Ontologies Description and Applications

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Ontologies_Description_and_Applications(cont.)

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Ontologies – Description and Applications

Marek Obitko obitko@labe.felk.cvut.cz February 22, 2001

Abstract

The word “ontology” has gained a good popularity within the AI community. Ontology is usually viewed as a high-level description con- sisting of concepts that organize the upper parts of the knowledge base. However, meaning of the term “ontology” tends to be a bit vague, as the term is used in di‹erent ways. In this paper we will attempt to clarify the meaning of the ontology including the philosophical views and show why ontologies are useful and important. We will give an overview of ontology structures in several particular systems. A field proposed within ontological e‹orts, “ontological engi- neering”, will be also described. Usage of ontologies in several particular ways will be discussed. These include systems and ideas to support knowledge base sharing and reuse, both for computers and humans, ontology based communication in multi- agent systems, applications of ontologies for natural language processing, applications in documents search and enrichment of knowledge bases, both particularly for the World Wide Web environment and construction of educational systems, particularly intelligent tutoring systems.

Contents

1 Introduction 3 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Philosophical View . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 What is an Ontology? 4
2.1 Common Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1.1 Ontology as a Philosophical Term . . . . . . . . . . . . . . . . . . . . 5
2.1.2 Ontology as a Specification of Conceptualization . . . . . . . . . . . . 5
2.1.3 Ontology as a Representational Vocabulary . . . . . . . . . . . . . . . 6
2.1.4 Ontology as a Body of Knowledge . . . . . . . . . . . . . . . . . . . . 7
2.2 Other Ontology
Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3 Ontology Structure 9
3.1 CYC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.2 Russell & Norvig’s General Ontology . . . . . . . . . . . . . . . . . . . . . . . 10
3.3 Ontology Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.3.1 Structure of Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.3.2 Ontology Engineering Subfields . . . . . . . . . . . . . . . . . . . . . . 12
4 Using Ontologies 14
4.1 Top-Level Ontology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.2 Knowledge Sharing and Reuse . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.2.1 KIF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.2.2 Ontolingua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.2.3 Collaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.2.4 Particular Ontologies Reuse . . . . . . . . . . . . . . . . . . . . . . . . 18
4.3 Communication in Multi-Agent Systems . . . . . . . . . . . . . . . . . . . . . 18
4.3.1 FIPA Agent Management Model . . . . . . . . . . . . . . . . . . . . . 19
4.3.2 Ontology Service by FIPA . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.3.3 Ontologies Relationships . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.3.4 FIPA Knowledge Model and Meta-Ontology . . . . . . . . . . . . . . . 22
4.4 Natural Language Understanding . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.4.1 CYC NLP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.4.2 WordNet Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.5 Document Search and Ontologies . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.5.1 OntoSeek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.5.2 WebKB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.5.3 Knowledge Representation Techniques . . . . . . . . . . . . . . . . . . 26
4.5.4 Document Enrichment . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.6 Educational Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.6.1 EON . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.6.2 ABITS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.6.3 Other Proposals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
5 Conclusion 30

