Toward a Knowledge Management Approach Based ...

Toward a Knowledge Management Approach Based on an Ontology and Case-based Reasoning (CBR) Application to Railroad Accidents

Ahmed Maalel

Lassad Mejri

RIADI Labs. National School of Computer Sciences, University of Manouba, Tunisia [email protected]

Abstract— The work developed in the context of this article stems from the thesis work in progress (done in RIADI labs. at the National School of Computer Sciences, Tunisia in collaboration with the Research Unit of Evaluation of Automated Transport Systems and Safety IFSTTAR, French). The works treat the problem of knowledge management of a critical area, that of security and in particular the railroad accidents. The goal is to provide methodological support and tools to support the capitalization and exploitation of produced knowledge and/or used by domain experts. We have proposed an approach based on domain ontology and Case-based Reasoning CBR. We will present as part of this article, the first realized works in our approach. Keywords-component; Knowledge Management; Ontology, Case-based Reasoning; Security; Railroad Accidents.

I.

Habib Hadj Mabrouk

Henda Ben Ghezela

RIADI Labs. National School French Institute of Science and RIADI Labs. National School of Computer Sciences, Technology of Transport, of Computer Sciences, University of Manouba, Planning and Networking. University of Manouba, Tunisia IFSTTAR France Tunisia [email protected] [email protected] [email protected]

INTRODUCTION

A system of case-based reasoning (CBR) is a knowledgebased system that relies on different types of knowledge (vocabulary, case base, similarity measures and adaptation knowledge) for problem solving. In this context, ontologies play an important role in CBR systems, because they can significantly reduce the effort of acquiring knowledge in the various stages of reasoning. Ontologies are as effective ways to formalize, structure, store and use knowledge. They help to establish a common vocabulary for describing the case, or to model the knowledge necessary for indexing and organizing the event. They can also be used to enable semantic reasoning in the calculation of similarity [1]. In this paper, we propose a new approach that derives from knowledge engineering an ontology-based reasoning and casebased CBR. The context of the application of this approach is the field of railroad accident. The implementation of this approach favors the use of semantic web techniques. Our work is a two-field contribution, first to a knowledge engineering in that it aims to propose a methodology for developing a system based on Case Based Reasoning on an

ontology which at present, in the heart of work in the field of knowledge engineering, and second as in input on the scope, accidents in the rail, which is a first original for this work. The organization of this paper is as follows: section 2 presents the process of CBR, section 3 presents ontology and the different elements that compose it, Section 4 describes the general architecture of the proposed approach, Section 5 describes the first part realized by the proposed approach namely, the knowledge model and the tool implemented for querying the model. II.

THE CASE BASED REASONING : CBR

A. Presentation The Case Based Reasoning is a type of reasoning in Artificial Intelligence AI in the field of automatic learning. Case based reasoning means remembering past situations similar to the current situation and by these situations to help resolve the current situation. The case based reasoning (CBR) is a form of reasoning by analogy [2,3]. The analogy searches for cause and effect relation in past situations and transfer to the current situation the similarities between the past situations as well as the current situation. The case based reasoning research only looks for similarities or proximity relations between past situations and the current situation. The C.B.R. considers reasoning as a process of remembering a small set of practical situations: the cases, it bases its decisions on the comparison of the new situation (target cases) with the old (reference cases). The general principle of CBR is to treat a new problem (target case) by remembering similar past experiences (base case) [2,3]. This type of reasoning rests on the assumption that if a past experience and new circumstances are sufficiently similar, then everything can be explained or applied to past experience (base case) and remains valid when applied to the new situation which represents the new problem to solve. From a very global view, the CBR uses a basis of experience or case, a mechanism for searching and retrieving similar cases and an adaptation mechanism and evaluation

solutions of selected cases emanating in order to solve the specified problem (Fig. 1).

important role off-line during the construction of new cases or sources of knowledge of adaptation, as we shall see in of this article.

Figure 2.

