Combined Resources

These resources combine a set of autonomous resources with some enrich-ment resources into standalone resources. For example, a parallel corpora or a comparable corpora is a resource of this kind since it contains doc-uments (autonomous) and alignments between their content (enrichment) [Wallis & Nelson 2001]. Semantic hypertexts are also combined resources combining linguistic resources indexed by terms or concepts from termino-logical or ontotermino-logical resources (e.g., Wikipedia). Large biomedical ontolo-gies may result from merging different vocabularies or terminoloontolo-gies using alignments between them.

3.4 Discussion

In the second chapter of the book “In Search of an Integrative Vision for Tech-nology” [Strijbos & Basden 2006], Andrew Basden discussed the facilitates to consider for easily representing knowledge. He defined some characteris-tics of knowledge representation aspects such as irreducibility (aspects are equally important), dependency (aspects are dependent from each other but not reducible) and non-absoluteness (aspects are not absolute individually in

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terms of foundation of meaning and all aspects taken together are not abso-lute in general). The author states that aspects of knowledge may be derived from Herman Dooyeweerd’s ontology of modal aspects [Basden 2002]:

• items: the analytical aspect of representing knowledge (concepts);

• relationships: the structural aspect that defines relationships between the items;

• values: the quantitative aspect of knowledge;

• spatial: the spatial aspect that defines the extension of knowledge;

• text: the lingual aspect that defines signification of knowledge;

The interesting fact about Basden’s discussion is the claim that “Modal aspects are irreductibe spheres of meaning that are more than categories;

they enable being, doing, relating, properties, norms, and each of these in-dicate a difference portion of KRF.” [Strijbos & Basden 2006, p. 37]. The conclusion of the author is that a proper knowledge representation approach should create a knowledge representation formalism for each aspect and then integrate them together.

In this chapter we defined a categorization of knowledge resources based on the generic aspects such as autonomy, type of content, schemas, etc [Ghoula et al.2010c]. We also presented some knowledge resources repre-sentation models that are close in terms of reprerepre-sentation to the model that we intend to build. None of the proposed models can be adopted as such for resources representation since each of these models lacks a required com-ponent for representing all the kinds of knowledge that we consider (formal, conceptual, terminological and lexical all combined).

We will use this claim as a principle for the knowledge representation model that we will propose for representing heterogeneous knowledge sources [Ghoula 2012]. Consequently and based on a restriction of the re-sources representation aspects we intend to focus on representing conceptual, terminological, and lexical knowledge. Then the aspects that we are consid-ering for building a model for representing knowledge resources are:

• type of entities to represent (concepts, terms, lexical forms, etc.);

• types of relationships between items (structure of the resources);

• types of knowledge resources: the ability to represent knowledge based on different formalisms;

T OK : A meta-model for representing heterogeneous knowledge resources

Contents

4.1 Resources representation aspects for designing the resources model . . . . 46 4.2 Resources representation model: TOK_Onto . . . . 48 4.2.1 Metadata representation . . . . 48 4.2.2 Resources content representation model . . . . 50 4.2.3 The modeling approach of TOK_Onto. . . . 56 4.2.4 Example of using the model to represent WordNet . . 59 4.3 Representing resources management . . . . 60 4.3.1 Resources engineering operators representation . . . . 61 4.3.2 Process monitoring representation . . . . 62 4.3.3 Resources evolution-tracking . . . . 63 4.4 Use case scenario . . . . 64 4.5 Discussion . . . . 66

In this chapter we describe our approach of representing different types of terminological, ontological, and linguistic knowledge resources. This ap-proach leads to a common representation formalism that serves as a model of a centralized knowledge repository. We first introduce our meta-model for representing the content of heterogeneous resources. Finally we show some scenarios for using the representation approach that we propose. We de-scribe T OK, a meta-model for representing knowledge resources of different types. We choose this name as a reference toTerminological andOntological Knowledge resources.

4.1 Resources representation aspects for designing the resources model

In order to design our model, we first identified and categorized the resources that we represent. Then, for each type of these resources we investigated their structure, entities and semantics. For defining and validating T OK, we followed an iterative process by trying different abstract models, testing them on resources from each category and then refining them or modifying them depending on their capacity of representing all the types of resources.

To properly describe and use these heterogeneous resources we need to define their lifecycle within the repository (see figure 4.1). A resource is imported using a specific operator that represents its content using our model and stores it into the repository. Any resource can be involved in different knowledge engineering operations that generate new knowledge resources.

Resource

import

Resource  

Representa-on operations Resource

Representation

Derived Resource Resources  Meta-­‐model

export

instance of

Figure 4.1: Lifecycle of a TOK resources within the repository Resources representation within a knowledge repository does not only involve metadata and content but also other aspects such as resources’ evo-lution and involvement in different knowledge engineering tasks. After mul-tiple versions and conducting several changes, we identified the structure of the resources representation model (figure 4.2). This model is an aggregation of five components:

1. Resources Metadata and Content: Representing resources within the same repository requires to design a model that is able to describe the metadataand content of these resources in a generic way with a decidable consistency (described in section 4.2).

2. Knowledge Operators Representation: Knowledge engineering operators within the repository are represented using a dedicated model and may have multiple implementations and different kinds of input and output (described in section 4.3).

3. Evolution Representation: Knowledge resources within a reposi-tory are subject to change and evolution, which might be problematic if not monitored within the repository. This is why we created a model to represent the types of changes that may occur during each resource’s lifecycle (described in section 4.3).

4. Process Execution Monitoring: This aspect uses the resources and the operators’ representations to store each execution of any kind of operation that involves specific resources within the repository. This is useful to re-execute a process each time a change occurs in an operator or a resource that have been involved in it (described in section 4.3).

5. Change Execution Monitoring: The second component uses the evolution model and the operators model to monitor the changes on each resource during its lifecycle (described in section 4.3).

Model of TOK resources

Evolution Representation Knowledge Operators

Representation

Processes Execution Monitoring Resources Representation

Change Execution Monitoring Uses

Uses

triggers

Uses Uses

Figure 4.2: Resources representation aspects and interactions

The representation of common metadata elements in resources and the definition of axioms on these elements is a key for their interoperability. Our approach defines a representation model that is able to:

• represent existing knowledge representation formalisms and integrate different models;

• represent resources metadata and content;

• create new knowledge representation models that are convenient for specific tasks, e.g. Concept hierarchy, or WordNet-like;

• define import/export and model mapping (transformation) operations;

• represent operations, knowledge engineering processes, etc.