Comparison of the Nodes of Knowledge method with other graphical methods for knowledge representation Alen Jakupović*, Mile Pavlić**, Ana Meštrović** and Vladan Jovanović*** *
Polytechnic of Rijeka, Business Department, Rijeka, Croatia
[email protected] ** University of Rijeka, Department of Informatics, Rijeka, Croatia
[email protected] [email protected] *** Georgia Southern University, School of Information Technology, Statesboro GA, USA
[email protected]
Summary – One of the research paths in the field of artificial intelligence is knowledge representation. There are different approaches, formalisms, methods and languages. They vary from simple to complex and from less semantically rich to very expressive. In their previous papers, the authors introduced a new method for knowledge representation named Nodes of Knowledge (NOK), bearing the idea that it should be simple but semantically rich. This article presents a brief example of the basic concepts in the NOK method and its comparison with the following methods: Basic Conceptual Graphs, Multi-layered extended semantic networks, Hierarchical Semantic Form and Resource Description Framework. All these methods belong to the same class as the NOK method - graphical methods for knowledge representation.
I.
INTRODUCTION
Knowledge representation and reasoning is a central field within the research of artificial intelligence. It is concerned with how knowledge can be represented symbolically and manipulated in an automated way by reasoning programs [1] and it involves machineinterpretable representation of the world [2]. During the time, many different approaches, formalisms, methods and languages for knowledge representation have been developed. The most influential and the most important approaches are logic schemas, network schemas, frames and rules. In the last two decades, ontologies play an important role in the knowledge representation domain. Different applications need different formalisms and languages that may vary from simple to complex and very expressive knowledge representation formalisms. More expressive (semantically rich) knowledge representation formalisms are easier to use and to understand on the semantic level. The disadvantage of semantically rich formalisms is a more complex inference algorithm. The trade-off between expressiveness and efficiency has to be considered while choosing or creating knowledge representation formalisms. Therefore, drawbacks of existing formalisms and languages are
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either poor expressiveness or high complexity and inefficiency. The authors are introduced a new method for knowledge representation named Nodes of Knowledge (NOK). Its main features are simplicity, the ability of displaying many different types of human knowledge, the ability of automatic detection of new knowledge from existing knowledge, the ability of guided input of new knowledge and the ability of simple creation of user queries over the knowledge base. An example of knowledge represented with the basic concepts in the NOK method is given below. Subsequently, the NOK method is compared with the following graphical methods for knowledge representation: Basic Conceptual Graphs, Multi-layered extended semantic networks, Hierarchical Semantic Form and Resource Description Framework. II.
NODES OF KNOWLEDGE METHOD
NOK uses two elements for graphical representation: nodes and links. Different kinds of nodes are used for representation of terms. Links between nodes enable grouping of terms into more complex expressions. Specially, a process node is introduced as an aggregation point for representing knowledge described in the sentences. Further, an array of interconnected process nodes can represent knowledge expressed in a sequence of sentences. The goal of NOK is to represent a knowledge network of text-based knowledge. Furthermore, the NOK method can capture different types of knowledge: knowledge from dictionaries and encyclopaedias, knowledge from existing databases, knowledge embedded in business processes, knowledge stored in business documents, etc. Figure 1 demonstrates an example of graphical knowledge representation using the NOK method. The following types of knowledge are presented: - according to the contextual node “studies”, the statement “The student studies mathematics” is true
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- according to the process node “studies”, the statement “Marko studies the definition of natural numbers” is true, but here we also have a connection with the process node “takes”, which gives the answer to the question: “Why does Marko study?”. Therefore, the full statement is “Marko studies the definition of natural numbers because he takes”. According to the process node “takes”, the statement “Marko takes the exam” is true. Therefore, combined with the former statement finally we get “Marko studies the definition of natural numbers because Marko takes the exam.” - The existence of three contextual connections demonstrate that the following statements are true: “Marko is a student”, “The definition of natural numbers belongs to the field of mathematics”, “The studying of Marko can be classified as studying of a college student” and “Studying of the definition of natural numbers can be categorized as studying of mathematics”. Finally, the graphical knowledge representation on picture 1 could be translated to the following statements: “A student studies mathematics.” “Marko is a student.” “Marko studies the definition of natural numbers because Marko takes the exam.” “The studying of Marko can be classified as studying of a college student.” “Studying of the definition of natural numbers can be categorized as studying of mathematics.” “The definition of natural numbers belongs to the field of mathematics.” Context nodes
Context links
Links with the role
Process nodes
Regular nodes
Figure 1. Example of graphical knowledge representation using the NOK method
III. COMPARISON OF THE NOK METHOD WITH OTHER METHODS FOR GRAPHICAL KNOWLEDGE REPRESENTATION NOK is a knowledge representation method that belongs to the group of semantic networks. In this chapter, the following methods will be compared with the NOK method: BG (Basic Conceptual Graphs) [3], MULTINET (Multi-layered extended semantic networks) [4], HSF (Hierarchical Semantic Form) [5] an RDF
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(Resource Description Framework) [6]. All methods can graphically represent knowledge, and the resulting model consists of nodes (used to represent concepts) and links between them (used to represent relations between concepts). Various models depicting the following statement will serve as illustrative examples of differences between methods: “Peter finished the discussion”. Figure 2 represents the statement modelled with the NOK method.