Figure 1. Cycle case based reasoning (CBR) [2]

B. Formalization Taken up in following the notation presented in [4], In an application of CBR, we consider the existence of two spaces: Problems, problems space and solutions, the solution space. Assume that there exists a binary relation, G on Problems × Solutions meaning "for a solution." Solve problems is to find (or build) sol a problem pb Solutions as bps solution is to ground it in writing, sol = Sol (pb). We consider that a case is defined by a pair (bp, Sol (pb)). A case-based reasoning is intended to solve a new problem, called the target problem and noted the target by using or already-solved similar problem, called source and noted srce problem and its solution Sol (srce). C. Knowledge Base A CBR system is a knowledge-based system because it relies on a set of knowledge to problem solving. [5] introduced the concept of containers of knowledge (knowledge containers) that contain the knowledge and structure used by a Case Based Reasoning. This knowledge is about the application domain, but also on the methods used in problem solving. Richter has distinguished the following four containers (Fig. 2): base case, vocabulary, similarity measures, and changes in solution (adaptation knowledge) used during the adaptation. According to [6], the last three containers containing knowledge compiled in session problem-solving (in offline mode), while the case base contains knowledge (cases) performed only during problem solving (in-line mode). This is not always true, because the case base can also play an

Containers of knowledge [5]

III.

ONTOLOGY

A. Presentation Reference [7] proposes the following definition: "an explicit specification of conceptualization" that is so far the definition most cited in the literature in artificial intelligence. This definition was modified slightly [8] as "formal specification of a shared conceptualization." These two definitions are contained in that of (Studer et al., 1998) as "formal and explicit specification of a shared conceptualization." •

Formal: the ontology should be machine readable, which excludes natural language.

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Explicit: the explicit definition of the concepts and constraints of their use.

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Conceptualization: the abstract model of a real world phenomenon by identifying the key concepts of this phenomenon.

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Shared: the ontology is not the property of an individual, but it represents a consensus accepted by a community of users.

B. The components of an ontology It is possible and even advisable to use the plural to refer to the notion of ontology to reflect the many facets that it covers. According to [9] there are several types of ontologies according to the model domain and possibly the tasks for which they are designed.

In our context, we chose the domain ontology. This ontology expresses conceptualizations specific to particular areas of ontologies, such as the safety of railroad accidents in particular, while being generic to the field. These conceptualizations are constraints on the structure and contents of domain knowledge. An ontology can be viewed as a lattice of concepts and relationships between these concepts intended to represent objects in the world in an understandable form by both men and machines. An ontology consists of concepts and relations as well as properties and axioms [10]. •

The concepts: are concepts (or objects) to the description of a task, function, action, strategy or process of reasoning, etc..

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Relationships: links are organizing concepts to represent a type of interaction between the concepts of a domain. Examples of binary relations are: subconcept of, connected to, kind-of, etc..

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The properties (or attributes) are characteristics related concepts. Thus, an ontology is not only the identification and classification of concepts but also the characteristics attached to them. These features can be valued.

Figure 3.

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The axioms of the ontology used to define the semantics of terms (classes, relationships), their properties and any constraints on their interpretation. They are defined using well-formed formulas of first order logic using the predicates of the ontology.

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Instances are used to represent elements. IV.

PROPOSED APPROACH

Our goal is to design a system of case-based reasoning CBR to help build and operate all the historical accidents from the rail, our current application domain. We present in this section, the architecture of our system and in particular the first part of the embodied approach, the knowledge model. We present the different stages of development of the ontology and also the tool used for querying it. A. General architecture of the proposed approach Several CBR systems architectures have been proposed in the literature. These architectures share more or less the same components. Inspired by these architectures, our system consists of three main parts: an offline process, a base case and an online process (Fig. 3).

System architecture CBR proposed

The offline process includes data from the real terrain and those extracted from domain experts, the formalization of knowledge about the reasoning and ultimately the construction of knowledge model based on ontology. The basis of cases generally includes all instances of the ontology, each instance describes a real accident,

all cases are well capitalized, indexed for the possible retrieval step in the process of reasoning. •

The On-line process is the CBR reasoning cycle: Development, Remembering, Adaptation, enrichment, validation and storage. And a dedicated web interface for the interaction between the expert / user and the system.

