The Evolution of Knowledge Representation within Embrapa's Information Agency

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1 The Evolution of Knowledge Representation within Embrapa's Information Agency Kleber X. S. de Souza a, Joseph Davis b, Silvio R. M. Evangelista a, Marcia I. F. Souza a, M. F. Moura a and Adriana D. dos Santos a a Embrapa Information Technology, Caixa Postal 6041, Campinas, SP, Brazil, {kleber,silvio,marcia,adriana}@cnptia.embrapa.br b School of Information Technologies, The University of Sydney, NSW 2006, Sydney, Australia, jdavis@it.usyd.edu.au Abstract Knowledge representation has always been a major concern when designing systems for technology transfer because of its impact on the system's usability. The Embrapa's Information Agency was designed as a space for exchanging knowledge among farmers, agricultural researchers and rural extension technicians. In its first version, the system represented the knowledge through ontologies realized as trees displayed in hyperbolic space. This strategy, although appropriate for the users of the system, posed some difficulties when comparing ontologies developed for sub-domains of the agricultural domain. This fact, motivated the search for a new representation that should have a good usability whereas allowing such comparisons. Key words: knowledge representation, ontology, lattice. 1 Introduction The Information Agency (Souza et al, 2004), a project we worked on, produced separate but overlapping ontologies in sub-domains of the agricultural domain, such as: dairy cattle, beef cattle, sheep and beans, among others. The implemented system is, in general terms, a context management system (CMS) with an associated visualization in hyperbolic tree. Information is organized in the system using two criteria. Firstly, every resource is cataloged in accordance to Dublin Core Metadata Standard (Dublin Core Metadata Initiative, 2005). Secondly, a set of terms belonging to the universe of discourse of farmers, rural extension technicians and researchers is identified. Those terms were organized in the form of a tree and embedded in the system's navigational structure, which provides an option for hyperbolic visualization (Lamping et al., 1995), illustrated in Fig. 1. The benefit of the hyperbolic representation is that it combines the advantages of the tree layout, which provides the user with reference points during navigation, with hyperbolic distortion, which amplifies the region that the user is focusing, reducing the peripheral region. In this way, the hyperbolic tree provides a natural balance between the exponential growth commonly found in trees and the limited amount of space available for their visualization. Ontology, according to the definition proposed by Hopsapple and Joshi (2002), is an explicit specification of an abstract view of a world that one desires to represent. However, the formalization of ontologies, proposed by the W3C (World Wide Web Consortium) in the RDF (Resource Description Framework), describes data as a set of statements of the form (subject, predicate, object), which can be viewed as a directed graph (World Wide Web Consortium, 2003). 464

2 Fig.1 Hyperbolic tree visualization of the dairy cattle Information Agency. The main difference between the two definitions is that the second is more restrictive, because it imposes a structure to the data being represented. In the Information Agency, the knowledge tree can be viewed as an ontology in the first sense, because the explicit specification organized the terms identified in the universe of discourse in a tree structure, instead of a graph. The fact that the Information Agency adopted a tree instead of a directed graph to represent its ontologies simplified the construction of the ontologies, but also implied the elimination of certain relations that could only be expressed in a richer model (graph). This article discusses the issues involved in the simplification above mentioned and proposes a new model for the system based on Formal Concept Analysis (Wille, 1982), a data analysis technique based on lattice theory. 2 Design Issues When the Information Agency Project started in 2000, the Semantic Web was in its infancy. The RDF model was still a candidate specification. As a consequence, the complete Semantic Web recommendation was not available to be used as a basis for the construction of the system. Nevertheless, the proposition of constructing knowledge based systems using predicates of type (subject, predicate, object) had long been proposed in literature. The first issue addressed while modeling the system was that the relations between objects had to be visualized in a scalable way. This created a problem, because triples (subject, predicate, object) require graph visualization. It is well known that graph visualization becomes difficult when the number of nodes increase (for details, please see Section 4). The knowledge engineers had to explain to agricultural researchers and rural extension technicians how 465

