Ontology for People- Vagueness and multiplicity Hideaki Takeda12
1 National Institute of Informatics
2-1-2, Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan
takeda@nii.ac.jp
http://www-kasm.nii.ac.jp/~takeda/
2 Research into Artifacts, Center for Engineering (RACE), The University of Tokyo
Kashiwanoha 5-1-5 Kashiwa Chiba 277-8568, Japan

ABSTRACT. In this paper, I argued ambiguity of representing concepts. Ontologists incomputer science, especially in Semantic Web dismissed the importance of variety of conceptualization. But richness of our conceptualization is crucial for representing ontology andcan contribute various use of ontology in computer systems. In particular I picked up multiplicity of ontology in this paper. I show two types of treatment of multiplicity in ontology.
One is representation of multiple and shared ontologies by combining fragments of ontologies. The other is representation of multiplicity of concept. Variety of concept representation
by people are aggregated and represented as a virtual concept.

1 Introduction
Ontology in information systems becomes so popular mainly thanks to Semantic Web.
It started from Tom Gruber’s introduction of “ontology” in artificial intelligence [Gruber 1993]. He intended ontology as a basic layer of knowledge in the context of knowledge sharing, in particular knowledge sharing among computer agents. He defined
ontology as “A specification of a representational vocabulary for a shared domain of
discourse — definitions of classes, relations, functions, and other objects — is called an
ontology.” It is in spirit a return to traditional approach in artificial intelligence like
[Genesereth et al. 1987]. We cannot represent grand truth in computer as the traditional
AI approach presumed implicitly, but we can deal with truth-like representation on
condition that people agree. Its simplicity and orthodoxy are embraced by AI researchers, and so are Web people later on. Now ontology languages RDFS and OWL become
one of standard languages for Web.
Spreading out Semantic Web concept and its applications revealed that simplicity in
Gruber’s definition does not fit concept sharing in the real world, in particular in a large
scale like Web. I believe that it comes from vagueness and multiplicity.
In this paper, I firstly discuss what concepts people want to express and share to eachother by observing social software systems on Web. Then I show my approach how todeal with it with computer, in particular representation and interpretation of multiple
ontologies.

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2 Semantic Web and Social Software Systems on Web
Frankly speaking, it looks difficult that Semantic Web applications become popular.
Aside from Semantic Web, social software like social tagging becomes so popular andrecognized one of the important services on Web.
For example, people share their tags in social tagging systems. We can say that people
share concepts through such systems. But sharing is very loose. What they can do when
adding a new instance for a tag is checking the labels of the tag and understanding theexisting instances for the tag at most. Even they skip to check existing tags but just add
new tags and instances by themselves. So mixture of concepts in a single tag often happens like “apple” tag both for fruit and a computer company. Despite such flaws, social
tagging systems are accepted as information sharing tools by people.
The other example of social software system is Wikipedia, volunteer-based encyclopedia. Everyone can create a new article and edit any articles. There are some interesting
points from ontology viewpoint. Here we can assume that an article and a category
represent an instance and a class respectively. Each article is identified only by its name.
There is no need to provide definition of categories or classes in advance. Categories
can be assigned to articles but it is not necessary and can be assigned more than one.
Some groups of articles have common formats called “infobox” which represents something like attribute and its value. But it is also arbitrary.
People wish to share concepts but definition of concepts in Semantic Web way does not
fit with what people want to express and share.
Our recognition of concept is very vague. We can indicate a concept but it does not
imply that we can define or even represent it. There are many different points in comparison to concept in ontology in computer systems. I pick up two sub problems here.
One sub problem is the problem of definition. Except scientific or engineering concepts,
concepts exist without or preceded their definition at most. In social tagging systems,
definition is abandoned. Instead only extension is given, i.e., a set of instances (URLs).
In Wikipedia, definition follows the existence of articles like adding categories andinfobox.
The other problem is multiplecity of concept. Even if concepts are definable, it does not
imply the definition is unique. Instead it is natural to have multiple definitions for asingle concept, since concepts can be used in different contexts. In particular, we areassuming concepts for sharing among people, difference of context should be taken into
accounts. In social tagging systems, people do not matter if there is disagreement orinconsistency of usage of individual tags. Rather they enjoy vague recognition of tags
because it can aggregate more information. In Wikipedia, multiplicity of concept is
acceptable but should be resolved in some way. The name multiplicity is solved bynaming convention such as adding parenthesis expression. Multiplicity of class hierarchy is represented by multiple categories. Multiplicity of the content itself isrepresented as the structure of page after discussion by people.
In Semantic Web, the importance of these problems has been dismissed since it started
from simplicity of ontology definition by Tom Gruber. Multiplicity of ontologies is now
becoming a hot topic such as ontology alignment or ontology matching. But still ontol-

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Ontology for People3

ogy is believed to be simple and unique ideally behind ontology alignment and ontology matching.
I believe that multiplicity of concept is necessary to represent richness of our conceptualization and can contribute various use of ontology in computer systems.
In the following sections, I introduce our attempts to deal with multiplicity. The firstattempt is done before the rapid rise of Web [Takeda et al. 1995] and the second is ouron-going research topic [Shakya et al. 2008].

