IntergatingOntology

Int.J.Human-Computer Studies(2002)56,665–720doi:10.1006/ijhc.1010Available online at .onA cooperative framework for integrating ontologiesJ esualdo T oma s F erna ndez-B reis and R odrigo M arti nez-B e jar Departamento de Ingenier!ıa de la Informaci!o n y las Comunicaciones,Universidad de Murcia,30071-Espinardo(Murcia),Spain.emails:jfernand@dif.um.es,rodrigo@dif.um.es(Received21July1999and accepted in revised form8May2002)Nowadays,there are systems and frameworks that support Ontology construction processes.However,ontology integration processes have not sufficiently been specified to date.In this article,by making use of a cooperative philosophy,we describe a real framework for the integration of ontologies supplied by a predetermined set of(expert) users,who may be interconnected through a communication network.This framework is based on a set of well-defined assumptions that guarantee the consistency of the ontology derived from the ontology integration process.Moreover,in the approach presented here,every(expert)user may consult the so-derived ontology constructed until a given moment in order to refine his or her private ontology.In addition to this, the model proposed in this work allows the experts involved in the construction of the ontology to use their own terminology when querying the global ontology obtained until a given instant from their own co-operative work.The validation of the framework is also included in this work.#2002Elsevier Science Ltd.All rights reserved.KEYWORDS:ontology;knowledge integration;knowledge acquisition.1.IntroductionRecently,there has been increased interest in ontologies and in the idea of reusing existing(domain)ontologies(Crow&Shadbolt,2001).Moreover,there is a high level of consensus in considering that costly human resources as well as intense organizational work are normally required to construct ontologies.Thus,on the one hand,experts and knowledge engineers must work together in order to create an ontology.On the other,it is necessary to coordinate experts’work and to manage the informationflowing between human agents involved in the ontology’s construction.In this sense,a complex elicitation process between experts and knowledge engineers is usually required.This situation presents several problems,such as the need to have meetings between experts and knowledge engineers and to establish a consensus on the task schedule that must be adhered to in order to construct an ontology(Hameed, Sleeman&Preece,2001).Fortunately,experts’availability can be overcome to some extent by exploiting the possibilities of a communication network to which a set of user nodes is connected.In this manner,(expert)user nodes can be distributed in both space and time while all of them are solving a certain task.1071-5819/02/$-see front matter#2002Elsevier Science Ltd.All rights reserved.According to Reimer (1998),Knowledge Integration can be seen from two points of view:integration of different knowledge bases and integration of different representa-tions of the same knowledge at different formalization levels.This work is focused on the second perspective,that is,we will deal with the problem of ontology integration.Thus,the aim of this work is to use a set of end-users interconnected via a communication network to generate an ontology as a result of integrating a set of ontologies that can be provided by them.To be more precise,we will restrict ourselves to organizational and definitional aspects of a given set of ontologies,in order to obtain their integration into a global ontology,which will be sent to a certain user as a response to his or her information request.We therefore intend to perform ontological integration,which has been considered a complete process rather than a single ontological activity (Pinto &Martins,2001).Different authors have structured this process in different ways,although the community seems to agree on the objectives of such a process.For these authors,the process aggregates,combines and assembles together different source ontologies to form the resulting ontology,possibly after reused ontologies have undergone some changes.In McGuiness,Fikes,Rice and Wilder,2000,ontology integration consists of the iteration of three steps:(1)finding overlapping areas within the ontologies;(2)relating concepts;(3)checking the consistency,coherency and non-redundancy of the result.In the approach introduced here,the basic idea is that several experts on the same topic are encouraged to work on an ontology construction process in a cooperativeway.However,working cooperatively can give rise to several problems (Fern !andez-Breis &Mart !ınez-B !ejar,2000a ).Some of these are as follows.Redundant information .Two different experts might attempt to describe the same part of the domain knowledge.Given this eventuality,it would be desirable for the system to be capable of managing this possible situation so that redundancies could be e of synonymous terms for a concept.Apart from dealing with redundant information,different experts may employ different terminology for the same concept.In other words,there might be correspondence between different terms employed for a given concept (Shaw &Gaines,1989).During the ontology construction process,information concerning the use of synonymous terms for a concept must be stored and managed,since a particular terminology should not be imposed on any expert during the Knowledge Acquisition process.However,an ontology would strive towards ‘‘consensual knowledge’’,that is,a fixed terminology.Synonyms are possible but,ideally,everybody should agree on the terminology.The Ontolingua Server overcomes all of these problems (Farquhar,Fikes &Rice,1997),since there cannot be two identical concept identifiers.Concepts are internally represented as qualified concepts with respect to the ontology to which they