The paper proposes an analysis on some existent ontologies, in order to point
out ways to resolve semantic heterogeneity in information systems. Authors are
highlighting the tasks in a Knowledge Acquisiton System and identifying aspects
related to the addition of new information to an intelligent system. A solution
is proposed, as a combination of ontology reasoning services and natural
languages generation. A multi-agent system will be conceived with an extractor
agent, a reasoner agent and a competence management agent.Comment: 10 pages, exposed on 5th International Conference "Actualities and
  Perspectives on Hardware and Software" - APHS2009, Timisoara, Romani

Cicortas, Alexandru

Fortis, Alexandra Emilia

Iordan, Victoria Stana

English

arXiv

The paper proposes an analysis on some existent ontologies, in order to point out ways to resolve semantic heterogeneity in information systems. Authors are highlighting the tasks in a Knowledge Acquisiton System and identifying aspects related to the addition of new information to an intelligent system. A solution is proposed, as a combination of ontology reasoning services and natural languages generation. A multi-agent system will be conceived with an extractor agent, a reasoner agent and a competence management agent

Alexandru Cicortaş

Victoria Stana Iordan

Alexandra Emilia Fortiş

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     Anale. Seria Informatică. Vol. VII fasc. 1 – 2009 Annals. Computer Science Series. 7th Tome 1st Fasc. – 2009    79      C Co on ns si id de er ra at ti io on ns s   o on n   C Co on ns st tr ru uc ct ti io on n   O On nt to ol lo og gi ie es s     A Al le ex xa an nd dr ru u   C Ci ic co or rt ta aş ş1 1, ,   V Vi ic ct to or ri ia a   S St ta an na a   I Io or rd da an n1 1, ,   A Al le ex xa an nd dr ra a   E Em mi il li ia a   F Fo or rt ti iş ş2 2   1West University, Timişoara, Romania 2”Tibiscus” University, Timişoara, Romania   ABSTRACT. The paper proposes an analysis on some existent ontologies,  in  order  to  point  out  ways  to  resolve  semantic heterogeneity in information systems. Authors are highlighting the tasks in a Knowledge Acquisiton System and identifying aspects  related    to  the  addition  of  new  information  to  an intelligent system. A solution is proposed, as a combination of ontology reasoning services and natural languages generation. A  multi-agent  system  will  be  conceived  with  an  extractor agent, a  reasoner agent and a competence management agent.    Keywords: learning ontologies, knowledge acquisition, multi-agent systems   Introduction  The need for increasing the cognitive support knowledge engineering is a real  requirement  and  a  visual  representation  is  a  real  need.  Between knowledge representations are: Sowa based on the KIF [Gin91], CODE4 [SL95], focused in more detail on the user experience, and also combined it with a logically rigorous representational semantics.   In many domains like business-to-business e-commerce are required dynamic  and  open-interoperable  information  systems  that  are  service-oriented. Services are often made of sub-services and tasks that normally belong to autonomous participants.  Inevitably the underlying information systems are distributed and autonomous. The semantics of diverse information sources is captured by their ontologies, i.e., the terms and relationships between them [CB00]. In tightly coupled applications, the intended meaning of a term is often implicit, thus      Anale. Seria Informatică. Vol. VII fasc. 1 – 2009 Annals. Computer Science Series. 7th Tome 1st Fasc. – 2009    80 relying  on  developer’s  mutual  agreement.  In  a  distributed  environment mutual agreement is hard to come by if not impossible. Thus it is crucial for the domain model and the vocabulary to be represented in such a way that enables  programs  to  reuse  them  as  they  were  originally  intended  with minimum human intervention during their execution.  Semantic heterogeneities represent another major problem that must be carefully analyzed. Heterogeneity in many domains is inevitable because the  concerned  systems  are  often  developed  by  autonomous  participants. Semantically equivalent concepts: •  Different  terms  are  used  to  refer  the  same  concept  by  two models.  These  terms  are  often  called  synonyms.  However, synonyms  in  their  common  usage  do  not  necessarily  denote semantically equivalent concepts. •  Different  properties  are  modelled  by  two  systems (heterogeneity).  As  an  example,  for  the  same  product,  one catalogue has included its colour but the other has not.  •  Property-type mismatches. For example, the concept length may be given in different units of measure. Semantically  unrelated  concepts  here  the  conflicting  term  are  a concept.  The  same  term  may  be  chosen  by  two  systems  to  denote completely different concepts. Semantically related concepts are: •  Generalization and specification. As an example is that student in one system refers to all students, but the other only to PhD students. •  Definable terms or abstraction - A term may be missing from one ontology,  but  which  can  be  defined  in  other  terms  in  the ontology, •  Overlapping concepts.  •  Different  conceptualisation.  Example:  one  ontology  classifies person  as  male,  female,  and  the  other  person  as  employed unemployed. Ontology can be seen as a way to resolve semantic heterogeneity by specifying explicitly the semantics of the terms used in information systems. Between multiple ontology definitions the two following seem to be useful for  our  intentions.  Ontology  is  an  explicit  specification  of  a conceptualisation [Gru92]. Ontology is a logical theory accounting for the intended meaning of a formal vocabulary, i.e., its ontological commitment to a particular conceptualisation of the world [Gua98].       Anale. Seria Informatică. Vol. VII fasc. 1 – 2009 Annals. Computer Science Series. 7th Tome 1st Fasc. – 2009    81 1 Previous Works  In [All03] was done an analysis of  the visualization tools for knowledge engineering, from that the lack of an established theory about user tasks and the cognitive support they require was revealed. Also were identified many difficulties  encountered  when  performing  user  testing  in  the  knowledge engineering domain, including gaining access to expert users, generalizing results over different domains and quantifying the knowledge acquired and used by such tools.  These  issues  recommend  focusing  on  more  qualitative  approaches which included a user survey, two contextual inquiries, and investigation of related work; using these different techniques provided a series of useful perspectives  on  the  problem.  There  exists  a  wide  variety  of  users  and domains to which ontology engineering is being applied, and further, that visualization is a desired feature. Based on an analysis of the Knowledge Acquisition system [TKG01] the experimenters observed users performing the following high-level tasks: •  understanding the given knowledge acquisition task; •  deciding how to proceed with the knowledge acquisition task; •  browsing the knowledge base to understand it; •  browsing the knowledge base to find something; •  editing (create or modify) a knowledge base element; •  checking  that  a  modification  had  the  expected  effects  on  the knowledge base; •  looking around for possible errors; •  understanding and deciding how to fix an error; •  recovering from an error by undoing previous steps (to delete or restore a knowledge base element); •  reasoning about the system. In  [BKR01]  were  identified  some  typical  concerns  that  users  may have when adding new knowledge to an intelligent system. Some of these concerns were that the users do not know where to start and where to go next, the users do not know if they are adding the right things and the users often get lost as it takes several steps to add new knowledge. Here is clearly shoed that the standard knowledge engineering methodology, consisting of the  steps  of  modelling,  acquiring  and  verifying  knowledge,  fails  to accommodate the specific needs of users, even modellers in the domain. It is no use to have a crisp and detailed methodology if users cannot easily make use of it in any one of its stages.      Anale. Seria Informatică. Vol. VII fasc. 1 – 2009 Annals. Computer Science Series. 7th Tome 1st Fasc. – 2009    82 In  [Ng00],  based  on  an  evaluation  of  user  requirements  in  ontology modelling tasks, was designed a tool, Info Lens, to browse description logic ontologies (using a combination of lenses) which revealed different information about  the  domain  as  they  were  interactively  moved  about  the  model representation.  One  issue  was  scalability  for  practical  sized  systems.  For cognitive support specific tasks, were identified the need for a tool to support information integration (between different representations), to support the often cyclic task-switching between navigation (around the model) and visualization (of a specific aspect of the model). Initial user surveys were quite positive but some  aspects  of  the  implementation  hindered  the  evaluation.  In  [C+01]  is described a graphical tool for knowledge acquisition. Although they only tested it on four users, and those users were not modellers or knowledge engineers, we still present the results for the insight it offers into the benefit of increased cognitive support. The users were able to enter a few hundred concepts into a large medical knowledge base within a week, and also verify the model using competency questions. As major problems were extracted the basic machinery works, providing a basic vehicle for axiom-building without the users having to encode axioms directly or even encounter terms like concept, relation, instance, quantification. Also  were  identified  as  areas  needing  improvement  include multifaceted  representations,  active  critiques  from  the  system  and  more expressivity in the interface such as temporal relations and conditions. Protege  is  an  ontology  engineering  and  knowledge  acquisition  tool created  at  Stanford  University  [G+03].  