Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine

Although African patients use both conventional or modern and traditional healthcare simultaneously, it has been proven that 80% of people rely on African traditional medicine (ATM). ATM includes medical activities stemming from practices, customs and traditions which were integral to the distinctive African cultures. It is based mainly on the oral transfer of knowledge, with the risk of losing critical knowledge. Moreover, practices differ according to the regions and the availability of medicinal plants. Therefore, it is necessary to compile tacit, disseminated and complex knowledge from various Tradi-Practitioners (TP) in order to determine interesting patterns for treating a given disease. Knowledge engineering methods for traditional medicine are useful to model suitably complex information needs, formalize knowledge of domain experts and highlight the effective practices for their integration to conventional medicine. The work described in this paper presents an approach which addresses two issues. First it aims at proposing a formal representation model of ATM knowledge and practices to facilitate their sharing and reusing. Then, it aims at providing a visual reasoning mechanism for selecting best available procedures and medicinal plants to treat diseases. The approach is based on the use of the Delphi method for capturing knowledge from various experts which necessitate reaching a consensus. Conceptual graph formalism is used to model ATM knowledge with visual reasoning capabilities and processes. The nested conceptual graphs are used to visually express the semantic meaning of Computational Tree Logic (CTL) constructs that are useful for formal specification of temporal properties of ATM domain knowledge. Our approach presents the advantage of mitigating knowledge loss with conceptual development assistance to improve the quality of ATM care (medical diagnosis and therapeutics), but also patient safety (drug monitoring)

Conceptual analysis knowledge management and conceptual graph theory

There exists an impressive quantity of literature dealing with knowledge Representation that covers highly technical contributions as well as more philosophical ones or again those that have a more or less explicit "cognitive" orientation. So, it is not very astonishing to notice that the definition of what knowledge representation is, is quite vague. It is not our intention to give a historical survey of that notion nor to proceed to a critical enumeration of the several topics that are covered by it. Our objective is, rather, to develop a conceptual framework that should permit us to handle the major descriptive problems in the conception of knowledge based systems. In order to be able to put forth in a systematic way our conception of knowledge representation (KR), we will discuss in the first section some central problems of knowledge description. In the second section, we will introduce the conceptual graph theory developed mainly by Sowa (1984) and try to give a more formal account of KR

Supporting Argumentation Systems by Graph Representation and Computation

International audienceArgumentation is a reasoning model based on arguments and on attacks between arguments. It consists in evaluating the acceptability of arguments, according to a given semantics. Due to its generality, Dung's framework for abstract argumentation systems, proposed in 1995, is a reference in the domain. Argumentation systems are commonly represented by graph structures, where nodes and edges respectively represent arguments and attacks between arguments. However beyond this graphical support, graph operations have not been considered as reasoning tools in argumentation systems. This paper proposes a conceptual graph representation of an argumentation system and a computation of argument acceptability relying on conceptual graph default rules

A conceptual graph-based model of creativity in learning

Teaching creativity is one of the key goals of modern education. Yet, promoting creativity in teaching remains challenging, not least because creative achievement is contingent on multiple factors, such as prior knowledge, the classroom environment, the instruction given, and the affective state of the student. Understanding these factors and their interactions is crucial for successfully integrating creativity in teaching. However, keeping track of all factors and interactions on an individual student level may well exceed the capacity of human teachers. Artificial intelligence techniques may thus prove helpful and necessary to support creativity in teaching. This paper provides a review of the existing literature on creativity. More importantly, the review is distilled into a novel, graph-based model of creativity with three target audiences: Educators, to gain a concise overview of the research and theory of creativity; educational researchers, to use the interactions predicted by theory to guide experimental design; and artificial intelligence researchers, who may use parts of the model as a starting point for tools which measure and facilitate creativity.Peer Reviewe

What conceptual graph workbenches need for natural language processing

An important capability of the conceptual graph knowledge engineering tools now under development will be the transformation of natural language texts into graphs (conceptual parsing) and its reverse, the production of text from graphs (conceptual generation). Are the existing basic designs adequate for these tasks? Experience developing the BEELINE system's natural language capabilities suggests that good entry/editing tools, a generous but not unlimited storage capacity and efficient, bidirectional lexical access techniques are needed to support the supply of data structures at both the linguistic and conceptual knowledge levels. An active formalism capable of supporting declarative and procedural programs containing both linguistic and knowledge level terms is also important. If these requirements are satisfied, future text-readers can be included as part of a conceptual knowledge workbench without unexpected problems

Default Conceptual Graph Rules: Preliminary Results for an Agronomy Application

International audienceIn this paper, we extend Simple Conceptual Graphs with Reiter's default rules. The motivation for this extension came from the type of reasonings involved in an agronomy application, namely the simulation of food processing. Our contribution is many fold: rst, the expressivity of this new language corresponds to our modeling purposes. Second, we provide an effective characterization of sound and complete reasonings in this language. Third, we identify a decidable subclass of Reiter's default logics. Last we identify our language as a superset of SREC-, and provide the lacking semantics for the latter language

AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba

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.Comment: Accepted by KDD 2021 (Applied Data Science Track

Knowledge formalization in experience feedback processes : an ontology-based approach

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving themissions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable

Requirements modelling and formal analysis using graph operations

The increasing complexity of enterprise systems requires a more advanced analysis of the representation of services expected than is currently possible. Consequently, the specification stage, which could be facilitated by formal verification, becomes very important to the system life-cycle. This paper presents a formal modelling approach, which may be used in order to better represent the reality of the system and to verify the awaited or existing system’s properties, taking into account the environmental characteristics. For that, we firstly propose a formalization process based upon properties specification, and secondly we use Conceptual Graphs operations to develop reasoning mechanisms of verifying requirements statements. The graphic visualization of these reasoning enables us to correctly capture the system specifications by making it easier to determine if desired properties hold. It is applied to the field of Enterprise modelling