Ontology-Based Knowledge Representation in Robotic Systems: A Survey Oriented toward Applications

Knowledge representation in autonomous robots with social roles has steadily gained importance through their supportive task assistance in domestic, hospital, and industrial activities. For active assistance, these robots must process semantic knowledge to perform the task more efficiently. In this context, ontology-based knowledge representation and reasoning (KR & R) techniques appear as a powerful tool and provide sophisticated domain knowledge for processing complex robotic tasks in a real-world environment. In this article, we surveyed ontology-based semantic representation unified into the current state of robotic knowledge base systems, with our aim being three-fold: (i) to present the recent developments in ontology-based knowledge representation systems that have led to the effective solutions of real-world robotic applications; (ii) to review the selected knowledge-based systems in seven dimensions: application, idea, development tools, architecture, ontology scope, reasoning scope, and limitations; (iii) to pin-down lessons learned from the review of existing knowledge-based systems for designing better solutions and delineating research limitations that might be addressed in future studies. This survey article concludes with a discussion of future research challenges that can serve as a guide to those who are interested in working on the ontology-based semantic knowledge representation systems for autonomous robots.

Representation of knowledge in digital educational environments: A systematic review of literature

The representation of knowledge is a process widely used in education for its potential to generate deep learning, metacognition, and also in mapping the student's cognitive structure while developing a broad spectrum of thinking skills. Notwithstanding the abovementioned benefits, the development and evolution of new digital ecologies of learning is still an unexplored field for knowledge representation systems. As part of a larger study, this article shows the process and results of a systematic review of literature on knowledge representation systems, with the purpose of identifying the foundations and main applicable instruments of digital educational environments. Among the most representative findings of this review is that despite the existence of a large number of educational experiences that have incorporated both physical and digital knowledge representation tools, their use has been restricted almost entirely to the understanding of concepts and the assessment of learning in non-collaborative environments. These findings suggest the relevance of studying the representation of knowledge in digital collaborative contexts that facilitate the development of thinking skills for the digital age, and the need for co-creation and transformation of knowledge. Together these suggest a new perspective on knowledge representation for digital ecologies of learning.

Formal Definition and Modeling Language of Association-Oriented Database Metamodel (AssoBase)

The paper provides a concise discussion of the most important theoretical aspects of the Association-Oriented Database (AODB) Metamodel. Even though the model has been practically verified, the author has focused on its formal aspects and modeling language. The AODB Metamodel has been developed for the purposes of building the knowledge representation systems. Basically, such systems are structurally and functionally complex, hence they require advanced solutions to be applied for the purpose of data modeling. The modeling language enables designing database structures in the AODB Metamodel, taking into account various features of this database metamodel. The language in question is fully integrated and compatible with AODB Metamodel. It has been developed for the purposes of this metamodel, it operates with categories specific to it and, as such, it constitutes neither a version nor an extension of any of the existing languages. The second part of the paper provides the definition and discussion concerning the graphical modeling language — Association-Oriented Modeling Language (AML). The last section of the paper introduces the case-study that presents the key features of the metamodel, as well as the use of modeling language. The topics of presented examples comprise a simplified model of degree programs for universities and the model of Ontological Core, the main module of Semantic Knowledge Base (SKB).

A novel inheritance mechanism for modeling knowledge representation systems

The mechanism of inheritance is a powerful tool used to describe complexity of a reality fraction. It is particularly important for knowledge representation systems modelling. It provides a specific ability to take over properties from a base element, what is crucial for conciseness, and modelling efficiency as well. In the Association-Oriented Database Metamodel, inheritance retains coherence with the object-oriented model in the most general terms. However, it has been otherwise defined which stems from the specificity of the metamodel, and particularly from its capabilities which have blazed a trail for its further extension. The main contribution of this article is a description of preliminary assumptions, postulates and conceptual solutions applicable to inheritance. They have been discussed against the background of the Association-Oriented Database Metamodel as well as an objectoriented model compared with the former.

Knowledge Extraction from Domain-Specific Documents

Knowledge-based systems have become widespread in modern years. Knowledge-base developers need to be able to share and reuse knowledge bases that they build. As a result, interoperability among different knowledge-representation systems is essential. Domain ontology seeks to reduce conceptual and terminological confusion among users who need to share various kind of information. This paper shows how these structures make it possible to bridge the gap between standard objects and Knowledge-based Systems.

Phenotypes in insect biodiversity research

Recent advances in Web technology and information sciences, especially the development of knowledge representation systems---ontology languages (Web Ontology Language) and syntaxes (Manchester syntax)---are now infiltrating the world of insect biodiversity research. Data generated from taxonomic revisions, comparative morphology studies, and other enterprises now have the potential to be shared broadly and to be computed across---i.e., they are rendered semantic---in order to address questions relevant to multiple domains in the life sciences. In this chapter we describe the philosophy behind these new tools, the mechanisms by which they operate, and the real and future benefits of the semantic representation of phenotype data (e.g., standardization of terminology). We provide examples using real data and describe some limitations of semantic phenotype annotations.