3 to represent the knowledge in a simple and straightforward way, in such a way that they should be able to visualize the whole ontology during its design. It was then decided that a tree would be used to represent ontologies in that first version of the project. Users are accustomed to deal with trees because they are the main form of navigation in information embedded in most computational systems. Naturally, this decision brought some problems, because ideally information should be represented as graphs and trees are more restrictive as a representation. For example, if the object X in Fig.2 has to inherit the properties a and c, one has to decide either to draw the tree either descending from a or descending from c because they are in the same level. In knowledge representation that meant that one had to choose the most relevant attribute to represent that object and link the object to that attribute. Now, that the basic design issues are known by the actors involved in the construction of the system, we decided to design the future version using Gallois Lattices, a lattice built in accordance to the formalism proposed by Formal Concept Analysis, discussed in the next section. 3 Applying Formal Concept Analysis Formal Concept Analysis (FCA) is a technique based on lattice theory and propositional calculus, producing what is called a Concept Lattice. FCA has been applied in many domains, such as structuring of information systems, knowledge discovery in databases, political science and psychology. Due to space limitations, we cannot provide in this paper details of the application of FCA, because it involves a great amount of mathematics. Please see (Souza and Davis, 2004) for further information. We will give a brief explanation of the process. FCA involves the analysis of a set of attributes A, a set of objects O which contain these attributes, and a binary relation R between O and A. Let a, b and c be attributes of two objects, X and Y. The matrix in Table 1 shows the relation R by marking with an x whenever the object has one of the attributes. Table 1 Objects and attributes represented in the lattice of Fig. 2. attributes objects a b c X x x x Y x x FCA analyzes which subsets of the set of objects have the same attributes and, conversely, which subset of attributes is shared by the same objects. For example, the subset {a,c} is present both in X and in Y, and b exists only in X. So, {a,c} is related to {X,Y} and {a,b,c} is related to {X}. Fig. 2 A graph (left) and its corresponding spanning tree (right). 466

4 In Formal Concept Analysis, the abstraction of concepts present in human thoughts, in which concepts are classes of things having certain attributes, is structured in a lattice, the concept lattice. In this lattice, if a concept A is above a concept B, and the two are linked, concept A is more general than B and, as being such, it carries part of attributes of B. As a consequence, we can say that whenever B happens, A is also happening, which suggests a logical entailment. In the lattice, we can not only see a hierarchy of concepts, but also the whole set of binary relations present among concepts. That makes the visual analysis of data superior to the one we can obtain by looking at a hierarchy of classes. In Fig. 2, every node in the graph is a concept. In the concept lattice of Fig. 2, we interpret a, b and c as attributes of an object X, because they are positioned in nodes from the node labeled X up to the root node. Y has also attributes a and c, but not b. In this, way, we can say that X shares two attributes with Y and that it has one attribute that Y does not have. Using this information, the similarity evaluation is performed. If two objects are positioned in the same node (concept), they have the same attributes and are, therefore, instances of the same class of objects that have that set of attributes. The number of attributes in common can then be weighted against the number of attributes that are present only in one of the objects to measure the similarity between two objects. Having defined the concept lattice, the idea was to compare different nodes in different ontologies, provided they were positioned in the same lattice. If we could say that two nodes were similar, perhaps the information cataloged in one node could be linked to the other node as well. However, this could not be realized. If we take two ontologies, namely Beef Cattle and Dairy Cattle, the hierarchies designed for them showed that the same term had been used with different meanings, and positioned in different places in the hierarchy. Therefore, we could not find a single position that would satisfy both ontologies. The main problem was with the ordering of the concepts. We had to find and ordering system that would allow us to compare ontologies. Fortunately, all the information contained in the system had also been cataloged using a thesaurus, namely Agrovoc (FAO, 1995). The ordering provided by the thesaurus was used in conjunction with FCA to identify the precise meaning of a given node. The definition of thesaurus used in this paper is that a thesaurus is a set of terms organized in accordance to a partial ordering. This partial ordering distributes the terms into many sub-trees, each of them containing the term's definition, its super-concept and eventual sub-concepts. Fig. 3 shows the Hasse Diagram of the lattice generated by FCA. The names near each node correspond to thesaurus terms and the names in boxes are objects of ontologies Beef Cattle (A) and Dairy Cattle (B). In the lattice we can see that the object A_pasture_usage is linked upwards to growth, beef cattle, developmental stages, feeding systems, intensive husbandry, grazing systems and animal husbandry methods. With the positioning provided by the FCA technique associated with the thesaurus we could then evaluate how close each concept was to other concepts in the same or in other ontologies. However, there still remained the visualization problem. As each lattice is a graph, and usually the ontologies designed have more then 200 nodes, graph visualization becomes an issue again. 4 Graphs and Trees The concept lattice is a type of graph. Many books and articles deal with the problem of finding the best rendering for graphs, trying to reduce edge crossings. For an excellent review on the subject, focusing specifically on visualization applied to navigation in information, please refer to Herman et al. (2000). Even when we find a good visualization from the aesthetics point of view, there still remains one problem that in most cases cannot be eliminated: cluttering. In a cluttered graph, we cannot distinguish clearly one node from the other and we cannot read the information contained in the labels attached to each node. 467