3. Multiple Ontologies for Agents
Knowledge sharing with computer agents was a possible solution before the rise ofWeb. In this approach, heterogenous agents communicate to each other to solve problems. KIF [Genesereth et al. 1992] and KQML [Finin et al. 1995] are defined for interagent communication languages. Although KQML performative has an attribute tospecify ontology, ontology in a multi-agent system is shared without inconsistency. But
it is natural that heterogenous agents use heterogenous ontologies. Then the problem is
how to realize communication between agents with different ontologies.
We tried to solve this problem within the logical treatment as an extension of Ontologinua [Gruber 1992]. We divide the logical theory which represents ontology into fragments of the logical theory (called aspect) and represent heterogenous ontologies withcombination of fragments of the logical theory.

3.1 Multiple Aspects
It is often assumed that a single ontology is shared among agents. But it is not easy tobuild large ontologies in fact. One of the reasons is that we often confuse concepts fromdifferent conceptualization when we try to build large ontologies. For example, Figure
1 shows how concept “temple” is modeled differently. One may think temple as item intextbook of history, so “founded year”, “sect”, “historical events”, and so on are usedwith “temple”. One may think temple as place for religion, so “doctrine”, “parishioners”,“branch temples”, “chief priest” are concepts related to “temple”. Of course, some concepts are appeared in more than one aspect like “name” and “location”.
Mixture of concepts from different conceptualization often confuses us. Some different
concepts in different aspects may denote the same fact, for example “chief priest” inaspect-for-temple-as-religious-place and “representative” in aspect-for-temple-aspublic-organization may be the same. On the other hand, concepts used in more than
one aspect may denote different facts, for example “name” is used in common, but its
meaning is either religious name or historical name or official name with according to
aspect it is used. In such cases, the more concepts are collected, the less clear are meanings of concepts.

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Hideaki Takeda

T empleT emplefounded years ectfounder

T emple his torical eventss tyle of architecture

namehis torical value

location

events of the year

chief pries tbranch temples
paris hioners
doctrinearea
repres entative

T emplecultural as s ets
Fig. 1: What concept is good for “temple”?
Each concept is meaningful if and only if concept is used in proper way, that is, concept
is used with concepts which come from the same conceptualization. We call this unit{\it aspect}. We can say that an aspect is a consistent view for conceptualization.
Then ontology can be composed of some aspects.
We use various aspects, for example in engineering we use aspects like dynamics aspects, kinematics aspects. To model the commonsense world of traveling, we may use
aspects like traffic aspect or geography aspect.

3.2 Representation of Multiple Aspects
There are two issues on aspect. One issue is what should be in aspect. Aspect shouldhave a vocabulary to describe phenomena in its domain. It should also have a theory
which associates concepts in its vocabulary. And the theory should be consistent. In theother words, aspect is what we can conceptualize the world without inconsistency.
The other issue is how to compose aspects from other aspects. We provide two types ofbasic connections among aspects. One is combination aspect. This is just integration of
aspects for different domains. For example, one of the ways to build aspect-for-travel
is to combine aspect-for-hotel and aspect-for-traffic. In this case, concepts like “railway” which is in aspect-for-traffic do not exist in aspect-for-hotel, because domain ofmodeling is different from each other. In aspect-for-travel, concepts like “tour” aredefined using concepts from both aspects.
The other is category aspect. This is collection of aspects which share domains but
come from different conceptualization.

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Ontology for People

5s ight-s eeing tour

sight-s eeing hotels ight-s eeing point

charge withtwo meals

templehotel

hotel chargeitineraryplace

charge withoutmeal

bus ines s hotel
bus ines s tour

: category as pect
: combination as pect

Fig 2: Multiple and Shared Ontologies
When a temple is modeled differently we have just shown, we can assume there is a
category-aspect-for-temple. This aspect has some specific aspects for temple like aspect-for-temple-as-history-textbook and aspect-for-temple-as-religious-place as component. Since component aspects share domains, it is reasonable (but not mandatory)
that there are relations among concepts in different component aspects. Such relations
are contents of the category aspects.
Since combination and category aspects can use other combination or category aspects
as component, we can construct large aspects from relatively small aspects. We callsuch relatively large aspects as ontologies.
Figure 2 is an example how different ontologies can be defined with sharing aspects.
Two aspects can share aspects in their constitution, or be connected by category aspects.
We call these two aspects are compatible. That is, they may share or transfer information to each other.

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Hideaki Takeda

3.3 Logical Treatment of Multiple Aspects
We formalized aspect in a logical framework. The detail is shown in [Takeda et al.
1995]. The basic idea is use of modal logic. Combination aspect is represented as conjunction of logical theories of individual aspects. Category aspect is represented as
possibility of conjunction of logical theories of individual aspects. In both cases, we can
include some inter-aspect theory to describe relationship among individual aspects.
Then we can prove some theorems to describe relationship among such composite aspects.

