The informative role of the context and its efficient representation as “world models” are key for the strategic behavior of software agents engaged in automated negotiations to reach agreements aligned with established preferences and goals in complex and competitive environment such as e-markets. Existing approaches tackle many of the problems related to opponent modeling, preference elicitation, domain narrowing and protocol selection, as well as the definition of ontological models. However, these agents face problems to cope with dynamic contexts as they often lack of a context-aware nature and cognitive abilities to act intelligently, which raises serious concerns about their rationality to act and explain the decision-making process used for negotiations. This paper explores the potential of Knowledge Graphs (KGs) to enhance the semantic contextual understanding, adaptability to dynamic contexts and the informed decision-making process in negotiation agents. The proposal suggests leveraging the expandable nature of KGs to integrate information and metadata from the context while improving the agents' cognitive abilities with contextual understanding, reasoning and inference. Promising results are presented in a smart grid case study, envisioning a future of self-explainable negotiation agents that make rational decisions given the contextual circumstances encoded in their KGs.

Knowledge is currently considered as a strategic resource for organisations. Most organisational knowledge is stored in knowledge objects with natural language contents, which should be managed in their original representation to facilitate proper access without losing its semantics. This work presents a knowledge representation model that provides an innovative structure for representing domain knowledge and a set of semantic strategies that use this model to annotate and search explicit knowledge. This model makes a proper conceptual separation between ontological and linguistic spaces enabling an adequate representation of concepts of the world and its description in a particular language. By this model, semantics of knowledge objects can be captured taking into account compound terms and their semantic derivatives (synonyms and hyperonyms/hyponyms). These features extend the abilities of traditional keyword-based searches and help users to retrieve explicit knowledge.

Domain ontologies are used in several document annotation and retrieval strategies for knowledge management systems. They help to improve semantic annotation and to provide context for information. Domain knowledge changes and grows over time as accumulated experiences and such evolution should be addressed by adding new terms in the ontology. In this paper, we propose an ontology evolution strategy for a knowledge management architecture based on distributed organisational memories. This strategy provides support for annotation and retrieval knowledge through natural language queries with compound terms. The proposed strategy adds new compound terms to domain ontology during the annotated process of a document, allowing the annotation process execution with an uncomplete ontology.