Perceptual and Semantic Processing in Cognitive Robots

The challenge in human–robot interaction is to build an agent that can act upon human implicit statements, where the agent is instructed to execute tasks without explicit utterance. Understanding what to do under such scenarios requires the agent to have the capability to process object grounding and affordance learning from acquired knowledge. Affordance has been the driving force for agents to construct relationships between objects, their effects, and actions, whereas grounding is effective in the understanding of spatial maps of objects present in the environment. The main contribution of this paper is to propose a methodology for the extension of object affordance and grounding, the Bloom-based cognitive cycle, and the formulation of perceptual semantics for the context-based human–robot interaction. In this study, we implemented YOLOv3 to formulate visual perception and LSTM to identify the level of the cognitive cycle, as cognitive processes synchronized in the cognitive cycle. In addition, we used semantic networks and conceptual graphs as a method to represent knowledge in various dimensions related to the cognitive cycle. The visual perception showed average precision of 0.78, an average recall of 0.87, and an average F1 score of 0.80, indicating an improvement in the generation of semantic networks and conceptual graphs. The similarity index used for the lingual and visual association showed promising results and improves the overall experience of human–robot interaction.

Understanding Large-Scale Social Relationship Data by Combining Conceptual Graphs and Domain Ontologies

People worldwide communicate online and create a great amount of data on social media. The understanding of such large-scale data generated on social media and uncovering patterns from social relationship has received much attention from academics and practitioners. However, it still faces challenges to represent and manage the large-scale social relationship data in a formal manner. Therefore, this study proposes a social relationship representation model, which addresses both conceptual graph and domain ontology. Such a formal representation of a social relationship graph can provide a flexible and adaptive way to complete social relationship discovery. Using the term-define capability of ontologies and the graphical structure of the conceptual graph, this paper presents a social relationship description with formal syntax and semantics. The reasoning procedure working on this formal representation can exploit the capability of ontology reasoning and graph homomorphism-based reasoning. A social relationship graph constructed from the Lehigh University Benchmark (LUBM) is used to test the efficiency of the relationship discovery method.

Conceptual Graphs and Terminological Idiosyncrasy in UNCLOS and CBD

Do two conventions of international environmental law necessarily endow the same word with the same meaning? A single counterexample is enough to answer in the negative: this is the case of the term “resource” in the United Nations Convention on the Law of the Sea (UNCLOS) and the Convention on Biological Diversity (CBD). Beyond this result, we tackle the questions, raised by the method of analysis implemented, about the semantics of legal texts, a source of interpretative flexibility but also of cognitive amalgamations and confusions of various types. A conceptual graph is associated with each proposition or sentence comprising the term “resource.” Some expressions, especially those of a deontic nature and noun phrases naming a group of interrelated entities or a fact, are encoded in nested graphs. The scope of a term is revealed by the neighbourhood of its uses. Neighbouring expressions, positioned along the paths of conceptual graphs, are ranked owing to their distance from the target expression. Then the neighbours the most contributing to the distributional meaning of the targets are classified in a coarse taxonomy, providing basic ontological traits to “resource” and related expressions in each convention. Although the two conventions rely on the same language, the weak overlap of their respective neighbourhoods of the term “resource” and associated expressions and their contrasted ontological anchorages highlight idiosyncratic meanings and, consequently, divergent orientations and understandings regarding the protection and conservation of resources, especially of living resources. Thus, the complexity of legal texts operates both in the gap between language semantics and cognitive understanding of the concepts used, and in the interpretative flexibility and opportunities for confusion that the texts offer but that the elementary operations of formalisation allow to deconstruct and clarify.