belong.In addition to this,when a user manifests his or her intention to use an ontology in order to extend it,this system (i.e.the Ontolingua Server)never allows this end-user to add information that is inconsistent with the contents of the ontology that is being built.Every cooperative work-based tool should include a component that permits dialogue among the agents involved.This issue has been studied,for example,in the Ontolingua Server,which allows several users to modify the same ontology using the concept of a shared session.It also provides mechanisms that allow agents to J .T.FERNANDEZ-BREIS AND R.MARTI NEZ-BE JAR 666INTEGRATING ONTOLOGIES667 communicate via electronic mail.In Tadzebao and WebOnto(Domingue,1998),and KARAT(Abecker,Aitken,Schmalhofer&Tschaitschain,1998),the referred dialogue is considered as one of the most important activities.Thus,in KARAT,dialogue is promoted through knowledge sharing,making it visible to all agents.Moreover,in these systems,dialogue is established with the purpose of facilitating(refinement-oriented)discussion between two different experts over a pre-created ontology.From our perspective,cooperative dialogue possesses a different nature in that the agents are on one hand the collective of ontology suppliers(i.e.expert-users),and on the other,the global ontology generators.Benjamins and Fensel(1998)have presented(KA)2,an initiative for cooperative development of an ontology about research on Knowledge Acquisition.In this work, the use of the World Wide Web(WWW)is presented as an important factor for cooperative ontology development.Firstly,the WWW represents the most important knowledge source in the world.Secondly,the technology underlying the WWW allows cooperation in ontology construction processes,and the ontology is centralized while its instances are distributed over the WWW.ONTOBROKER(Fensel,Decker, Erdmann&Studer,1998)shows how distributed Organizational Memories can be consulted by any agent by means of the WWW.The structure of this paper is as follows.In Section2,the system implemented is presented.The implementation and evaluation processes are also briefly described.In Section3,the main assumptions considered in this work for integrating ontologies are described.Moreover,the framework,based on these assumptions,that permits integrating ontologies is put forward in this section.Section4shows an example that can help to understand how the framework and system work.Section5contains a brief comparison of our system with other ontological tools.Finally,in Section6we present some conclusions.2.The systemIn this section,the implementation and validation of the system are addressed.Also,an example of the way in which the system works is presented at the end of this section. The implementation subsection deals with kinds of system users and the facilities with which they are provided.The application of the system to different domains is described in the validation subsection.Finally,an example is shown,addressing the way in which the integration framework,formalized in the following section,works.2.1.IMPLEMENTATIONThe general objective of the application was to develop a system that could serve as a framework for cooperatively built,integration-derived(i.e.global)ontologies.In addition to this,we pursued a more ambitious goal in that every user could benefit from what other users had already contributed to an integration-derived ontology.In other words,the system was also designed to facilitate knowledge sharing.The system implemented does not allow a user either to modify another user’s contribution to a global ontology,or to see(ontological)knowledge,belonging toanother user,that has not already been incorporated into that particular ontology.Instead,the system makes it possible to access the benefits produced by the integration of knowledge contribution (of the set of users)represented by means of ontologies.In this system,two kinds of users are defined:normal and expert.Normal users are ‘‘information consultants’’,or people who are curious about some topic.On the other hand,expert users are ‘‘integration-derived ontology constructors’’,namely,those who generate knowledge for the system,so that normal users can consult it.Also,every expert user can obtain information about other expert users’contributions,provided that they have already input knowledge into an already existing integration-derived ontology.The decision about who is a normal/expert user is automatic.In this sense,as a result of an evaluation of the data supplied by a user,the system decides whether a user is normal or expert.Such an evaluation is founded on the fact that the class of appropriate experts is predefined by the system administrator(s)so that only those thought to be real experts can contribute to the ontology integration process.We have implemented a client–server infrastructure in such a way that each user corresponds to a client and the ontology construction engine is reflected in the server.In other words,every user only knows the existence of a server,but he/she cannot access another user;hence this user has to make service requests to the server (i.e.access is exclusively via the server).Expert users must enter their particular ontologies to the system by means of files.In particular,ontologies entering the system are constructed from a text file,so that given a so-constructed ontology the concepts contained in the file are in correspondence with the concepts in the (relevant)ontology,obtained by a level-by-level extraction process starting from the root node.In addition to this,users must specify in these files information about each concept,including its associated terms,its synonymous terms,its specific attributes,its mereological parents (if any),its taxonomic parents (if any)and its specializations .Given a concept,we call specialization a pair (concept,attribute )where concept stands for the concept from which the concept under question is a child;and attribute represents the attribute (i.e.the view)considered,thus establishing that the concept under question is a child’s concept of concept .The file format as well as the approach here presented have also been utilized elsewhere for enterprise modelling(Fern !a ndez-Breis &Mart !