Figure 2. Representation of the statement "Peter finished the discussion." using the NOK method
A. NOK vs BG In the BG method, nodes are divided into conceptual and relational and they are linked with connectors. Each node has its name and it is graphically represented with a rectangle (if we are talking about a conceptual node), or an ellipse (if we are talking about a relational node). A relational node connects conceptual nodes and defines the direction in which the model is to be read. The connection between them has a number that determines the order of conceptual nodes in a relation, which is important to know while reading knowledge. In order to represent the same knowledge (e.g. A is close to B) in reverse order it is necessary to introduce a new relational node with a reverse order of numbers. Solely based on the knowledge contained in the statement “Marko is working on a project”, it is not possible to conclude that “On a project Marko is working”, without specifying it separately. In the NOK method, the process node is analogous to the relational node of the BG method. Similar to the BG method, a process node connects conceptual nodes. The NOK method, unlike the BG method, does not have number on connections. Instead of numbers, each connection has a role – the name of the role is a simple question. Thanks to this feature, reading is not associated with the order of appearance of a concept node in the relation, but it can be started from any node, but with the application of the corresponding link role, i.e. question. A very important concept that characterizes the NOK method and can’t be found in the BG method is the connection between process nodes. This feature makes it possible to represent the execution order of process nodes, their logical moves, their conditioning etc. The BG method has a concept for the representation of generalization/specialization (arrow) between nodes. However, generalization/specialization can be also specified in the name of a specialized node by indicating the name of the generalized node to which it belongs. NOK enables grouping or generalizations through contextual nodes and their connections. BG allows subnets, so that a group of nodes is declared a new node (an aggregation of nodes) and is connected to other nodes. In NOK there are no subnets as aggregations of nodes and connections are made by connecting any
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nodes. Knowledge about relationships on a higher level is achieved by connecting context nodes. Figure 3 represents the statement “Peter finished the discussion”, modeled using the BG method.
Figure 3. Representation of the statement "Peter finished the discussion." using the BG method
Numbers on connections reveal the order of conceptual nodes in the relation “Finish”. Based on this model, the following statements can be read: “Peter finish ?”, “Peter finish discussion.”, “? finish discussion” and “? finish ?”. It is not possible to read statements such as “Peter ?”, “? discussion” and “Peter ? discussion.”. Based on the model that was made using the NOK method (Figure 2), it is possible to read statements such as these and statements that were derived from the model. Therefore, the set of statements that can be read from the NOK-based model is a superset of statements that can be made from the BG-based model. B. NOK vs MULTINET In the MULTINET method, each node belongs to a class from a predefined classification of nodes called conceptual ontology (a collection of 29 classes). In the NOK method the number of classifications is not limited and the user can add them freely based on needs and field of use. Each node has 7 predefined attributes, the values of which position it in the semantic space (this approach is motivated by the analogy with a point in the Euclidian space). NOK enables free addition of attributes and only the identifying attribute is necessary. Connections between nodes are made using one of 89 predefined connection types. 16 of them have the socalled cognitive function and those connections describe the relation between the major participants in a given situation. In the NOK method, the connections are established using special process nodes that can be predefined as context or new nodes used to describe current knowledge. The method does not graphically distinguish between node types as the NOK method does. The MULTINET method is extremely complex due to a large number of elements that need to be defined for each node (it has be assigned to one of 29 classes, values of 7 attributes have to be assigned and while connecting it, it is necessary to pick an adequate connection type among 89 categories). Figure 4 represents the statement “Peter finished the discussion.” that is modelled using the MULTINET method.