1 Introduction The word “ontology” has gained a good popularity within the AI community. Ontology is usually viewed as a high-level description consisting of concepts that organize the upper parts of the knowledge base. However, meaning of the term “ontology” tends to be a bit vague, as the term is used in di‹erent ways. In this paper we will attempt to clarify the meaning of the ontology and show why ontologies are useful and important. We will discuss usage of ontologies in several particular ways, such as knowledge base reuse, knowledge sharing, communication in multi-agent systems, applications of ontologies for WWW applications, for natural language processing, and for intelligent tutoring systems. 1.1 Motivation In AI research history, we can identify two types of research [31, 8]. One is form-oriented research (mechanism theories) and the other is content-oriented research (content theories). The former deals with logic and knowledge representation while the latter with content of knowledge. Apparently, the former has dominated AI research up to date. Recently, however, content-oriented research has become to gather much attention because a lot of real-world problems to solve such as knowledge reuse, facilitation of agent communication, media in- tegration through understanding, large-scale knowledge bases, and so on, require not only advanced theories or reasoning methods, but also sophisticated treatment of the content of knowledge. Formal theories such as predicate logic provides us with a powerful tool to guarantee sound reasoning and thinking. It even enables us to discuss the limits of our reasoning in a principled way. However, it cannot answer to any of the questions such as what knowledge we should have for solving given problems, what is knowledge at all, what properties a specific knowledge has, and so on. Sometimes, the AI community gets excited by some mechanisms such as neural nets, fuzzy logic, genetic algorithms, constraint propagation etc. These mechanisms are proposed as the “secret” of making intelligent machines. At other times, it is realized that, however wonderful the mechanism, it cannot do much without a good content theory of the domain on which it is to work. Moreover, we often recognize that once a good content theory is available, many di‹erent mechanisms might be used equally well to implement e‹ective systems, all using essentially the same content. Importance of content-oriented research is being recognized more and more nowadays. Unfortunately it seems that there are no widely recognized sophisticated methodologies for content-oriented research now. Major results till later years were only development of knowl- edge bases. The reasons for this can be [31]:  content-oriented research tends to be ad-hoc  there is no methodology that enables to accumulate research results It is necessary to overcome these diŽculties in the content-oriented research. Ontologies are proposed for that purpose. Ontology engineering, as proposed in e.g. [31], is a research methodology which gives us design rationale of a knowledge base, kernel conceptualization of the world of interest, strict definition of basic meanings of basic concepts together with sophis- ticated theories and technologies enabling accumulation of knowledge which is dispensable for modeling the real world.

Interest in ontologies has also grown as researchers and system developers have become more interested in reusing or sharing knowledge across systems. Currently, one key imped- iment to sharing knowledge is that di‹erent systems use di‹erent concepts and terms for describing domains. These di‹erences make it diŽcult to take knowledge out of one system and use it in another. If we could develop ontologies that could be used as the basis for multi- ple systems, they would share a common terminology that would facilitate sharing and reuse. Developing such reusable ontologies is an important goal of ontology research. Similarly, if we could develop tools that would support merging ontologies and translating between them, sharing would be possible even between systems based on di‹erent ontologies. 1.2 Philosophical View The term ontology was taken from philosophy. According toWebster’s Dictionary an ontology is  a branch of metaphysics relating to the nature and relations of being  a particular theory about the nature of being or the kinds of existence Ontology (the “science of being”) is a word, like metaphysics, that is used in many di‹erent senses. It is sometimes considered to be identical to metaphysics, but we prefer to use it in a more specific sense, as that part of metaphysics that specifies the most fundamental categories of existence, the elementary substances or structures out of which the world is made. Ontology will thus analyze the most general and abstract concepts or distinctions that underlay every more specific description of any phenomenon in the world, e.g. time, space, matter, process, cause and e‹ect, system. Recently, the term of “ontology” has been up taken by researchers in Artificial Intelligence, who use it to designate the building blocks out of which models of the world are made. An agent (e.g. an autonomous robot) using a particular model will only be able to perceive that part of the world that his ontology is able to represent. In this sense, only the things in his ontology can exist for that agent. In that way, an ontology becomes the basic level of a knowledge representation scheme. An example is set of link types for a semantic network representation which is based on a set of ”ontological” distinctions: changing–invariant, and general–specific. 2 What is an Ontology? The term “ontology” is used in many di‹erent ways. In this section we will discuss what an ontology is on several definitions that are currently used. 2.1 Common Definitions The most widespread definitions of ontology are given below. 1. Ontology is a term in philosophy and its meaning is “theory of existence”. 2. Ontology is an explicit specification of conceptualization [21]. 3. Ontology is a theory of vocabulary or concepts used for building artificial systems [31].

