[IEEE 2005 International Conference on Neural Networks and Brain - Beijing, China (13-15 Oct. 2005)] 2005 International Conference on Neural Networks and Brain - Study on Data Exchanging based on SoftMan

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  • Study on Data Exchanging based on SoftManZhonggui Ma Bin Ye

    Department ofAutomatic ControlBeijing Institute of Technology

    Beijing 100081, ChinaEmail:mzg1216@sohu.com


    Abstract-Architecture of data exchanging based onSoftMan has been put forward, and different applicationsystems can exchange data in XML by corresponding adapterSoftMan. Data exchanging flow chart has been given. QuerySoftMan, the core of data exchanging, has been represented byquery model which is a concept-based rule engine. At last,ontology of petroleum industry has been built, and it can use toexplain data items, allow flexibility of physical expression,allow diversity of physical expression, and liberate fromrequiring every application to use the same terminology. Thus,using the unifying ontology, any two application systems caninteroperate seamlessly. Each application system maps to theontology, rather than to each other.


    The term "SofiMan" was first put forward by GuangpingZeng and Xuyan Tu. Its basic concept is developing a kindof automated intelligent tool named "SoftMan" in theinternet on the basis of Artificial Intelligence, Artificial Lifeand Distributed System, combined intelligent robot,intelligent net with Multi-Agent. This kind of SoftMan canmove freely, and deal with some tasks automatically usinginformation push-pull technology. It can act as certain officeclerk, such as safety policeman, garbage collector,information waiter in the internet, and so on[1].

    In a SoftMan system, SoftMan can solve distributedproblems by coordination and cooperation. The conformityof multi-source complex information is the core in the keytechnology of digital gas field, it directly restrict the successof production control, decision-making and data mining, andits purpose is to achieve the conformity of multi source andmultilayer data, and make existing application exchangedata one another. One way to solve this problem is to builduniform data format(XML Schema) in data exchangingcenter, and build corresponding relation between uniformdata format and its own data format in every applicationaccordingly. However, as a new application system joins,new mapping relations must be built, so it is different tomaintain. So we want to settle this problem by ontology.The strategy is quite simple: all application systemsparticipating in data exchanging can compose any physicalexpression, provided the physical expression does notviolate the fundamental data relationships defined in theontology. Thus, using the unifying ontology, any two

    Zongjie Wang Xuyan TuSchool of Information Engineering

    UTniversity of Science ak Technology BeijingBeijing 100083, China

    Email: wangzongjie@vip.sina.comtuxuyan@sina.com

    application systems can interoperate seamlessly. Eachapplication system maps to the ontology automatically,rather than to each other.

    With this small collection of items you are able toseamlessly interoperate. Further, there is no need for allparties to agree to a common message format. Each partycan create physical expressions in a manner that best meetstheir needs and desires.


    In SoftMan system, ontology serves as the domainknowledge for organizing semantically interrelated databeing exchanged. Compared to the xml schema, ontologycan describe more complex objects due to its richersemantic constructs.

    A. Definition ofontologyOntology specifies a rich description such as terminology,

    concepts, relationships between the concepts and rules. Inthe following, we give the formal definition of ontology.

    Definition 1 (Ontology) An ontology consists of 6elements {C, Ac, R, AR, H, X}, where C represents a set ofconcepts; Ac represents a collection of attribute sets, one foreach concept; R represents a set of relationships; ARrepresents a collection of attribute sets, one for eachrelationship; H represents a concept hierarchy; and Xrepresents a set of axioms.

    Each concept ci in C represents a set of objects of thesame kind, and can be described by the same set ofattributes denoted by AC(ci). Each relationship ri(cp, cq) in Rrepresents a binary association between concepts cp and cq,and the instances of such a relationship are pairs of (cp, cq)concept objects. The attributes of ri can be denoted by AR(ri).H is a concept hierarchy derived from C and it is a set ofparent-child (or superclass-subclass) relations betweenconcepts in C. (cp, cq) E H if cq is a subclass, orsub-concept, of cp. Each axiom in X is a constraint on theconcept's and relationship's attribute values or a constrainton the relationships between concept objects. Eachconstraint can be expressed like a Prolog-like rule [2].

    B. Representation ofontology

    0-7803-9422-4/05/$20.00 2005 IEEE1411

  • This ontology can be represented in any ontologyrepresentation language such as RDF(S) [3], DAML+OIL [4]and others. In this paper, we only focus on the concept,relationship and attribute entities of an ontology since theyare the usual entities involved in annotation. Axioms, on theother hand, are useful when reasoning based on ontology isrequired. So we use DAML+OIL to represent ontology.DAML+OIL is a semantic markup language for resources. Itbuilds on earlier W3C standards such as RDF and RDFSchema, and extends these languages with richer modelingprimitives. DAML+OIL provide modeling primitivescommonly found in frame-based languages. A DAML+OILknowledge base is a collection of RDF triples. DAML+OILprescribe a specific meaning for triples that use theDAML+OIL vocabulary.


    Fig. 1 shows the architecture of data exchanging based onSoftMan. And different application systems exchange data(receive data or send data) in XML by correspondingadapter SoftMan. Data exchanging center communicate withapplication systems by interface SoftMan. Validate SoftManchecks the received XML whether it is valid by XMLSchema. If received XML is invalid, query SoftMan willquery ontology repository to match according to XMLSchema. If successful, convert SoftMan will convertreceived XML to standard format, and exchange with centerdatabase.