B. Offline Process 1) Models of knowledge (Model Case) We were inspired by the work of [11,12] to propose the model of case. One case in our system is railroad accident. It contains two main parts (Fig.4): the part description: PROBLEM-CASE, which describes the general context of the

occurrence of the accident, the dangerous parts, possible causes, the level of risk and potential accident. And part SOLUTION-CASE dedicated to preventive measures and / or corrective to implement in order to prevent future reproduction of the accident. The cases are represented by instances of each concept

Figure 4. Case model proposed

Taking into account that our work is ongoing, the rest of this article will focus only on the first part of our approach materialized; the building of the ontology and the tool used to interrogate it. 2) Steps to build the ontology

access points to find existing concepts such as the concept "RiskManagement" in order to reuse them. We have proposed a method based on the methodology Methontology [12,13,17] and contains the following phases of development (Fig 5):

The ontological engineering does not propose at present any standard method or general methodology for the construction of ontologies, which makes the process of developing ontology long and costly. However, some authors have proposed methodologies inspired by their experience of building ontology [13,14,15,16,17]. These methodologies offer through a set of steps, a cycle of development of ontology that can be adopted during the construction of a new ontology. We also quoted Watson 1 and Falcons 2 ontology that can be considered good

1 2

http://watson.kmi.open.ac.uk/ http://ws.nju.edu.cn/falcons/

Figure 5. Ontology development cycle [11]

a) Specification : This phase is to give a general description of the ontology to be developed in order to agree on key points before beginning development. We distinguish the following two aspects that the designers of the ontology must specify: •

Design principles of ontology that take into account the recommendations such as names standardization, etc.. These recommendations can result in a well structured ontology and consistent with common criteria established by experts.

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The specification of the ontology that describes the purpose, scope, and key concepts (the concepts roots) of the ontology.

2) Usage scenario

Figures 6 and 7 shows the sequence of acquisition of a case. The user can access the main classes, subclasses, including bodies extracted from the ontology. It can select one or more instances possible (leaves) for each subclass. Each selection uses an Ajax function that executes an XQuery code responsible for extracting subclasses, concepts in the selected class or even bodies. The above operation is repeated for all other classes and subclasses to identify all the elements necessary for the description of an accident.

b) Conceptualization This phase is to produce the conceptual model of the ontology that contains domain concepts and their properties. This model must be designed in a form understandable by domain experts (eg glossary of terms, tables, concept hierarchy, etc.).. We distinguish in this phase the two following tasks: •

The definition of concepts: it is to identify the concepts from the resources that were originally specified in the specification phase. We have several documents based on references made by the rail safety experts, more research [18,19,20] and a confidential basis accident scenario [21], result of the work of knowledge acquisition at INRETS, now called IFSTTAR, to extract new concepts. The concepts proposed by this method are then filtered by an expert to keep the relevant concepts only.

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The hierarchy of concepts: it is to organize the concepts in a hierarchy that expresses the subsumption relationship between concepts.

c) Implementation This phase is to move the conceptual model to a model implemented in one of the ontology languages (OWL, OWL Lite). For the implementation of the ontology, we opted for the ontology editor Protégé .

Figure 6. Steps for extracting data from the ontology

Made at this stage, all the sub classes and instances have been selected. It only remains to select the options listed in the ontology.

d) Maintenance This phase is to update the ontology developed by adding, modifying, or deleting concepts or other elements of the ontology. Maintenance of an ontology is very important because it allows it to stay up to date.

C. Online Process (first steps) 1) Query tool of the ontology implemented Although it is interesting to implement tools to interact with ontologies, we must certainly develop these tools for reasoning mechanisms to allow the representation of different types of knowledge (knowledge of terminology, facts, rules and constraints) . The goal is to make operational the ontology in order to manipulate knowledge represented through mechanisms tailored to the target system already defined.

Figure 7. Extraction of instances for the class, possible solutions

V.

CONCLUSION

We presented in this article the first works realized in our approach, including the general architecture of our system of CBR. This architecture consists of two processes, offline and online, using different types of knowledge to solve problems. We have described the model of knowledge, in particular, the field and case relied upon by our system. We presented also the method we followed for the construction of the ontology and the tool used to interrogate and develop the case base. Although the scope of this work is in itself an original, we can also extend the application given the generic and open model of knowledge we have proposed, to other fields of application Such as the road sector, sea sector or even in other sectors. We are currently working on the acquisition of reasoning knowledge to improve developed knowledge model in particular ontology, so as to show the direct impact of this improvement on the process of Case Based Reasoning If we are going to graft in the system. Clearly, it makes sense to study and take advantage of recent work in the field of CBR who have used ontologies in different ways: to describe and structure the cases (Creek) [24], to offer independent models of the domain (CBROnto) [25] CBROnto [26], and [27] to calculate the semantic similarity (FAQFinder ) [28], for the treatment of the heterogeneity of the cases [11] etc.. The goal is to consolidate and strengthen the approach we present through a clear and significant contribution in this study. REFERENCES [1]

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