4. Ontology is a body of knowledge describing some domain (eg. a common sense knowl- edge domain in CYC [45]) The definition 1 is radically di‹erent from all the others (including additional ones dis- cussed below). We will shortly discuss some implications of its meaning for definition of “ontology” for AI purposes. The second definition is generally proposed as a definition of what an ontology is for the AI community. It may be classified as “syntactic”, but its precise meaning depends on the understanding of the terms “specification” and “conceptualization”. The third definition is a proposal for definition within the knowledge engineering community. The last fourth definition di‹ers from the previous two ones — it views the ontology as an inner body of knowledge, not as the way to describe the knowledge. Although these definitions are compact, they are not suŽcient for in-depth understanding of what an ontology is. We will try to give more comprehensive definitions and insights. 2.1.1 Ontology as a Philosophical Term Following [24] we will use the convention that the uppercase initial letter “O” is to distinguish the “Ontology” as a philosophical discipline from other usages of this term. Ontology is a branch of philosophy that deals with the nature and the organization of reality. It tries to answer questions like “what is existence”, “what properties can explain the existence” etc. Aristotle defined Ontology as the science of being as such. Unlike the special sciences, each of which investigates a class of beings and their determinations, Ontology regards “all the species qua being and the attributes that belong to it qua being” (Aristotle, Metaphysics, IV, 1). In this sense Ontology tries to answer the question “what is the being?” or, in a meaningful reformulation “what are the features common to all beings?”. This is what is today called “General Ontology” in contrast with various Special or Re- gional Ontologies (eg. Biological, Social). From this, Formal Ontology is defined as an area that has to determinate the conditions of the possibility of the object in general and the in- dividualization of the requirements that every object’s constitution has to satisfy. According to [24] Formal Ontology can be defined as the systematic, formal, axiomatic development of the logic of all forms and modes of being. From this, Formal Ontology is not concerned so much in the existence of certain objects, but rather in the rigorous description of their forms of being, i.e. their structural features. In practice, Formal Ontology can be intended as the theory of the distinctions, which can be applied independently of the state of the world, i. e. the distinctions:  among the entities of the world (physical objects, events, regions...)  among the meta-level categories used to model the world (concept, property, quality, state, role, part...) In this sense, Formal Ontology, as a discipline, may be relevant to both Knowledge Rep- resentation and Knowledge Acquisition [24]. 2.1.2 Ontology as a Specification of Conceptualization The second definition of ontology mentioned above, explicit specification of conceptualiza- tion, is briefly described in [20]. The definition comes from work [22] where the ontology is used in context of knowledge sharing. According to Thomas Gruber, explicit specification of conceptualization means that an ontology is a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents. This definition is consistent with the usage of ontology as set of concept definitions, but more general. In this sense, ontology is important for the purpose of enabling knowledge sharing and reuse. An ontology is in this context a specification used for making ontological commitments. Practically, an ontological commitment is an agreement to use a vocabulary (i.e. ask queries and make assertions) in way that is consistent (but not complete) with respect to the theory specified by an ontology. Agents are then built that commit to ontologies and ontologies are designed so that the knowledge can be shared with and among these agents. The body of a knowledge is based on a conceptualization: the objects, concepts, and other entities that are assumed to exist in some area of interest and the relationship that hold among them. A conceptualization is an abstract, simplified view of the world that we wish to represent for some purpose. Every knowledge base, knowledge-based system, or knowledge-level agent is committed to some conceptualization, explicitly or implicitly. For these systems, what “exists” is that which can be represented. When the knowledge of a domain is represented in a declarative formalism, the set of objects that can be represented is called the universe of discourse. This set of objects and the describable relationships among them, are reflected in the representational vocabulary with which a knowledge-based program represents knowledge. Thus, in the context of AI, we can describe the ontology of a program by defining a set of representational terms. In such an ontology, definitions associate the names of entities in the universe of discourse (e.g. classes, relations, functions, or other objects) with human readable text describing what the names mean, and formal axioms that constraint the interpretation and well-formed use of these terms. Formally it can be said that an ontology is a statement of a logical theory [20]. Ontologies are often equated with taxonomic hierarchies of classes without class definitions and the subsumption relation. Ontologies need not to be limited to these forms. Ontologies are also not limited to conservative definitions, that is, definitions in the traditional logic sense that only introduce terminology and do not add any knowledge about the world. To specify a conceptualization, one needs to state axioms that do constrain the possible interpretations for the defined terms. Pragmatically, a common ontology defines the vocabulary with which queries and as- sertions are exchanged among agents. The agents sharing a vocabulary need not share a knowledge base. An agent that commits to an ontology is not required to answer all queries that can be formulated in the shared vocabulary. In short, a commitment to a common ontol- ogy is a guarantee of consistency, but not completeness, with respect to queries and assertions using the vocabulary defined in the ontology. 