ınez-B !ejar,2000b ),and it can be described as follows.The file is comprised of the set of concepts which are part of the ontology.Each concept is defined through its attributes,its name and its parent concepts,either mereological or taxonomic ones.The following lines explain in more detail the information to be supplied for each concept.*Concept*Name of the concept.*Alternative names for the concept.Its format is as follows.–Number of alternative names.–List of alternative names.*Specific attributes.Format.–Number of specific attributes.–List of specific attributes’names.J .T.FERNANDEZ-BREIS AND R.MARTI NEZ-BE JAR 668INTEGRATING ONTOLOGIES669 *Mereological parents–Number of mereological parent concepts.–List of mereological parent concepts’names.*Taxonomic parents–Number of taxonomic parent concepts.–List of taxonomic parent concepts’names.*Specialization list*Number of specializations.*List of specializations.Figure1represents the menu options available for experts,while the options for normal users are constrained due to their role in the system.To be more precise,the latter cannot send new information in terms of ontologies to the server(update the server)because they are not experts,and given that they cannot have their own ontologyfile,they cannot view particular ontologies.They are only allowed to view the result of the integration process.Let us suppose that the user has selected the working topic.Once we have chosen to look up the integration of the previously selected topic (and if the process hasfinished successfully)the(normal or expert)user will be presented afirst visualization of the ontology like the one reflected in Figure2.The user may also change the name of the concepts belonging to the ontology presented. Finally,the ontology can be visualized as a tree,displaying all the available information regarding the selected concept from the tree.The user may also change the name of the concepts,but in this case possible names are restricted to a set defined by the current name and the alternative ones.2.2.EXAMPLESuppose that there are two different user-nodes,identified by UN A and UN B, respectively,working on the construction of an ontology about the Faculty of Sciences at the University of Murcia.Let us assume that the situation at t=t1is the one reflected in Figures3and4,where for every concept only the specific(i.e.non-inherited) attributes have been written in both mereological ontologies.Let us also assume that at t2>t1the user-node UN A wishes to view the(global)ontology constructed so far.Then,beby applying the proposed integration algorithm,the ontology shown in Figure5willF igure1.Expert user menu.F igure2.A visualization of a user ontology.built.That algorithm,which is detailed in further sections,can be summarized as follows.The first step is to select,amongst the set of ontologies to be integrated,the best subset of ontologies that can be integrated together,those that are neither equivalent nor inconsistent.The selected ontologies are inserted into a new ontology,which is called the integration-derived ontology.Then,the algorithm continues bydetecting F igure 4.Visualization of the ontology UN A by using the implementedsystem.synonymous concepts and transforming the ontologies.The result is that all of the ontologies will use the same terminology.This ontology is called the instantiated, integration-derived ontology.Thefinal step is to merge the branches from the previously obtained instantiated,integration derived ontology in order to unify knowledge and generate thefinal ontology,which is called the transformed,integration-derived ontology.Then,as the consult request comes from UN A,and by applying(some of)the definitions pointed out in the following section,this user node will be provided with the ontology shown in Figure6.It can be observed that the concept PEOPLE has been converted into PERSON in the ontology corresponding to UN B,since both concepts have been found to have the same attributes(according to the name-based equality criterion).After that,suppose that at t3>t2,an(external)end-user sends an information request to the system.Then,by applying the mentioned algorithm,he or she will be provided with the ontology depicted in Figure7(see also Figure8).Note that the ontology corresponding to UN B was selected as the one to which knowledge(e.g.the attribute HEIGHT)has been added,as this ontology has more nodes than the ontology corresponding to UN A.2.3.VALIDATIONThe system has been evaluated by people with different knowledge levels,but mainly by under-graduate and post-graduate students.The system has been used for1year by final(fifth)year Computer Science students and by some Ph.D.students.The range of domains to which the system has been applied ranges from information technology(e.g. videoconferencing)to medicine(e.g.Leukemia).The jobs consisted of building different ontologies about the domains assigned to the students according to the knowledge acquired from interviewing different experts.Each student was assigned a topic and his/her work consisted of interviewing an expert and building the corresponding ontology.Therefore,each student had to act as a knowledge engineer and acquire knowledge from the experts.Thus,once the students obtained the ontologies,other students assigned to the same topic had to use the system to achieve the integration of their results to see whether or not the system and underlying framework were useful for them.In some sense,when the students are