Conceptual Graphs (CG) are a graph-based knowledge representation and reasoning formalism; fuzzy Conceptual Graphs (fCG) constitute an extension that enriches their expressiveness, exploiting the fuzzy set theory so as to relax their constraints at various levels. This paper proposes a comparative study of existing approaches over their respective advantages and possible limitations. The discussion revolves around three axes: (a) Critical view of each approach and comparison with previous propositions from the state of the art; (b) Presentation of the many possible interpretations of each definition to illustrate its potential and its limits; (c) Clarification of the part of CG impacted by the definition as well as the relaxed constraint.

Conceptual Graphs (CGs) are a graph-based knowledge representation formalism. In this paper we propose cgSpan a CG frequent pattern mining algorithm. It extends the DMGM-GSM algorithm that takes taxonomy-based labeled graphs as input; it includes three more kinds of knowledge of the CG formalism: (a) the fixed arity of relation nodes, handling graphs of neighborhoods centered on relations rather than graphs of nodes, (b) the signatures, avoiding patterns with concept types more general than the maximal types specified in signatures and (c) the inference rules, applying them during the pattern mining process. The experimental study highlights that cgSpan is a functional CG Frequent Pattern Mining algorithm and that including CGs specificities results in a faster algorithm with more expressive results and less redundancy with vocabulary.

Conceptual graphs, which is a particular type of Knowledge Graphs, play an essential role in semantic search. Prior conceptual graph construction approaches typically extract high-frequent, coarse-grained, and time-invariant concepts from formal texts. In real applications, however, it is necessary to extract less-frequent, fine-grained, and time-varying conceptual knowledge and build taxonomy in an evolving manner. In this paper, we introduce an approach to implementing and deploying the conceptual graph at Alibaba. Specifically, We propose a framework called AliCG which is capable of a) extracting fine-grained concepts by a novel bootstrapping with alignment consensus approach, b) mining long-tail concepts with a novel low-resource phrase mining approach, c) updating the graph dynamically via a concept distribution estimation method based on implicit and explicit user behaviors. We have deployed the framework at Alibaba UC Browser. Extensive offline evaluation as well as online A/B testing demonstrate the efficacy of our approach.

Conceptual Knowledge Markup Language (CKML) is an application of XML. Earlier versions of CKML followed rather exclusively the philosophy of Conceptual Knowledge Processing (CKP), a principled approach to knowledge representation and data analysis that "advocates methods and instruments of conceptual knowledge processing which support people in their rational thinking, judgment and acting and promote critical discussion." The new version of CKML continues to follow this approach, but also incorporates various principles, insights and techniques from Information Flow (IF), the logical design of distributed systems. Among other things, this allows diverse communities of discourse to compare their own information structures, as coded in logical theories, with that of other communities that share a common generic ontology. CKML incorporates the CKP ideas of concept lattice and formal context, along with the IF ideas of classification (= formal context), infomorphism, theory, interpretation and local logic. Ontology Markup Language (OML), a subset of CKML that is a self-sufficient markup language in its own right, follows the principles and ideas of Conceptual Graphs (CG). OML is used for structuring the specifications and axiomatics of metadata into ontologies. OML incorporates the CG ideas of concept, conceptual relation, conceptual graph, conceptual context, participants and ontology. The link from OML to CKML is the process of conceptual scaling, which is the interpretive transformation of ontologically structured knowledge to conceptual structured knowledge.

The Second International Conference on Conceptual Structures (ICCS'94) was held at the University of Maryland on August 16 to 20. The conference, which marked the tenth anniversary of conceptual graphs, was attended by about 80 scientists from 12 countries. This article presents some of the conference highlights. The conference marked the tenth anniversary of conceptual graphs. About 80 scientists from 12 countries attended.

This article presents an experiment of expertise capitalization in road traffic-accident analysis. We study the integration of models of expertise from different members of an organization into a coherent corporate expertise model. We present our elicitation protocol and the generic models and tools we exploited for knowledge modeling in this context of multiple experts. We compare the knowledge models obtained for seven experts in accidentology and their representation through conceptual graphs. Finally, we discuss the results of our experiment from a knowledge capitalization viewpoint.

This article presents an experiment of expertise capitalization in road traffic-accident analysis. We study the integration of models of expertise from different members of an organization into a coherent corporate expertise model. We present our elicitation protocol and the generic models and tools we exploited for knowledge modeling in this context of multiple experts. We compare the knowledge models obtained for seven experts in accidentology and their representation through conceptual graphs. Finally, we discuss the results of our experiment from a knowledge capitalization viewpoint.

Prizes were awarded to students to encourage improved research. Michel Wermelinger, Universidade Nova de Lisboa, Portugal, was the winner of the best paper award for his work "Basic Conceptual Structure Theory," which provided a significant In "Representations Technology, Bangkok, Thailand, won Papers were presented by a number interest in the use of conceptual he Second International Conference (ICCS'94) was held at the of individuals and groups from graphs. The funds were made available University of Maryland, College several countries on the development through a grant from the American Park, Maryland, on August 16 to 20. and use of the conceptual Association for Artificial Intelligence The conference marked the tenth graph representational language. Sponsors included the University of Graph Workbench," chaired by Gerard vice-president of academic affairs, Paradigm Development Corp. in Urbana, Illinois, was the second She received her Ph.D. from the introduction, "Aristotelian and

This approach leads to an infinite sequence of algorithms I. Surface Models

Abstract: A data base system that supports natural language queries is not really natural if it requires the user to know how the data are represented. This paper defines a formalism, called conceptual graphs, that can describe data according to the user’s view and access data according to the system’s view. In addition, the graphs can represent functional dependencies in the data base and support inferences and computations that are not explicit in the initial query.IBM Journal of Research and Development 20:4, pp. 336-357.