2.1.3 Ontology as a Representational Vocabulary The third definition of ontology proposed above says that it is in fact a representational vo- cabulary [8, 31]. The vocabulary can be specialized to some domain or subject matter. More precisely, it is not the vocabulary as such that qualifies as an ontology, but the conceptu- alization that the terms in the vocabulary are intended to capture. Thus, translating the terms in an ontology from one language to another, for example from Czech to English, does not change the ontology conceptually. In engineering design, one might discuss the ontology of an electronic devices domain, which might include vocabulary that describes conceptual elements — transistors, operational amplifiers, and voltages — and the relations between these elements — operational amplifiers are a type-of electronic device, and transistors are component-of operational amplifiers. Identifying such a vocabulary and the underlying con- ceptualization generally requires careful analysis of the kinds of objects and relations that can exist in the domain. The term ontology is sometimes used to refer to a body of knowledge describing some domain (see below), typically a common sense knowledge domain, using a representational vocabulary. For example, CYC [45] often refers to its knowledge representation of some area of knowledge as its ontology. In other words, the representation vocabulary provides a set of terms with which one can describe the facts in some domain, while the body of knowledge using that vocabulary is a collection of facts about a domain. However, this distinction is not as clear as it might first appear. In the electronic-device example, that transistor is a component-of operational amplifier or that the latter is a type-of electronic device is just as much a fact about its domain as a CYC fact about some aspect of space, time or numbers. The distinction is that the former emphasizes the use of ontology as a set of terms for representing specific facts in an instance of the domain, while the latter emphasizes the view of ontology as a general set of facts to be shared. 2.1.4 Ontology as a Body of Knowledge Sometimes, ontology is defined as a body of knowledge describing some domain, typically a common sense knowledge domain, using a representation vocabulary as described above. In this case, an ontology is not only the vocabulary, but the whole “upper” knowledge base (including the vocabulary that is used to describe this knowledge base). The typical example of this definition usage is project CYC (http://www.cyc.com/, [45]) that defines its knowledge base as an ontology for any other knowledge based system. CYC is the name of a very large, multi-contextual knowledge base and inference engine. The development of CYC started during the early 1980s headed by Douglas Lenat. CYC is an attempt to do symbolic AI on a massive scale. It is neither based on numerical methods such as statistical probabilities, nor is it based on neural networks or fuzzy logic. All of the knowledge in CYC is represented declaratively in the form of logical assertions. CYC contains over 400; 000 significant assertions [45], which include simple statements of fact, rules about what conclusions to draw if certain statements of fact are satisfied (true), and rules about how to reason with certain types of facts and rules. New conclusions are derived by the inference engine using deductive reasoning. The CYC team doesn’t believe there is any shortcut toward being intelligent or creating an artificial intelligence based agent. Addressing the need for a large body of knowledge with content and context may only be done by manually organizing and collating information. This knowledge includes heuristic, rule of thumb problem solving strategies, as well as facts that can only be known to a machine if it is told. Much of the useful common sense knowledge needed for life is prescientific and has there- fore not been analyzed in detail. Thus a large part of the work of the CYC project is to formalize common relationships and fill in the gaps between the highly systematized knowl- edge used by specialists. It is not necessary to divide such a large knowledge base into smaller pieces to enable reasoning in reasonable time. Because of this, the CYC knowledge base uses a special context space [29], that is divided by 12 dimensions into smaller pieces (contexts) that have something in common and can be used to reason about a specific problem in that context. It is possible to “lift” assertion from one context to another when the problem requires it. The CYC common sense knowledge can be used as a body of a knowledge base for any knowledge intensive system. In this sense, this body of knowledge can be viewed as an ontology of the knowledge base of the system. 2.2 Other Ontology Definitions As we can see from the above discussions, the exact definition of ontology is not obvious, however it can be seen that the definitions have much in common. In addition to the above definitions there are many other proposals for ontology definitions. Some other definitions collected from [24] are: 1. informal conceptual system 2. formal semantic account 3. representation of a conceptual system via a logical theory (a) characterized by specific formal properties (b) characterized only by its specific purposes 4. vocabulary used by a logical theory 5. (meta-level) specification of a logical theory Definitions 1 and 2 conceive an ontology as a conceptual “semantic” entity, either formal or informal, while according to the interpretations 3, 4 and 5 is a specific “syntactic” object. According to interpretation 1, an ontology is the conceptual system which may be assumed to underlay a particular knowledge base. Under interpretation 2, instead, the ontology, that underlies a knowledge base, is expressed in terms of suitable formal structures at the semantic level. In both cases, we may say that “the ontology of knowledge base A is di‹erent from that of knowledge base B”. Under interpretation 3, an ontology is nothing else then a logical theory. The issue is whether such a theory needs to have particular formal properties in order to be an ontology or, rather, whether it is the intended purpose which lets us consider a logical theory as an ontology. The latter position can be supported by arguing that an ontology is an annotated and indexed set of assertion about something: “leaving o‹ the annotations and indexing, this is a collection of assertions: what in logic is called a theory” (Pat Hayes statement in [24]). According to interpretation 4, an ontology is not viewed as a logical theory, but just as the vocabulary used by a logical theory. Such an interpretation collapses into 3.a if an ontology is thought of as a specification of a vocabulary consisting of a set of logical definitions. We may anticipate that the Gruber’s interpretation (specification of conceptualization) collapses into 3.a as well when a conceptualization is intended as a vocabulary. Finally, under interpretation 5, an ontology is seen as a specification of a logical theory in the sense that it specifies the “architectural components” (or primitives) used within a particular domain theory.