It  uses  a  frame-based  knowledge representation  formalism to  allow  users  to model  domains  using  classes, instance, slots (relations) and facets (constraints on the slots). Written in Java,  its  architecture  allows  for  extensions  to  be  added  via  a  plug-in metaphor. Recently, work has been ongoing to make the tool compatible with the OWL ontology language for the Semantic Web (a key component), as well as support web-specific concepts such as namespaces and Universal Resource Indicators (URIs). More details are available in [KMN03].    2 Managing ontologies  Ontology is seen as domain oriented concepts. It includes abstract concepts and specifies domain-level constraints that can be used for knowledge-level reasoning;  Ontology  is  suited  to  represent  high-level  information requirements.  Schemas  and  classes  are  data-level  concepts  that  are implementation  dependent.  They  are  designed  to  optimise  procedural      Anale. Seria Informatică. Vol. VII fasc. 1 – 2009 Annals. Computer Science Series. 7th Tome 1st Fasc. – 2009    83 operations.  Constraints  at  this  level  are  operational  constraints.  Many domain  constraints  are  not  explicitly  represented  at  this  level.  The relationship  between  ontology  and  the  underlying  data  sources  are represented in the following figure:    Figure 1: The relationship between ontology  In addition, DOME ontologies form a hierarchy as shown in Figure 2.    Figure 2: Ontology hierarchy   3 Learning ontologies  Besides  learning  ontologies  from  existing  data  sets,  we  can  also  reuse existing ontologies available from the Web [SP07]. The first step is to get the  candidates  by  using  ontology  search  tools  like  OntoSearch  [T+04],      Anale. Seria Informatică. Vol. VII fasc. 1 – 2009 Annals. Computer Science Series. 7th Tome 1st Fasc. – 2009    84 [JT06]. The major problem consists from finding the way that allows to the ordinary users, who may not know OWL at all, decide which ontology suits their  application  best.  A  good  solution  [P+02]  is  to  combine  ontology reasoning services and natural language generation to provide human read able presentation of parts of ontologies. Ontology takes the form of a set of logical  axioms,  and  so  the  challenge  is  to  present  the  material  of  these axioms in comprehensible way using a language such as English. However, it is important to take on board the fact that the axioms may not come in a form  ready  for  direct  realisation  in  English.  The  axioms  represent  one possible  way  that  the  material  could  have  been  expressed,  but  there  are many other possible ways that this could have been done equally well.   4 Contributions  In  the  domain  we  had  many  papers  [CIN08],  [IC08]  that  treats  the competence representation and description using ontologies. As we stated above,  based  on  the  remarks  from  [OM06]  and  in  accordance  with  our intentions, the following is proposed.  The  problem  statement  can  be  summarized  as  follows:  own  the ontology in order to construct a tool that generates another ontology based on appropriate inference and reasoning. As a simple example in education: having many course descriptions define  the  skills  and  the  capabilities  and  based  on  these,  derive  the competences that can be obtained attending these courses. In one of the previous sections were presented the operations on the ontologies.  As  it  can  be  seen  we  propose  another  operation  deriving ontology from other one. For that we have at least two possible solutions: •  conceive an expert system with appropriate goals; •  conceive  some  intelligent  agents  that  are  able  to  do  it  in  an appropriate context. The multi-agent system has as main goal to derive ontology from another one in the following way. It will extract from course descriptions the  possible  skills  and  capabilities.  From  the  skills  and  capabilities,  the competences  that  are  acquired  which  are  expressed  in  terms  of  a  new ontology. The system has three agents: Extractor, Reasoner and Competence Management Agents.       Anale. Seria Informatică. Vol. VII fasc. 1 – 2009 Annals. Computer Science Series. 7th Tome 1st Fasc. – 2009    85  Fig. 3: Multi-Agent system for extracting the competences Extractor Agent Ontology terms Reasoning rules Competences repository Extracting basic skills Generic Extraction  rules Basic skills Reasoning about basic skills Reasonner Agent Reasoning skills Competence Management Agent Matching skills with competences Resulted competences New competence ontology generated  Refined the  competences      Anale. Seria Informatică. Vol. VII fasc. 1 – 2009 Annals. Computer Science Series. 