    Fig. 1. Architecture ofData Exchanging based on SoftMan


    A. Data exchangingflow chartFig.2 shows the data exchanging flow chart. First,

    interface SoftMan receives exchanging data sent bycorresponding adapter SoftMan. Validate SoftMan checksthe received XML whether it is valid by XML Schema. Ifreceived XML is valid, it can exchange with center databasedirectly. And if received XML is invalid, query SoftManwill query ontology repository to match according to XMLSchema. Second, if all items can be matched, convertSoftMan will convert received XML to standard format, andexchange with center database. If part of items can't bematched, it implies data exchanging is not successful.Finally, data exchanging center inform correspondingapplication system the result.

    Fig.2. Data Exchanging Flow Chart

    B. Query ModelIn fact, In Fig.2, Query SoftMan is the core of data

    exchanging. In this paper, it can be represented by querymodel. Query model is a concept-based rule engine, consistsof Jena and Jess, as Fig.3.

    1) XML+ namespace + xml schema: We use XMLwith namespace to represent exchanging data, and use XMLSchema to validate the XML.

    2) RDF + RDF Schema: RDF is an assertionallanguage intended to be used to express propositions using


  • precise formal vocabularies, particularly those specifiedusing RDFS, for access and use over the World Wide Web,and is intended to provide a basic foundation for moreadvanced assertional languages with a similar purpose.

    3) Rule Markup Language: Rule Markup Language isa markup language for publishing and sharing rule bases onthe World Wide Web. Rule Markup Language builds ahierarchy of rule sublanguages upon XML, RDF, XSLT,and OWL. Rule Markup Language uses Datalog as thekernel of its family of sublanguages. Its syntax is defined byan XML Schema. Its semantics is defined via Herbrandmodels.

    4)Jena: Jena can store data of RDF and represent RDFgraphs and write in N-Triples format. we can load aDAML+OIL ontology in Java using Jena easily.

    5)Jess: Jess is a rule engine and scripting environmentwritten entirely in Java language, and it supports thedevelopment of rule-based expert systems which can betightly coupled to code written in the powerful, portableJava language. Jess as CLIPS uses the Rete algorithm toprocess rules, a very efficient mechanism for solving thedifficult many-to-many matching problem.

    Fig.3. Query Model Architecture


    Building ontology of petroleum industry need manydomain experts, and it is a huge project, but we try to buildan ontology to meet our need, and it can be use to explainexchanging data items. Part of our building Ontology asfollows:

  • For a given ontology there are many possible physicalexpressions. Suppose that our application receives aphysical expression from the other application. Further,suppose that my application has been coded to understandthese terminologies: GasWell, production,geographic setting; the other application has takenadvantage of XML's flexibility and has elected to use otherterminologies: TypeA Well, output, location. As ourapplication parses the XML document that it received fromthe other application it encounters . Itdoesn't "understand" TypeA-Well so it "consults" thepetroleum Ontology:

    "What do you know about TypeA_Well?"The Ontology returns:"TypeA Well is a type of GasWell."This knowledge provides the link for our application to

    understand the relationship between something it doesn'tknow TypeA_Well and something it does know GasWell.

    It encounters < output >. Again, our application was notcoded to understand output, so it consults the petroleumOntology:

    "What do you know about output?"The Ontology returns:"Output is synonymous with production."Once again, this knowledge serves to bridge the

    terminology gap between something our application doesn'tknow and something our application does know. And so on.At last, Convert SoftMan executes translation, and generatesour application can recognition data format.

    Science & Technology Tacking Key Project of the TenthFive years Plan under grant no. 2004BA616A-1 1.


    [1] Guangping Zeng and Xuyan Tu, "SoftMan", Proceedings of10th CAAIConference, Beijing: Beijing University of Posts andTelecommunications Publishing House,2003, pp.677-682.

    [2] I. Bratko, Prolog Programming for Artificial Intelligence, PearsonEducation Limited, 2000.

    [3] RDF Semantics, W3C Recommendation, February 2002.http://www.w3.org/TIRrdf-mt/.

    [4] D. Connolly, F. V. Harmelen, I. Horrocks, et al, Annotated DAAML+OILOntology Markup, December 2001.http:llwww.w3.org/RTdaml+oil-walktbru/.

    [5] Zhongzhi Shi, Intelligent Agent and its Applications, Beijing: SciencePublishing House, 2000.

    [6] Guowei Yang, Model of Artificial Life, Beijing: Science PublishingHouse, 2005.

    [7] D. Fensel, J. Hendler, H. Lieberman, et al, Semantic Web Technology,Boston: MIT Press, 2002.


    Architecture of data exchanging based on SoftMan hasbeen put forward, and different application systems canexchange data in XML by corresponding adapter SoftMan.Data exchanging flow chart has been given. Query SoftMan,the core of data exchanging, has been represented by querymodel which is a concept-based rule engine. At last,ontology ofpetroleum industry has been built, and it can useto explain data items, allow flexibility of physicalexpression, allow diversity of physical expression, andliberate from requiring every application to use the sameterminology. Thus, using the unifying ontology, any twoapplication systems can interoperate seamlessly. Eachapplication system maps to the ontology, rather than to eachother. It is difficult to build ontology of some industry, so weonly built some concepts and relations which have beenused in our project. In future, we will continue to extendontology of petroleum industry, and make it inferencestronger. It can be used for reference for other domains.


    This work was supported in part by the National ScienceFoundation under grant no. 60375038 and China National



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