3 Ontology Structure From the overview above we can see that an ontology can be perceived in basically two approaches. The first approach is an ontology as a representational vocabulary, where the conceptual structure of terms should remain unchanged during translation. The other ap- proach, that is discussed in this section, is an ontology as the body of knowledge describing a domain, in particular a common sense domain. An ontology can be divided in several ways. We will describe some of the proposals here. Particularly interesting is so called “upper ontology” that is intended to serve as an upper part of ontology of practically all knowledge based systems. Some of the ways of dividing presented here are intended to be used for merging to form an upper ontology standard in the IEEE Standard Upper Ontology Study Group [39]. On pages linked from [39] there are many other examples that could be used as some kind of an upper ontology.

(figure 1)

Figure 1: How ontologies di‹er in their analyses of the most general concepts [8] It is interesting that many authors agree that the upper class1 of the ontology is “thing”, however even in the second level they do not agree on the separation, as can be seen in the figure 1. The initiative [39] tries to unify these views.

3.1 CYC The ontology of CYC is based on a several terms that form the fundamental vocabulary of the CYC knowledge base. The universal set is #$Thing2 (see figure 1). It is the set of everything. Every CYC constant in the knowledge base is a member of this collection. In the prefix notation of the language CycL [10], we express that fact as (#$isa CONST #$Thing). Thus, too, every collection in the knowledge base is a subset of the collection #$Thing. In CycL, that fact is expressed as (#$genls COL #$Thing). The set #$Thing has some subsets, such as PathGeneric, Intangible, Individual, Sim- pleSegmentOfPath, PathSimple, MathematicalOrComputationalThing, IntangibleIndividual, Product, TemporalThing, SpatialThing, Situation, EdgeOnObject, FlowPath, ComputationalObject, Microtheory, plus about 1500 more public subsets and about 13600 unpublished subsets.

