by alternate to human tutors. Despite there are much supports to programming education by ITSs, there no attempts for organizing knowledge in programming. Organizing knowledge is acquiring the systematized knowledge and its scalability which enabling to existing knowledge reuse to the same or similar problems that solved once by scaling knowledge. We considered that organizing knowledge is fosters problem-solving skills, and it gains Computational Thinking eventually. Therefore, we have been focused on supporting the process of solving problems by combining a bit of program. Then we selected the knowledge of “function” and “source program that achieves the function” as knowledge to be organized. And, we defined a pair of knowledge as a component. In this paper, we proposed and developed Compogram: an ITS for organizing knowledge by visualizing behavior in programming. Furthermore, for identifying learning gains, we conducted an evaluation compared to our conventional systems. Results were suggested that Compogram was fostering knowledge organizing skills that can apply to out of learning ranges.

The intelligent maintenance of power plants greatly relies upon previous experiences and historical information to make informed decisions. Previous research is predominantly focused on collecting data from the maintenance process while little work has been done on the automatic capture and mining of knowledge resources from the data accumulated. Focusing on the experience feedback issue in power plants maintenance, this work proposes a novel process of automatic construction and reasoning of knowledge graphs to support the intelligent maintenance of complex power equipment. In this process, the Bi-LSTM-CRF model and the attention-based Bi-LSTM are specifically used to identify and extract entities and relations from unstructured status reports. On this basis, the knowledge graph construction method based on the neo4j graph database is developed. This paper details the preliminary work towards implementing the proposed process.

Part 3: TRIZ Applications in Technical Design; International audience; Through analyzing the solving mechanism of product inventive design problem based on TRIZ theory, we propose in this paper a multi-perspective and multi-level knowledge organization model, so as to fill the “GAP” between the general solution and the particular solution in the process of solving inventive problems. We labeled here design knowledge by five attributes, i.e., TRIZ, function, behavior-flow, effect, and domain, each of which includes respectively three levels that are conceptual level, semantic level and factual resource level. Thereby, a multi-perspective and multi-level local knowledge base is established. Besides, we put forward also a card-type knowledge representation template, which divides the traditional cases, patents and other knowledge into several independent parts, and each of them can be displayed and pushed to designers in form of cards. Thus, it’s conductive to stimulate designers’ analogy and association. One multi-perspective and multi-level knowledge organization based inventive problem solving model is conclusively established, which supports analysis and problem-solving in different perspectives and at different levels. A conceptual design of an ultrasonic washing machine is illustrated at the end as an implemented example for this proposed model.

This paper explains the connection and mapping of knowledge representations between RDF and CMDI. Therefore, the challenge is to create a bridge between Linked Open Data (LOD) and the Component MetaData Infrastructure (CMDI) to ensure that the limits of the two paradigms are compensated and strengthened to create a new hybrid approach. While on the one hand, CMDI is easier to use for modelling purposes, the Metadata is not descriptive enough for a document to be easily discoverable using Linked Data (LD) technologies to publish and to enrich the document’s content. Yet on the other hand, the explicit semantics and high interoperability of LOD have many advantages, but its modelling process is too complex for non-expert users. Here we show how knowledge organization plays a crucial role in this issue.

In this article, the Basel Register of Thesauri, Ontologies & Classications (BARTOC.org) is introduced to raise awareness for an integrated, full terminology registry for knowledge organization systems. Recently, researchers have shown an increased interest in such a single access point for controlled vocabularies. The paper outlines BARTOC's technical implementation, system architecture, and services in the light of semantic technologies. Its central thesis is that if the KOS community agreed on BARTOC as one of their main terminology registries, all involved parties would benefit from linked open knowledge organization systems.

