A Step Towards Ontological Engineering
(Translation of the paper presented at the 12th National Conference on AI of JSAI, pp.24-31, June, 1998)
ISIR, Osaka University, Japan
Abstract: We discuss ontology and ontological engineering which is expected to play a role of foundation of so-called Content-Directed AI Research. I first answer questions about what is content-directed research followed by some definitions of an ontology. I next answer further questions about an ontology and describe the merits of ontology design. Finally, I present my idea about Ontological Engineering as well as its future.
Three major paradigm shifts have been occurring in AI research community: (1) from process-centered to Information-centered, (2) from computer-centered to human-centered, and (3) from form-centered to content-centered. While the first two are important, the third is expected to play a critical role in the Knowledge society in the coming 21st century and is the discipline of so-called gContent-directed researchh which I have advocated for years.
In AI, formalism-oriented research has dominated as basic research extensively to date. In other words, basic research concerning gvesselsh has been done paying little consideration on its content, while research on gTheory of contenth, that is, research on gwhat and how to put in the vesselh has not been done to date. This is partly because the content largely depends on the respective domains of interest, which prevents to investigate general theories of gcontenth. And this in turn has made it difficult for AI to treat the real world problems which require deep understanding of objects specific to respective domains together with effective devices to enable their treatment.
My colleagues and I have proposed gOntological Engineeringh[Mizoguchi 96, 97] and have practiced it to date. This paper describes what is Ontological Engineering.
2.1 What is content-oriented research?
Content-oriented research is a great attempt of knowledge sharing with humans and computers. The dichotomy of general theories of a vessel which can be easily formalized and gdomain-specific knowledgeh which lacks generality has been widely accepted in AI community. An idea that there cannot be any theory about gContenth has been accepted. The objective of Content-oriented research is to get rid of such a misleading understanding about AI research and to provide effective theories and techniques enabling gKnowledge accumulationh and gKnowledge interoperabilityh which do play critical roles in the next generation knowledge processing.
2.2 How is it possible to come up with general theories of content for such a variety of domain knowledge?
It is true that each branch of sciences has established their own knowledge respectively. On the other hand, computer science has pursued domain-independent theories and techniques treating ginformationh and gdatah obtained by abstracting things existing in all the domains. It is thanks to appropriate abstraction and careful consideration about them. Similarly, content-oriented research tries to exploit abstraction and decomposability of knowledge providing sophisticated guidelines and tools for understanding and model building of the target world(See 5.1).
2.3 Why ontology instead of knowledge?
It is true that knowledge is domain-dependent, and hence knowledge engineering which directly investigates such knowledge has been suffering from a rather serious difficulty caused by its specificity and diversity. However, ontology is different. In the ontology research we investigate knowledge in terms of its origin and elements from which knowledge is constructed. Hierarchical structure of concepts and decomposability of knowledge are exploited to deeply investigate primitives of knowledge as well as background theories of knowledge which enables us to avoid the difficulties knowledge engineering has faced with.
2.4 Isnft Ontological engineering another application-oriented research?
While ontological engineering deals with domain-specific knowledge, it tries to establish theories and technology for gaccumulatingh knowledge within reasonable size of stratified domains utilizing ontologies. It is such a branch of AI research that investigates basic theories and technology to treat real world knowledge and is such an enterprise that denies the simple dichotomy of AI research: gBasic researchh and gApplication researchh.
2.5 Does Content-oriented research neglect formalism-oriented research?
Although it has a plan to improve the unhealthy tendency of AI community in which formalism-oriented research has been overemphasized to date, it never neglects the importance of gformh or gformalizationh. It rather considers formalization as an indispensable technique as mathematics in physics. A typical example is seen in the use of axioms employed in the definitions of concepts and relations in an ontology. The content-oriented research tries to keep a healthy balance between gformh and gcontenth using an ontology as a kernel concept.
There are many interpretations about what an ontology is in spite of the fact that it is understood to serve as a kernel theory and building blocks of content-oriented research. In fact, hot discussions are often done in many meetings on ontology. This section presents some of the definitions of an ontology.