1. $Individual is the collection of all things that are not sets or collections. Thus,
2. $Individual includes (among other things) physical objects, temporal subabstractions of

physical objects, numbers, relations, and groups (#$Group). An element of #$Individual may have parts or a structure (including parts that are discontinuous), but no instance of

1. $Individual can have elements or subsets.
2. $Collection is the collection of all CYC collections. CYC collections are natural kinds

or classes, as opposed to mathematical sets. Their elements have some common attribute(s). Each CYC collection is like a set in so far as it may have elements, subsets, and supersets, and may not have parts or spatial or temporal properties. Sets, however, di‹er from collections in that a mathematical set may be an arbitrary set of things which have nothing in common (#$Set-Mathematical). In contrast, the elements of a collection will all have in common some feature(s), some ‘intensional’ qualities. In addition, two instances of #$Collection can be co-extensional (i.e. have all the same elements) without being identical, whereas if two arbitrary sets had the same elements, they would be considered equal.

1. $Individual and #$Collection are disjoint collections. No CYC constant can be an

instance of both.

1. $Predicate is the set of all CYC predicates. Each element of #$Predicate is a truth-

functional relationship in CYC which takes some number of arguments. Each of those argu- ments must be of some particular type. Informally, one can think of elements of #$Predicate as functions that always return either true or false. More formally, when an element of

1. $Predicate is applied to the legal number and type of arguments, an expression is formed

which is a well-formed formula (w‹) in CycL. Such expressions are called atomic formulas if they contain variables, or ground atomic formulas (gaf) if they contain no variables.

1. $isa:<#$ReifiableTerm> <#$Collection> expresses the ISA relationship. (#$isa EL

COL) means that EL is an element of the collection COL. CYC knows that #$isa distributes over #$genls. That is, if one asserts (#$isa EL COL) and (#$genls COL SUPER), CYC will infer that (#$isa EL SUPER). Therefore, in practice one only manually asserts a small fraction of the #$isa assertions — the vast majority are inferred automatically by CYC.

1. $genls:<#$Collection> <#$Collection> expresses similar relationship for collections

(generalization). (#$genls COL SUPER) means that SUPER is one of the supersets of COL. Both arguments must be elements of #$Collection. Again, as with the #$isa, CYC knows that #$genls is transitive, therefore, in practice one only manually asserts a small fraction of the #$genls assertions since the rest is inferred inferred automatically. More details about the structure of the CYC ontology and about how the CYC knowledge base is constructed can be found at http://www.cyc.com. 3.2 Russell & Norvig’s General Ontology Yet another view of general ontology structure is presented in Russell & Norvig’s book [38]. Every category of their ontology (see figure 2) is discussed in detail on example axioms. An example of this ontology in KIF [18] can be found at http://ltsc.ieee.org/suo/ ontologies/Russell-Norvig.txt.

(Figure 2)

Figure 2: Russell & Norvig’s general ontology structure [38]

3.3 Ontology Engineering Ontology engineering is a field in artificial intelligence or computer science that is concerned with ontology creation and usage. Report [31], that proposes and comments this field, declares that the ultimate purpose of ontology engineering should be “to provide a basis of building models of all things in which computer science is interested”. 3.3.1 Structure of Usage An ontology can be divided into following subcategories according to [31] from the knowledge reuse and ontology engineering point of view as follows. This is rather a structure of ontologies from a point of view of their usage than a division of one general ontology. Some examples are included.  Workplace Ontology This is an ontology for workplace which a‹ects task characteristics by specifying several boundary conditions which characterize and justify problem solving behaviour in the workplace. Workplace and task ontologies collectively specify the context in which domain knowledge is intended and used during the problem solving. Examples from circuit troubleshooting: fidelity, eŽciency, precision, high reliability.  Task Ontology Task ontology is a system of vocabulary for describing problem solving structure of all the existing tasks domain independently. It does not cover the control structure. It covers components or primitives of unit inferences taking place during performing tasks. Task knowledge in turn specifies domain knowledge by giving roles to each objects and relations between them. Examples from scheduling tasks: schedule recipient, schedule resource, goal, constraint, availability, load, select, assign, classify, remove, relax, add.