5 Fig. 3 FCA lattice showing the relation between nodes of Dairy Cattle (B) and Beef Cattle (A) ontologies. Herman et al. (2000) pointed out that the usability problem becomes an issue even when we are still able to identify all the nodes and edges displayed. One way to circumvent the cluttering problem is to display a spanning tree of the graph, instead of the graph itself. Figure 2 shows an example of a graph and its corresponding spanning tree. In this way, every graph can be displayed through its spanning tree. When associated with hyperbolic visualization, the spanning trees allow for the visualization of large graphs. That was the decision adopted for the Information Agency. However, constructing the spanning tree also poses a problem, viz. determining the best paths to be represented having in mind the navigation by the end user of the system. This problem was solved through measures of similarity using the amount of attributes shared in the concept lattice. In this way, an object would be linked to the parent node having the highest similarity value. To improve even more the navigation, we also added new information to the hyperbolic tree. Whenever an edge was eliminated, we indicated this with a label indicating the point where the link was broken. Fig. 4 shows this enhanced spanning tree. Node labeled 1 indicates that there was a link from node c to node 1 in the other branch of the tree. In the actual hyperbolic tree, we use different colors to identify such nodes. 5 New Representation for Information With the visualization problem solved, we decided to propose a new form for information representation based on lattice structure. This form has an impact on the way ontologies are elaborated by each group. First, we had to ensure that the partial ordering of the thesaurus was appropriately embedded in the method of construction of individual ontologies. Second, each group should be able to choose the best spanning tree for the generated lattice. The steps involved in the creation of a new ontology are the following: 1. Classify each node by choosing the appropriate terms form a thesaurus. The thesaurus can be chosen freely, provided it is used by every ontology to be compared, e.g. those with closely related domains; 2. Place the selected terms of the thesaurus appropriately either as a sub-node of an existing node, as a new node linked to the root node, or as a new node of an existing branch. Remember that thesaurus terms have broader terms and narrower terms. This requires a correct placing vis-à-vis the existing branches linked to the root node; 3. Link every new object to the nodes representing the thesaurus terms, either as a real node or as a link (as in Fig. 4). 468

6 Fig. 4 Spanning tree with broken link information. This construction method is being tested with new ontologies. The hyperbolic editor was adapted to support the creation of virtual links, in such a way that we are able to jump over the broken links, making the task of ontology creation more pleasant and organized. 6 Conclusion In this paper we discussed the enhancements being made in the Information Agency, a project developed by the Brazilian Agricultural Research Corporation (Embrapa). The focus of the discussion was the knowledge representation (ontology) embedded in the system's navigational structure. The existing ontology is realized as a tree of concepts displayed in hyperbolic space. This representation is being evolved to a lattice of concepts obtained by the application of the Formal Concept Analysis technique. Using the concept lattice, we are able to compare concepts and evaluate their similarities. To avoid the cluttering problem, we also propose that the lattice be visualized through its spanning tree. 7 References Dublin Core Metadata Initiative Dublin core metadata element set, version 1.1: reference description. Available at < Accessed 05 Jan FAO AGROVOC: Multilingual Agricultural Thesaurus. FAO, Rome. Herman, I., Melançon, G., Marshall, M.S Graph visualization and navigation in information visualization: A survey. IEEE Transactions on Visualization and Computer Graphics 6, Holpsapple, C.; Joshi, K. D A collaborative approach to ontology design. Communications of the ACM 45(2), Lamping, J., Rao, R., Pirolli, P A focus+context technique based on hyperbolic geometry for visualizing large hierarchies. In: Katz, I.R., Mack, R., Marks, L., Rosson, M.B., Nielsen, J. (Eds.). Proceedings of the Conference on Human Factors in Computing Systems (CHI'95), Denver, CO, USA, ACM Press, pp Souza, K.X.S., Davis, J Aligning ontologies and evaluating concept similarities. In: On The Move to Meaningful Internet Systems 2004: CoopIS, DOA, and ODBASE, Lanarca, Cyprus. Proceedings. Number 3291 in Lecture Notes in Computer Science, Springer-Verlag Heidelberg, pp Souza, K.X.S., Davis, J.; Souza, M.I.F Organizing information for the agribusiness sector: Embrapa's Information Agency. In: Proceedings of 2004 International Conference on Digital Archive Technologies, Taipei, Taiwan, Institute of Information Science - Academia Sinica, pp Wille, R Restructuring lattice theory: An approach based on hierarchies of concepts. In: Rival, I. (Ed.). Ordered Sets. Volume 83 of NATO Advanced Study Institute Series C. Reidel, Dordrecht, pp World Wide Web Consortium Resource Description Framework (RDF) Concepts and Abstract Syntax. W3C Working Draft 23 January Available at < Acessed 05 Jan