3.1 Some definitions
3.2 Typical questions
Let me cite a phrase found in the email archive of ontology:
Date: Wed, 26 Feb 1997 12:49:09 -0800 (PST)
From: Adam Farquhar axf@HPP.Stanford.EDU
a new knowledge base, a database schema, an object-oriented program?
The stronger the 'yes' answer is to these questions, the more 'ontological' it is.
The above opinion is based on that there is no clear boundary between ontology and knowledge. It is a reasonable understanding when we think of Cyc[Lenat, 95] whose upper part is definitely an ontology and the whole seems to be a knowledge base. But, the above opinion is somewhat misleading, since there is no clear definition of a knowledge base. My answer to the question is that we need to introduce a relativity when we understand an ontology. That is, if we think of a rule base of an expert system as an example of a knowledge base, an ontology is apparently different from the rule base because the rule base is based on the creatorfs conceptualization of the target world which the ontology specifies and formalizes. And, the rule base never represents such a conceptualization. Thus, such a relative relation between the both clearly shows the essential difference of the two.
(2) How an ontology is different from the class hierarchy in object-oriented paradigm?
They are similar and further, the developmental methodologies of an ontology and an object hierarchy is also similar to each other in the upper stream. In the lower stream, however, the former concentrate on declarative aspects and the latter on performance-related aspects. Thus, the essential difference between the two lies in that the ontology research exploits declarative representation, while the OO paradigm is intrinsically procedural. In OO paradigm, the meaning of class, relations among classes, and methods are procedurally embedded and they are implicit. The ontology paradigm, on the other hand, descriptions are made declaratively in most cases to maintain formality and explicitness.
(3) Whatfs new? How is it different from taxonomy of concepts?
An ontology contains a taxonomy as its component as discussed in the above. So, it partially implies a taxonomy. In general, a new term is rarely totally new. Rather, it is usually coined by extending existing terms. The term gontologyh is not an exceptional case. It is a new term and concept including existing concepts such as gtaxonomyh, gcommon vocabularyh, gupper modelh, etc. by adding formality, richer relations, explicit representation of stuff usually left implicit.
(4)Are upper model and domain ontology compatible with each other?
An ontology should be use-specific in engineering settings. People believe that a general and neutral ontology is of useless. A typical example is task ontology[Mizoguchi, 92, 95a, 95b] where domain knowledge has to be organized so as to fit into the task model the task ontology specifies. In such cases, neutral domain ontology cannot apply to any problem without adjusting to the task structure. This discrepancy suggests us a potential difficulty which we might face with. People involved in upper ontology development advocate the importance of general and use-neutral ontology which is hard to be accepted by people who have to treat real world problems. Ideally, however, many use-specific ontology could find some of the essential concepts in use-independent ontology which should be inherited. We need more effort to harmonize the both activities believing we will come up with several possible upper ontologies one of which is really agreeable.
(5) What is the computational semantics of an ontology? Is it just a set of labels?
This is one of the most crucial points of the roles an ontology plays. Contrary to that an ontology sometimes looks just a set of labels, it has deeper computational semantics. I have proposed the following three levels of ontologies.
Level 1: A structured collection of terms. The most fundamental task in ontology development is articulation of the world of interest, that is, elicitation of concepts and identifying so-called is-a hierarchy among them. These are indispensable to things to be an ontology. Typical examples of ontologies at this level include topic hierarchies found in internet search engines and tags used for metadata description. Little definitions of the concepts are made.
Level 2: In addition to that at the level 1 ontology, we can add formal definitions to prevent unexpected interpretation of the concepts and necessary relations and constraints also formally defined as a set of axioms. Relations are much richer than those at the level one. Definitions are declarative and formal to enable computers to interpret. The interpretability of an ontology at this level enables computers to answer questions about the models built based on the ontology. Many of the ontology building efforts aim at those at this level.
Level 3: The ontology at this level is executable in the sense that models built based on the ontology run using modules provided by some of the abstract codes associated with concepts in the ontology. Thus, it can answer questions about runtime performance of the models. Typical examples of this type are found in task ontologies[Mizoguchi, 92][Breuker94][Seta96][Chandra 98].@
(6) What are the concrete merits an ontology can provide?