3.4 Computational Treatment of Multiple Aspects
We also provided a language of aspects called ASPECTOL (Aspect-based Ontology
Description Language), which is an extension of Ontolingua-like ontology definition[Gruber 1992].
It extends Ontolingua to express the fragments of ontology as aspect. The other extension is to provide translation formula. A translation formula is defined between two
aspects in a category aspect, and is defined as define-translation which describes logicalrelation between concepts in both aspects. It can represent a class of direct relations
between formulae in two aspects which we have discussed in the previous section. A
left hand side of an implication formula is a formula of the aspect of the first argument
and a right hand side is a formula of the aspect of the second argument. The detail isalso shown in [Takeda et al. 1995].

4. StYLiD: Semantic Web Approach for Multiple
Ontologies
Many researchers in Semantic Web community are now aware that handling of multiple
ontologies is crucial for Semantic Web. The problem is often referred as ontology integration or ontology alignment or ontology matching. There are many studies such as[Wache et al. 2001] [Noy 2004] [Euzenat et al. 2007] [Ehring 2006] and workshops[Shvaiko et al. 2006] [Shvaiko et al. 2007]. As we discussed in the previous section,different ontologies can be formed based on different conceptualization. As long as
ontologies are based on a unique conceptualization, they can be either mapped, oraligned or even integrated in principle. But if not, such an operation is not possible per
se. There need extra knowledge.
We are interested more in conceptualization to form ontology. So we are now developing a system called StYLiD (“Structure Your Linked Data”) for sharing a wide variety
of structured information, i.e., representation of concepts and their instances [Shakya et
al. 2008].

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Ontology for People

Virtual Concept

7

Hotel

Virtual Concept

Hotel@user1

Hotel.1@user1
Hotel.2@user1
Name‐>(str)Hotel.3@user1
Address‐>…
Name‐>(str)Country
Address‐>…
Name‐>(str)‐>(Country)
Country
Address‐>…
‐>(Country)
Country‐>(Country)

Virtual Concept

Virtual Concept

Hotel@user2

Hotel.1@user2
Hotel.2@user2
Name‐>(str)Hotel@user3

Hotel.3@user3

Address‐>…
Name‐>(str)Country
Capacity‐>(num)‐>(Country)
Zip‐Code‐>(Zip‐Code)Name‐>(str)lat‐>(num)Long‐>(num)Fig3 : Variation of Concept and Virtual Concept

4.1 The Basic Idea
As we mentioned in Section 2, people are willing to share information via Web. Social
tagging and Wikipedia are good examples to demonstrate their behavior. Some people
just want to contribute other people or society but many people join to such activities
because they are fun to themselves. Representing own knowledge and organizing collected information by own are beneficial for themselves. Thereafter they recognize theimportance of information sharing. Information sharing from the beginning is difficultfor people to join [Takeda et al. 2005].
StYLiD4 is a service of sharing of structured information. It is similar to Freebase5 but avariety of structure for each concept is allowed. Any user can organize information
what they like. She can either use concept structure defined by other or modify it oreven create a new structure. All the variations for a single concept is maintained andtreated as a virtual concept. User can browse or search information either by virtual
concept or by individual concept. User can use virtual concept to specify range of property of a concept. The system solves ambiguity when it is needed.

-------------------------------4 The early prototype is accessible from http://dutar.ex.nii.ac.jp/stylid
5 http://www.freebase.com/

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Hideaki Takeda

4. 2 Consolidation of User Defined Concepts
Concepts having the same name defined by different users are grouped together by thesystem. This forms a single virtual concept which subsumes all the grouped concepts.
This consolidated concept can be used to retrieve all instances though different users
have different definitions for the concept.
All the concepts contributed by different users are visualized together as a concept
cloud. When the user hovers over any concept, the attributes and description are shown
so that it can be understood at a glance. Clicking on a concept shows all its instances.
Better concept definitions will satisfy more users and will have more instances. Popularity of concepts is visually highlighted by increasing size. Stable definitions will
gradually emerge from the vast cloud of concepts as more instances are contributed.
A consolidated concept can be expanded into a sub-cloud which shows all the versions
defined by different users. Further, in the sub-cloud, multiple versions defined by thesame user are sub-grouped together. The sizes of the different versions in a sub-cloud
add up to form the size of the consolidated concept. Clicking on a consolidated concept
shows instances of all versions of the concept. Similarly, we can see all instances of themultiple versions defined by a user.

5. Conclusion
In this paper, I argued ambiguity of representing concepts. Ontologists in computerscience, especially in Semantic Web dismissed the importance. They simplified conceptualization too much according to AI tradition. But I believe that richness of our conceptualization is crucial for representing ontology and can contribute various use of
ontology in computer systems.

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