Figure 4. Representation of the statement "Peter finished the discussion." using the MULTINET method [4]
The nodes Peter, finish and discussion are visible on the model, and they belong to one of the classes of the predefined conceptual ontology. The model does not show the values of 7 attributes that belong to each node, but the connections (arrows) are visible, as well as their type. The connections AGT and AFF have cognitive roles and stand for: agent and affected. SUBS is a connection that indicates that situational concepts finish and discussion are subordinate to their related general concepts. [SORT = ad] points out the fact that the agent Peter influences the entity that belongs to the group of abstract objects (strictly speaking, to the group of dynamic abstractions). Based on the model that is made using the MULTINET method it is possible to create all statements that can also be created using the NOK method. However, it needs to be pointed out that the MULTINET method is an extremely complex method that is primarily used to model aspects of natural languages. C. NOK vs HSF The HSF method consists of two basic concepts: a group and a link. These two concepts are graphically represented by circles (empty circle for groups, filled circles for links) that are mutually connected with arrows (to indicate the proper reading direction). The concept of groups is used to represent a specific sign, group of signs, word, semantic categories and complex patterns. It is used in order to represent the sequence of selected terms on various levels of abstraction (a group represents a link to the first element in a sequence). The same group can occur on different levels of abstraction. Simply speaking, the concept of groups represents an aggregation of facts/knowledge, starting with the lowest level – signs. In the NOK method, context nodes represent aggregated knowledge. However, it is not clear how the HSF method represents knowledge that was created by generalizing knowledge on a lower level of abstraction. For example, based on the statements “The student Marko studies mathematics.” and “The student Ana studies mathematics.” a generalized statement such as “A student studies mathematics.” could be made. This statement can be easily represented in the NOK method using context nodes which are further connected to the original statements by context links. The concept of links is used in the creation of a sequence of signs, group of signs, words, semantic categories etc. on various levels of abstraction. For example, for the pattern “Student”, the connection should represent the exact order of each sign in the pattern. It would be similar for words, sentences etc. In the NOK
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method, sequence is represented by links between process nodes. It is possible to use such a sequence to represent a pattern (group) of signs, but the goal in using the NOK method is primarily to represent concepts that contain minimal knowledge. The HSF method adds semantics to a certain sequence of terms by connecting them to a group concept that contains semantic description within itself (e.g. “is part” or “part of day”, etc.). In the NOK method, semantics of a certain sequence of concept that is represented by nodes and links (i.e. parts of a network) is represented by a sequence of context nodes (i.e. by an adequate network on a higher level of abstraction). The HSF method uses the SOUL algorithm that gives it the ability of learning new patterns. A future research path in the NOK method is the development of an algorithm that would be able to generalize knowledge on a higher level of abstraction from facts/knowledge on a lower level of abstraction (analogous to the induction method). Moreover, a possible future research path is the use of existing generalized knowledge already in a NOK model in order to add new knowledge on a lower level of abstraction (analogous to the deduction method). Figure 5 represents the statement “Peter finished the discussion.”, which is modelled using the HSF method.
D. NOK vs RDF The RDF method represents the relation between web resources by using a named attribute and its value. Examples of web resources include various data, documents, images etc. A named attribute refers to a specific resource. The name of the attribute is defined using a Uniform Resource Identifier (URI), so that the attribute itself can be considered as a new resource. The value of the attribute can be another resource or other data (e.g. string, number, date etc.). The RDF method has many display modes, one of them being the RDF graph. It consists of three basic concepts: a node representing a resource, a node representing data and an arrowed line that connects nodes. An ellipsis graphically represents a resource, while a rectangle graphically represents data. An arrowed line represents a property and the property name is written above it. The arrow indicates the property value. Since resources have properties, on the arrowless side of the line there is always an ellipsis (a resource node), while on the other side of the line there is always either an ellipsis or a rectangle (i.e. a resource node or data). Using the RDF method, structures of the type subject, object and predicate can be represented. Statements of this type can also be represented using the NOK method with nodes, process nodes and links between them. The RDF method cannot represent the sequence of execution of a process or logical constraints. The NOK method solves this problem using process nodes and process links. Similar to the NOK method, generalization/specialization can be represented using the RDF method (property “is part of” or “is of the type”). The RDF method makes a distinction between two types of nodes – resource and data, while the name of the attribute is written above the line. In the NOK method all these three concepts are represented by nodes. There is no link or role name in the RDF method. Figure 6 represents the statement “Peter finished the discussion.”, modelled using the RDF method.