This question is also very important. The following is an enumeration of the merits we can enjoy from an ontology:
The description of the target world needs a vocabulary agreed among people involved. The fundamental role of an ontology contributes to it.
In all of the human activities, we find presuppositions/assumptions which are left implicit. Typical examples include definitions of common and basic terms, relations and constraints among them, and viewpoints for interpreting the phenomena and target structure common to the tasks they are usually engaged in. Any knowledge base built is based on a conceptualization possessed by the builder and is usually implicit. An ontology is an explication of the very implicit knowledge. Such an explicit representation of assumptions and conceptualization is more than a simple explication. Although it might be hard to be properly appreciated by people who have no experience in such representation, its contribution to knowledge reuse and sharing is more than expectation considering that the implicitness has been one of the crucial causes of preventing knowledge sharing and reuse.
An ontology in a database is the conceptual schema. In this sense, an ontology provides us with a data structure appropriate for information description and exchange.
Knowledge systematization requires well-established vocabulary/concepts in terms of which people describe phenomena, theories and target things under consideration. An ontology thus contributes to providing backbone of systematization of knowledge. Actually, I have been involved in a project on Knowledge systematization of production knowledge under the umbrella of IMS: Intelligent Manufacturing Systems project[IMS 97][Ueda 97].
The success of the modern industries has been achieved thanks to standardization of various components. We cannot avoid standardization in the successful knowledge processing research and activities in the real world. We do need something compatible to gboltsh and gnutsh in our community.
Typical examples to be explicated include intention of the designers of artifacts, that is, part of design rationale. An ontology contributes to explication of assumptions, implicit preconditions required by the problems to solve as well as the conceptualization of the target object which reflects those assumptions. In the case of diagnostic systems, fault classes diagnosed and range of the diagnostic inference, in the case of qualitative reasoning systems, classes of causal relations derived, and so on.
A model is usually built in the computer as an abstraction of the real target. And, an ontology provides us with concepts and relations among them which are used as building blocks of the model. Thus, an ontology specifies the models to build by giving guidelines and constraints which should be satisfied. This function is viewed as that at the metalevel.
The following is an enumeration of the merits of the systems built based on an ontology:
The explicit description about assumptions of the world the system is interested in contributes to making the system understandable and transparent, and hence its reusability increases. Further, the level 2 ontology enables us to build a guidance system for identifying reusable modules for specific purpose. Needless to say, we need more to attain satisfactory reusability of modules.
An ontology helps us specify functions of the system and communication protocol among systems, it thus increase the interoperability of the systems.
An explicit specification of the assumptions, functions, and constraints of the modules make it easier to implement the system.
4. A brief survey of the research on ontology
N. Guarino and J. Sowa have been independently conducting research on theories of ontology. The both share the attitude towards philosophy. They both incorporate the results obtained in philosophy as principles to design the top-level ontology. Many of the practitioners have negative attitude to the top-level ontology under which they are required to put their ontology because they believe no use-independent ontology is useful. In the case of building an ontology for a large scale knowledge base, however, the validity of the knowledge base necessarily be justified in terms of wider rage of tasks, that is, it needs to show its generality rather than task-specific utility. Compliance with the principled top-level ontology provides a good justification. Thus, top-level ontology is important.
Sowafs ontology is based on J. S. Peircefs idea of the firstness which is defined without assuming any other things like human, iron, etc., the secondness which is defined depending on other things like wife, teacher, etc., and thirdness which provides an environment or context where the secondness works like family, school, etc. He introduces two important concepts, continuant and occurrent in addition to the three and obtains 12 top level categories by combining the seven primitive properties[Sowa 95, 98].
Guarinofs top level ontology is more extensively incorporates philosophical consideration. It is designed based on mereology(theory of parts), theory of identity, and theory of dependency. His ontology consists of two world: An ontology of Particulars such as things which exist in the world and Universals which include concepts we need when we describe Particulars[Guarino 97].