using the system,they are acting as quasi-experts since their ontologies represent knowledge acquired from an expert.The framework used for validating the usefulness of the system and integration framework is based on the measurement of knowledge gained by users.The gain is measured by comparing the user’s ontology with the transformation-,integration-derived ontology,and then enumerating the number of concepts,relations and attributes that appear in the transformation-,integration-derived ontology that did not appear in the expert’s private ontology.Each domain required a different workload in order to generate the ontology so that the size of the ontologies obtained by each student depended on the domain.Thismade F igure 8.A transformed ontology constructed with the implemented system.INTEGRATING ONTOLOGIES 673the size of the ontologies not to be homogeneous for the different domains that were dealt with in this project.Therefore,we think that the results should not be analysed and compared in absolute terms but in relative ones.Thus,percentage values are presented as the result of the evaluation of the system and the methodology used for integrating knowledge represented by means of domain ontologies. Three domains have been selected for showing results:(1)videoconferencing over multicast networks;(2)integrated circuits;and(3)the climate.Let us introduce these domains.Videoconferencing over multicast networks.This application domain deals with the transmission of information for videoconferencing using multicast IP instead of using the traditional unicast IP.This job was assigned to two students who interviewed experts in this application domain.Integrated circuits.This domain was assigned to three students who interviewed different experts on the design and implementation of integrated circuits.These experts provided information about the characteristics of integrated circuits,the technology used for making them and so on.Climate.Two students were assigned the study of the climate and the different elements that determine the climate of a region and different atmospheric events such as storms,tropical rains and so on.As a result of their assignment,two ontologies were built and integrated by using our system.2.3.1.Measuring knowledge gain.This subsection explains how the knowledge that users gain by using the system is measured.For this,a weighted average of knowledge gain has been calculated according to the three knowledge categories used for making this evaluation,namely,concepts,attributes and relations.Moreover,the same procedure has been used for all knowledge categories.The formula is based on the following elements.*Value of a knowledge category in each expert ontology.*Value of a knowledge category in the transformed,integration-derived ontology. Let usfirst introduce the notation used in the formulae.n is the number of ontologies that take part in the integration process.|X i|stands for the number of knowledge entities[i.e.concepts(C),attributes(A)or relations(R)]in the ontology i.Thus,the number of concepts in the ontology i is written as|C i|,the number of attributes as|A i|,and the number of relations as|R i|. Therefore,X2{A,C,R},and i=1..n.Local gain(D i)represents the knowledge the expert i gains by using the integration framework.That is,the percentage of knowledge included in transformed,integration-derived ontologies but not in the ontology i.It can be considered the gain with respect to the ontology i;X2{A,C,R};i=1..n,and X int stands for the corresponding knowledge category in the transformed,integration-derived ontology.Therefore,the average-weighted gain is calculated by using the following formulae:gain¼P n1ðj X i jÂD iÞP n1j X i j;D i¼ðj X int jÀj X i jÞj X i j:J.T.FERNA NDEZ-BREIS AND R.MARTI NEZ-BE JAR674Let us explain the reasons for using these formulae.The local gain calculates the percentage of knowledge added by the transformed,integration-derived ontology, which is an appropriate manner for obtaining relative results.It was stated earlier that we are interested in obtaining relative values instead of absolute ones due to the fact that ontologies corresponding to different domains had a different size.Thus,the computation of the absolute knowledge gain produced,in our opinion,unreliable results.The knowledge gain is obtained for the three mentioned knowledge categories by calculating the weighted average of the local gains.The local gains are weighted in order to consider the different quantity of knowledge that each ontology contains.This consideration must be taken into account in order to calculate the results of a specific application domain for obtaining realistic results.Let us present an example.Let us suppose that there are two ontologies,namely O1 and O2,such that the number of concepts in O1,written|C1|,is108and the number of concepts in O2,written|C2|,is65.After integrating both ontologies,a transformed, integration-derived ontology,O int,has been achieved and the number of concepts in O int,D1¼135À108108*100¼25%;D2¼135À6565*100¼107%written|O int|,is135.Let us now calculate the local gain for O1and O2,namely D1and D2,respectively.Hence,the knowledge gain for the concepts can be calculated as follows:gain¼108*25þ65*107108þ65¼55:8%:A non-weighted average would have produced a66%gain but this would not have been very realistic because small ontologies would have had more influence than large ones on thefinal result.2.3.2.Results of the evaluation.The previous way of measuring knowledge gain has been applied to three selected domains,and the results are presented in Table1.As can be appreciated,using the system and the integration framework is an advantage for users because the transformed,integration-derived ontology has more knowledge than each individual ontology.A high value in the knowledge gain indicates that the different experts have specified