7th Tome 1st Fasc. – 2009    86 As is shown in the figure 3, the ontology that describes the courses and the generic rules are used by the Extractor Agent. It extracts the skills and capabilities that are obtained after attending these courses. After it, the Reasoner  Agent  defines the  possible  competences  from  the  skills.  These competences  are  refined  based  on  the  comparisons  with  the  similar competences  that  exist  in  the  Competence  repository  and  the  resulted ontology  (of  the  new  competences)  is  obtained.  Our  model  has  some similitude and some functionality like the model presented in [L+05]. As basis for information and knowledge representation, the XML will be used. The main motivation is due to the fat that on the Internet the information must be extracted processed and presented in some specific form. Based  on  it  the  agents  will  be  able  use  the  information  for communicating each other and with the users.   Conclusions  Ontologies are frequently used in design of complex systems especially in the case of agent usage. Due to the similarities between the ontologies and competences, the ontology construction can be also used for competence construction. The multi-agent concepts are used in ontology  construction and based on it the different tools was developed. Future  our  works  will  concentrate  on  the  refinement  of  the  agent capabilities for construction the ontologies and competences.   References  [All03]     Allen, M.M. - Empirical evaluation of a visualization tool for knowledge engineering, M. Sc., University of Victoria, 2003  [BKR01]  Blythe, J., Kim, J., Ramachandran, S., Gil, Y. - An integrated environment  for  knowledge  acquisition,  In:  International Conference on Intelligent User Interfaces. San Francisco, CA  [CIN08]     Cicortaş, A., Iordan, V., Naaji, A., - Ontologies for Competence Description  in  Multi-agent  Systems,  Proceedings  of  the  10th Wseas  International  Conference  Mathematical  Methods  and Computational  Techniques  in  Electrical  Engineering,  Sofia,      Anale. Seria Informatică. Vol. VII fasc. 1 – 2009 Annals. Computer Science Series. 7th Tome 1st Fasc. – 2009    87 2008, ISSN: 1790-5-117, ISBN:978-9606766-602, ISI Thomson proceedings.  [C+01]    Clark, P., Thompson, J., Barker, K., Porter, B., Chaudhri, V., Rodriguez,  A.,  Thomere,  J.,  Mishra,  S.,  Gil,  Y.,  Hayes,  P., Reichherzer, T., - Knowledge entry as the graphical assembly of components,  In:  First  International  Conference  on  Knowledge Capture (K-Cap’01). 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[Gua98]      Guarino,  N.  -  Formal  Ontology  and  Information  Systems,  In Proceedings  Conference  on  Formal  Ontology  (FOIS’98),  Ed., Trento, Italy, 1998.  [IC08]        Iordan,  V.,  Cicortaş,  A.,  –  Ontologies  used  for  Competence Management, Acta Polytechnica, Journal of Applied Sciencea at Budapest  Tech,  Hungary,  ISSN  1785-8860,  Volume  5,  Issue No.2, 2008.  [JT06]       Jiang, X., Tan, Ah-T., - OntoSearch: A Full-Text Search Engine for the Semantic Web, Proceedings of the Twenty-First AAAI Conference on Artificial Intelligence July 16–20, 2006.      Anale. Seria Informatică. Vol. VII fasc. 1 – 2009 Annals. Computer Science Series. 7th Tome 1st Fasc. – 2009    88  [KMN03]  Knublauch, H., Musen, M.A., Noy, N.F., - Creating Semantic Web (owl) ontologies with Protégé, In: International Semantic Web Conference Tutorial, Sanibel Island, FL, 2003  [L+05]       Laclavik, M., Zoltan Balogh, Z., Giang, N.T., Gatial, E., Hluchy, L.,  -  Methods  for  Presenting  Ontological  Knowledge  to  the Users,  L.Popelinsky,  M.Kratky  (Eds.):  Znalosti,  Proceedings, VSB-Technicka  universita  Ostrava,  Fakulta  elektrotechniky  a informatiky,  2005,  ISBN  80-248-0755-6,  Vysoke  Tatry, Slovakia  [Ng00]       Ng, G. K.-C., - Interactive visualization techniques for ontology development, Ph.D. Thesis, University of Manchester, 2000  [OM06]      Obitko,  M.,  Mafik,  V.,  Integration  of  Multi-Agent  Systems: Architectural  Considerations,  Emerging  Technologies  and Factory Automation, ETFA '06., 2006  [P+02]     M. Paolucci, M., T. Kawamura, T., Payne, T. and Sycara, K., Importing the Semantic Web in UDDI, In Proceedings of the Workshop of Web Services, E-Business, and the Semantic Web, 2002.  [SP07]       Sabou,  M.,  Pan.  J.,  -  Towards  semantically  enhanced  Web service  repositories,  Web  Semantics:  Science,  Services  and Agents on the World Wide Web, No. 5, 2007.  [SL95]        Skuce,  D.,  Lethbridge,  T.C.,  -  Code4:  a  unified  system  for managing  conceptual  knowledge.  International  Journal  of Human Computer Studies 42, 1995,   [TKG01]   Tallis, M., Kim, J., Gil, Y., - User studies of knowledge acquisition tools: methodology and lessons learned, Journal of Experimental and Theoretical Artificial Intelligence, 2001, 13 (4)  [T+04]     Thomas, E., Zhang, Y., Wright, J., McKenzie, K., Preece, A., Sleeman, D., - OntoSearch: a Semantic Web Service to Support the Reuse of Ontologies, First AKT Workshop on Semantic Web Services, AKT-SWS04, December 8, 2004. 

Considerations on Construction Ontologies

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