4.2 Machine readable dictionary: MRD
Development of machine-readable dictionaries has been done extensively in natural language processing community where ontology has also been discussed as the upper level model of the words/concepts structure. Typical examples include WordNet [Miller93]@[WordNet],@EDR[Yokoi, 95],@EuroWordNet[EWN], Generalized Upper Model[GUM]. Cyc is not a dictionary, but is a huge common sense knowledge base whose upper level structure is an ontology. One thing to note here is the activity done in the ad hoc committee in ANSI where they intend to design a reference ontology(RO) (http://ksl-web.stanford.edu/onto-std/) by aligning the upper level model of each of WordNet, Cyc and EDR. It is not easy to attain the goal, but it is of worth to try, I think.
4.3 Metadata, XML, Tag, and intelligent player
Another activity to note is that about standardization about metadata such as Dublin Core[DC 97], MCF: Meta Content Framework [MCF 97] and RDF: Resource Description Framework [RDF 97] in W3C. Dublin Core is the first de facto standard of the metadata descriptors. MCF was proposed to W3C as a candidate of the framework for metadata representation and extended and elaborated into RDF which is becoming as a standard. RDF introduces XML for its syntax.
XML[XML] is a simplified version of SGML which is a powerful markup language definition language and has been widely used in document description for years. XML is equipped with several powerful hyper-reference functions to fit the internet environment. The good things of XML is that the users can define their own tags which shows not only structure information of the document but also its semantic information for various uses of the document to enable semantic interoperation. For example, we can design an intelligent instructional player of a teaching material as an XML document by sharing a set of tags for explicating the roles of the portion of document and controlling the interpretation. It can gPlayh the XML document adaptively to the performance of the learners. Further, we could formalize the set of tags to specify the performance of such instructional players. While tags in metadata description generally form a level one ontology, such tags can be those at the second level. Intelligent players with plug & play capability with shared XML tags is expected to be promising with Java implementation.
4.4 Developing methodology
We here present some of the developing methodologies and environments.
KBSI: Knowledge Based Systems Inc. has developed a comprehensive methodology for developing an ontology, IDEF5, especially for enterprise modeling together with graphical and representation languages. IDEF5 tries to follow the normal and typical methodology and adopts the steps such as term extraction, term definition, relation among terms extraction followed by formalization of them. Documentation is well done and contains helpful guidelines to follow.
TOVE is also one for enterprise ontology like IDEF5. Its remarkable characteristics include:
The approach is thus very formal and follows the standard approach in software engineering. The typical questions include:
TOVE thus axiomatizes time and activity to be able to investigate many characteristics of the model through conceptual-level execution.
4.4.3 DODDLE, Aspect theory and CLEPE&AFM
There are several activities also in Japan. DODDLE(A Domain Ontology rapiD DeveLopment Environment) [Yamaguchi 97] is an environment for ontology development which has been used to build a legal ontology. Its characteristics is that WordNet is incorporated in the environment as the upper model to give the users guidelines for identifying and defining domain-specific concepts.
Aspect theory [Takeda 95] is a framework for combining several ontologies built based on different viewpoints. An ontology is called an Aspect and a set of logical operation among them are defined to form a theory. ASPECTROL is a language to implement the theory. The ontologies built by the above two environment are those at level 1 mentioned in 3.2.
CLEPE[Seta 97] is an environment for task ontology[Mizoguchi, 93], a level 3 ontology,
Development. It is designed assuming three kinds of users such as basic ontology authors, task ontology authors and task model authors. Models are built under the guide of task ontology to maintain their consistency. Such models are able to answer the competency questions like TOVE.
AFM: Activity-First Method[Mizoguchi, 95] is an environment for ontology building where task analysis is first done to elicit verbs which specify objects necessary for performing the task of interest. Nouns used as objects of the verbs are then extracted with the roles played by them. The building process begins by document analysis managing several intermediate products obtained during the course of ontology building to enable sophisticated support. The ontology built by AFM is of the level 2. AFM is going to be augmented to enable users compare multiple ontologies built by different persons to come up with a partial agreement of them and to build a maximally agreed ontology.
5. Ontological Engineering
We here try to answer several questions about what an ontological engineering is.