different parts of the domain while a low knowledge gain indicates that the experts are describing the domain in a very similar way.Furthermore, some conclusions can be drawn from these results.T able1Evaluation results for the chosen domainsDomain Concepts(%)Attributes(%)Relations(%) Videoconferencing14.15025 Integrated circuits19.863.633 Climate55.836.354 INTEGRATING ONTOLOGIES675The higher rate of agreement between experts,and therefore a lower knowledge gain in this category,occurs with the concepts.This is due to different reasons.It is not very surprising that different experts find the same conceptual entities within a given domain,although,on the other hand,the synonym detection capability of the system can also perform this function.As for the results,the higher knowledge gain appears in the attribute category.The system can facilitate this disagreement.In the system,attributes are considered equivalent when they have the same name because they lack internal structure.On the other hand,it is more difficult to agree on the attributes than on the concepts,since each expert can define a concept from different,although compatible,perspectives;that is,they may share the definition of the basic characteristics of a concept,but each expert adds different minor characteristics to that concept.The results concerning relations are similar to the ones concerning concepts,because relations occurs among concepts,so that if experts find a similar set of conceptual entities in an application domain,the set of relations among them is likely to be very similar too.The nature of domains can be another reason,since the first two domains belong to the technology area,whereas the third one does not.In the third domain no agreement exists (i.e.climatic change)amongst the scientific community,although knowing in which category the largest gain occurs is not the main point of discussion.The system has also been directly evaluated by experts in different application domains,such as environmental planning.Thus,we prompted a set of experts located at different (geographic)sites to construct an ontology about visual landscape assessment,which is a subtask performed in environmental planning.These experts belong to different Spanish institutions such as the Technical University of Madrid,the Spanish Scientific Research Council and the University of Murcia.Consequently,this ontology has been crucial for a real project and a KBS for use in landscape assessment has been developed.As an example of the usefulness of this system,Figure 9shows a (real)concept hierarchy corresponding to an integration-derived ontology for this domain.Finally,it should be noted that the system has not been evaluated on large application domains due to the problematic nature of evaluating KA frameworks and tools (Shadbolt,O’Hara &Crow,1999).However,we are aware of the desirability of studying framework performance on integrating large ontologies (about 100–200concepts).3.Formalizing a framework for ontological integrationThis section provides the complete formalization of the system that has been presented in the previous section.This formalization process is carried out over various steps.First of all,the assumptions used in this work are presented in order to establish the basic notions that must be taken into account when reading further parts of this paper.These assumptions mostly deal with the ontological model that has been used for this research.Thus,the following sub-section represents the formalization of the ontological model.Therefore,all of the significant parts of an ontology (according to our model)will be formalized.Formalizing the ontological model has extreme significance for this paper since ontologies are the way in which we represent the domain knowledge.J .T.FERNANDEZ-BREIS AND R.MARTI NEZ-BE JAR 676Once the ontological model has been presented in a formal manner,the framework for integrating knowledge must be formalized too.Knowledge Integration is seen in this work as ontological integration,since knowledge is represented here by means of ontologies.Therefore,the defined and formalized framework is focussed on providing an infrastructure that allows for the integration of ontologies.So,the starting point of the integration framework is a set of ontologies belonging to different users.There may also be more than one ontology from the same user,but in this case the oldest one would be considered to have become obsolete and would therefore be discarded from the integration process.When the integration process is triggered by a user,this process will have to go through different steps before completing its task.One basic operation of the integration process is to compare the knowledge covered by different ontologies.Therefore,different functions to check for equivalence between ontologies or concepts are necessary,as well as functions to find inconsistencies between ontologies and concepts.Equivalencies and inconsistencies have been considered in this work from two points of view,namely,(1)attributes and (2)the organizational structure.Another function needed is one for deciding when two concepts are synonymous,which is a function that will permit users to use their own terminology,provided that the system is able to detect synonymy among concepts.The appendix contains a series of algorithms based on the definitions that appear across this section for the integration of knowledge represented by means of ontologies.In the following lines,we formalize everything that we have discussed until now.3.1.ASSUMPTIONS AND ONTOLOGICAL FUNCTIONS3.1.1.Assumptions .An ontology is viewed in this work as a specification of a domain knowledge conceptualization (van Heijst,Schreiber &Wielinga,1997).Inaddition,F igure 9.An ontology for the visual Landscape Assessment task from the vegetation–land use viewpoint.INTEGRATING ONTOLOGIES 677。