 Domain ontology Domain ontology can be either task dependent or task independent. Task independent ontology usually relates to activities of objects. – Task-dependent ontology A task structure requires not all the domain knowledge but some specific domain knowledge in a certain specific organization. This special type of domain knowledge can be called task-domain ontology because it depends on the task. Examples from job-shop scheduling: job, order, line, due date, machine availability, tardiness, load, cost. – Task-independent ontology ƒ Activity-related ontology  Object ontology. This ontology covers the structure, behaviour and function of the object. Examples from circuit boards: component, connection, line, chip, pin, gate, bus, state, role.  Activity ontology. Examples from enterprise ontology: use, consume, produce, release, state, resource, commit, enable, complete, disable. ƒ Activity-independent ontology  Field ontology. This ontology is related to theories and principles which govern the domain. It contains primitive concepts appearing in the theories and relations, formulas, and units constituting the theories and principles.  Units ontology. Examples: mole, kilogram, meter, ampere, radian.  Engineering mathematics ontology. Examples: linear algebra, physical quantity, physical dimension, unit of measure, scalar quantity, physical components.  General or Common ontology Examples: things, events, time, space, causality or behaviour, function etc. 3.3.2 Ontology Engineering Subfields We can also divide the ontology or ontologies from the point of view of ontology engineering as a field. The subjects which should be covered by ontology engineering are demonstrated in [31]. It includes basic issues in philosophy, knowledge representation, ontology design, standardization, EDI, reuse and sharing of knowledge, media integration, etc. which are the essential topics in the future knowledge engineering. Of course, they should be constantly refined through further development of ontology engineering.  Basic Subfield – Philosophy(Ontology, Meta-mathematics) Ontology which philosophers have discussed since Aristotle is discussed as well as logic and meta-mathematics.

– Scientific philosophy Investigation on Ontology from the physics point of views, e.g., time, space, pro- cess, causality, etc. is made. – Knowledge representation Basic issues on knowledge representation, especially on representation of ontologi- cal stu‹, are discussed.  Subfield of Ontology Design – General(Common) ontology General ontologies such as time, space, process, causality, part/whole relation, etc. are designed. Both in-depth investigation on the meaning of every concept and relation and on formal representation of ontologies are discussed. – Domain ontologies Various ontologies in, say, Plant, Electricity, Enterprise, etc. are designed.  Subfield of Common Sense Knowledge – Parallel to general ontology design, common sense knowledge is investigated and collected and knowledge bases of common sense are built.  Subfield of Standardization – EDI (Electronic Data Interchange) and data element specification Standardization of primitive data elements which should be shared among people for enabling full automatic EDI. – Basic semantic repository Standardization of primitive semantic elements which should be shared among people for enabling knowledge sharing. – Conceptual schema modeling facility (CSMF) – Components for qualitative modeling Standardization of functional components such as pipe, valve, pump, boiler, regis- ter, battery, etc. for qualitative model building.  Subfield of Data or Knowledge Interchange – Translation of ontology Translation methodologies of one ontology into another are developed. – Database transformation Transformation of data in a data base into another of di‹erent conceptual schema. – Knowledge base transformation Transformation of a knowledge base into another built based on a di‹erent ontology.  Subfield of Knowledge Reuse – Task ontology Design of ontology for describing and modeling human ways of problem solving.

– T-domain ontology Task-dependent domain ontology is designed under some specific task context. – Methodology for knowledge reuse Development of methodologies for knowledge reuse using the above two ontologies.  Subfield of Knowledge Sharing – Communication protocol Development of communication protocols between agents which can behave coop- eratively under a goal specified. – Cooperative task ontology Task ontology design for cooperative communication  Subfield of Media Integration – Media ontology Ontologies of the structural aspects of documents, images, movies, etc. are de- signed. – Common ontologies of content of the media Ontologies common to all media such as those of human behavior, story, etc. are designed. – Media integration Development of meaning representation language for media and media integration through understanding media representation are done.  Subfield of Ontology Design Methodology – Methodology – Support environment  Subfield of ontology evaluation – Evaluation of ontologies designed is made using the real world problems by forming a consortium.

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Notes:
1 When using hierarchy of classes for ontology description. For describing ontologies however we do not have to limit to class hierarchies as in the case of taxonomies.
2 We will use the notation used in CYC language. The explanation of it can be found in [10].