5.1 What do I mean by gAn ontology is a theory of contenth?
Observation of physics tells us mathematics have played a critical role in establishing it as a beautiful science. Mathematics provides us with:
These concepts successfully cover what physics needs to explain phenomena it is interested in, which enables physics to be a theoretical science. That is, physics has a firm theoretical foundation supporting its activity in dealing with gcontenth
Then, how about knowledge processing research? Does it have a similar theoretical foundation for gcontent-directed researchh? Letfs take gLogich as an example. Viewing logic as a theory of knowledge representation, it treats gcontenth as a predicate which takes a truth value, true or false neglecting all the other characteristics of the rich meaning. In return of the simplification, logic succeeds in getting generality and beauty of the theory. A predicate P in P(X) means it is gsomethingh taking true or false about an object X, nothing other. It does NOT tell X is red, heavy, a non-animate, a human, a student, a husband, X moves, walks, etc. In short, logic gives us nothing about meaning about the world.
An ontology provides us with a set of concepts(vocabulary) used as predicates we need to describe the world knowledge. It specifies what meaning and constrains each concept has other than taking a truth value. For example, it reveals the four predicates tall(X), human(X), student(X), move(X) are very different from each other by specifying them as gtallh is an attribute without identity, ghumanh is a category X belongs to by its inherent property with identity, gstudenth is a role concept which does not represent Xfs inherent property and is not persistent like the process gmoveh. The four predicates thus have their own properties and constraints associated with the categories they belong to. An ontology explicates such content-related information in the form of axioms, that is, an ontology provides us with a theory of content.
The remarkable progress of physics have been attained by describing and accumulating knowledge in various domains thanks to exploitation of mathematical concepts and formalism. On the other hand, knowledge processing researchers have never tried accumulation of knowledge by saying gcontent of each piece of knowledge is too domain-specifich. * Including the discussion about up to what abstraction level from the top we can accept as a common theory of content, taxonomy in an ontology contributes to construction of the theory of content and to knowledge accumulation. Recognizing the fact that it is unlikely to have a powerful theory with small amount of concepts and operations like physics, we need to make an effort to establish the theory of content with a longer term perspective.
* The only exception of this is Cyc project[Lenat, 1995].
5.2 What topics does ontology engineering cover?
At least the following topics should be investigated.
Metaphysics of science
Common sense base
Methodology of ontology development
Description language, Methodology, Environment
Ontology comparison, alignment and unification
Knowledge sharing and reuse
Business process modeling
Systematization of domain knowledge
Internet information retrieval
Common ontology for knowledge media
5.3 Does ontological engineering have a methodology?
While methodologies and environments for ontology development have been proposed as described above, we cannot say we have one for ontological engineering. It should be what we need to explore during the course of daily activities towards our goal. The following is just my personal view of a guideline.
5.3.1 What are implicit assumptions?
We cannot state anything without implicit assumptions. A typical case includes the situation where we build a model of a target object. In such a case, we find
In most of the cases, however, these kinds of assumptions are kept hidden and what is represented is only the model built in terms of the fundamental vocabulary. Even worse, the basic terms in the vocabulary are seldom defined explicitly and we are often surprised to find there is little consensus about the definitions of them, say, gbehaviorh, gfunctionh, or gmodelh. Thus, a model is usually so incomplete as an explicit description.
5.3.2 Isnft there any concrete example?
We sometimes find we do not know what a term, we think as a matter of course, exactly means. The following is my colleague, Kitamurafs and my experience in the research of gOntology of faulth[Kitamura 98].
What is a cause of a fault? At the first glance, it seems to be a very trivial question. But, the reality is not so. Let me take a fault of a TV set. Imagine, you are told by the engineer you asked to fix it gSir, this condenser was broken, so I replaced it with the new one.h Even if you do not understand the causal relation from the condenser to the symptom you initially found, you easily accept that the condenser is gthe causeh of the fault. If the TV set was very new, however, you might start to suspect another(deeper) reason which caused to break the condenser. An excessive current might have flown through the condenser because of a short in somewhere or a lot of heat might have been radiated by a device near it, etc.