Figure 5. Representation of the statement "Peter finished he discussion." using the HSF method
From the model made using the HSF method, it is visible that the source statement is fully preserved (contrary to the NOK method where the statement is read as “Peter finish discussion”). It is clear that nodes represent specific signs, sequences of signs, and sentences. The correct direction of reading the model is guided by arrows – therefore it is possible to read it only in one direction which starts by the node on the highest level of abstraction (in the example above it is the black node on the top of the figure). In the NOK method, it is possible to start reading it from any node due to the existence of link role (i.e. the question).
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Figure 6. Representation of the statement "Peter finished the discussion." using the RDF method
The first model assumes that there is a web resource (internet page) that describes Peter. Finished is a property of this resource, and the value is discussion. In the second model, apart from the assumption that there is a web resource that describes Peter, it is also assumed that there is a web resource that describes the discussion. In this example, the first web resource has the property finished, the value of which is the second web resource. In the NOK method web resources could be represented by concept nodes, but their property would be represented by process nodes. An RDF model can be represented using a mathematical formalism as an arranged triad (predicate,
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subject, object). Therefore, based on an RDF model, statements can be made that can also be made based on a NOK model, but the missing part is the role of a link, which gives it semantics. IV.
CONCLUSION
Based on the comparison of the NOK method with other methods, namely BG, MULTINET, HSF and RDF methods, the following characteristics of the NOK method can be highlighted: 1.
simplicity – the NOK method has two basic elements (node and link). There are three different types of nodes (regular, context and process), and two link types (regular and context). Each link has its role (the question), which is used in reading the modelled knowledge.
2.
expressiveness – the NOK method enables to represent knowledge on various levels of abstraction. The lowest level of abstraction represents relations between facts which are presented by regular and process nodes and their mutual relationships. Knowledge on higher levels of abstraction are represented by context and process nodes. The relationship between knowledge on higher and lower levels of abstraction is achieved through the use of context links.
3.
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Simplicity of reading – in the NOK method it is possible to start reading knowledge from any node using links roles. Therefore, the same knowledge can be reached in various ways (e.g. from the same NOK model, the following statements can be read: “Marko studies
mathematics” and “Mathematics is studied by Marko”.). The abovementioned characteristics of the NOK method will enable its simple implementation and its widespread usability. As a demonstration of its practical use, the authors of this paper started a project in order to demonstrate the possibilities in using the NOK method in business systems. It could be used in the creation of a knowledge base from an existing database. Further paths in the research of this method include its mathematical formalization, the creation of algorithms that can conclude and suggest new knowledge from existing knowledge (using the induction method), the creation of algorithms of guided input of new knowledge on the basis of existing knowledge (use of the deduction method), and the creation of a user interface which is based on human written language. REFERENCES [1]
[2]
[3] [4] [5]
[6]
R. Brachman, H. Levesque, "Knowledge representation and reasoning", The Morgan Kaufmann Series in Artificial Intelligence, Morgan Kaufmann, 2004. S. Grimm, P. Hitzler, A. Abecker, "Knowledge representation and ontologies logic", Ontologies and Semantic Web Languages, 2007. M. Chein, M. L. Mugnier, "Graph-based knowledge representation", Springer, London 2009. H. Helbig, "Knowledge representation and the semantics of natural language", Springer, London, 2006. M. Stanojević, S. Vraneš, "Knowledge representation with SOUL", Expert Systems with Applications, vol. 33, 2007; pp. 122134. K. Tolle, "Introduction to RDF", 2000., , 12.10.2012.
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