An approach based on answer set programming (ASP) is proposed in this paper for representing knowledge generated from natural language text. Knowledge in the text is modeled using a Neo Davidsonian-like formalism, represented as an answer set program. Relevant common sense knowledge is additionally imported from resources such as WordNet and represented in ASP. The resulting knowledge-base can then be used to perform reasoning with the help of an ASP system. This approach can facilitate many natural language tasks such as automated question answering, text summarization, and automated question generation. ASP-based representation of techniques such as default reasoning, hierarchical knowledge organization, preferences over defaults, etc., are used to model common-sense reasoning methods required to accomplish these tasks. In this paper we describe the CASPR system that we have developed to automate the task of answering natural language questions given English text. CASPR can be regarded as a system that answers questions by “understanding” the text and has been tested on the SQuAD data set, with promising results.

The paper presents strategy for organizing the content of the academic discipline using an ontological approach, which enables an effective system solution of a range of important methodological, methodical and technological tasks for the development of intellectualized systems of electronic education in the field of information technologies. The mathematical structures that describe and detail the abstract logic-semantic core of the discipline in the form of a set of axiomatic systems are developed. It is shown that the ontological approach of the organization of educational content ensures the presence of a clear, compact, ordered structure of the knowledge organization about the subject area of the academic discipline and is well consistent with the theoretical formal basis for the development of modern ontologies - a family of descriptive logics. As an example of the application of the proposed approach, the elements of the glossary and taxonomies of the concepts of the discipline “Computer Logic” are developed.

Conservation is an integral process of collections management aiming to preserve cultural heritage objects in the best possible condition. Object conservation procedures require detailed and accurate documentation in textual or visual records which provide valuable information for the future researcher, curator or conservator. Furthermore, conservation requires the awareness of cultural, historical and scientific information from sources both internal and external which in turn influence the ways in which conservators must approach their work. This integration of different information forms the body of knowledge, relevant to thoughtful decisions on treatment and care of cultural heritage objects. Taking into consideration the diversity of conservation information and associated information sources, the integration cannot be regarded as a trivial task. Therefore, knowledge organization, especially in a concepts level, is necessary. To this end this work presents a domain ontology known as the Conservation Reasoning (CORE) ontology aiming to address the specific requirements of the conservation sector.

Open Knowledge Graphs (KGs) such as DBpedia and Wikidata have been recognized as the foundations for diverse applications in the field of data mining and information retrieval. Each of these KGs follows a different knowledge organization as well as is based on differently structured ontologies. Moreover, it has been observed that type information are often noisy, incomplete or even incorrect. In general, there is a need for well defined and comparable type information for the entities of the KGs. In this paper, we propose an isomorphism-based approach to infer subsumption relations to RDF type information in Wikidata by exploiting the RDF type information from DBpedia.

DyKOSMap is a research prototype developed in the framework of the DynaMO project. It aims to maintain existing mappings established between knowledge organization systems (KOSs) by taking into account information about the dynamics of these KOSs. The aim of this work is to implement a Web application, in order to make a more robust and easier-to-manipulate tool starting from this prototype.

Semantic Search emerged as the new system paradigm in enterprise information systems. These information systems allow for the problem-oriented and context-aware access of relevant information. Ontologies, as a formal knowledge organization, represent the key component in these information systems, since they enable the semantic access to information. However, very few enterprises already can provide technical ontologies for information integration. The manual construction of such knowledge organizations is a time-consuming and error-prone process. In this paper, we present a novel approach that automatically constructs technical knowledge organizations. The approach is based on semantified document structures and constraints that allow for the simple adaptation to new enterprises and information content.

We begin this chapter by outlining a set of interdisciplinary information needs derived from our discussion in Chap. 1. We then discuss each of these in turn. We close the chapter by discussing how disciplinary scholars would be affected by the adoption of KOSs that met interdisciplinary needs.

This chapter begins with several strategies for developing the sort of general classification urged in preceding chapters. It first discusses strategies for reducing ambiguity. It then addresses how to structure a phenomenon-based KOS. Integrative levels, dependence relationships, and general systems theory are explored. The chapter then looks in some detail at the practical classification of phenomena and relationships.