The above two ways of understanding causes of a fault are different from each other. The former is based on the naοgthe whole(TV set)h must have been induced by ga faulth of its gparth, and the latter based on the more physical causal understanding. In fact, the former needs only subsumption relation among parts to trace the causal chain, the latter needs deeper causal relations in physical processes.
We could come up with so many questions about the seemingly well-understood topic, gfaulth. My group have investigated the topic and identified and formulated tens of such concepts to build an innovative diagnostic system as well as characterization of typical type of diagnostic systems.
5.4 How can ontological engineering cope with the variety of models?
I have declared that the ultimate goal of Ontological engineering is to establish the foundation for modeling all the objects, strictly speaking what the computer science needs to tackle, existing in the world. This statement needs some additional explanations to make it convincing.
The issue here is the diversity of gobjectsh. In the example of diagnostic tasks, the possible objects include a motor, TV set, car, electric plant, chemical plant, telephone network, etc. which varies in domains and size. And, further, tasks performed in each of the domains require the models tailored to them. In summary, theory of models, and hence ontological engineering should cope with the great variety of models.
The above is only concerned with the models of gthingsh. We have another type of objects which need to model, Problem solving process or gtasksh. What is a diagnostic task? What is design? Business process is also a target of modeling. And the last and biggest gobjecth is gKnowledgeh. What is the model of gKnowledgeh?
Model building requires a set of building blocks and specifications or theories of the composition of them. It is true that concrete building blocks are heavily domain-dependent. But the exhaustiveness or completeness is not the goal of the highest priority. Rather, we can stay at the right abstraction level at which we can find sizable classes of models. Each of them allows us to build an ontology by concentrating the hierarchical structure of concepts to find the class-wide common characteristics among concepts. Ontological engineering also contributes to formation of theories of model building domain-independently by building methodologies for building and utilizing ontologies, description language, etc.
The more important issue here is the class of computational level of an ontology, that is, the three-level architecture of an ontology mentioned earlier. I have to admit an ontology at the level 3 is not easy to design for various objects. But, one at the level 2 can be reasonably general and effective in forming a theory for various objects.
We have made a comprehensive discussion on an ontology together with trends in ontology research. Let us pick some topics again before closing this article.
Standardization has both of good and bad sides. Some people tend to stress the bad side. And others tend to think Ontology should exist as the universal one considering its original meaning in philosophy and say it is almost impossible for all the people to agree on gA universal ontologyh. I understand such speculations, but still want to take the position that we easily find the more in good side than those in bad side of an ontology. Knowledge sharing and reuse and semantic interoperability of documents, models and systems do requires standard ontology, at least, shared common ontology.
It is pity that we still do not come to an agreement of what an ontology is. Rather, ontology researchers seem to have come to an agreement that gLetfs not discuss the definition of an ontologyh. I can see at least three different kinds of attitudes towards an ontology: (a) Natural language processing, (b) Knowledge-based systems, and (c) Philosophical modeling of the world. NL people would like to discuss gvocabularyh and gLexiconh together with the meaning of each term in it keeping tasks such as text understanding in their mind. The KBS people, on the other hand, tend to stress the executability of components included in the ontology as building blocks of the knowledge \based systems. People taking the philosophical attitude would like to think of rather universal upper model designed intended to govern the domain-specific ontologies. Each of them has reasonable justifications, which makes it difficult to come to an agreement in the near future.
Further, it is interesting to view the differences in terms of cultural perspectives. US people tend to be pragmatic and hence they put reusability or interoperability as a primarily important criterion in ontology design. People in Europe would like to be more philosophical than US people and we Japanese stay in the middle and to nicely compromise the advantages of both.
In spite of the situation where we lack common understanding about an ontology, it is definite that we agree on the importance of an ontology in advanced knowledge processing technology. What we should do is to concentrate on what we have been doing based on our own belief avoiding the discussion on what an ontology is under the umbrella of gOntological engineeringh.
I am grateful to Mitsuru Ikeda and Yoshinobu Kitamura who have been discussing on the topic for years. My thanks also go to the member of the Ontological engineering committee sponsored by JIPDEC.
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