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normalized, two concepts can be easily compared to check wither the subsumption relationship holds between pairs of them or not: giving A = ( and A1,A2,A3,...,An) and B = ( and B1,B2,B3,...,Bm), the test “does the concept A subsume the concept B?” returns “true”, if and only if ∀Ai (i ∈ 1,...,n) ∃ Bj (j ∈ 1,...,m) such that Bj ⊑ Ai. The implementation of this process uses an array of Boolean (called “Table Of Test” further) to record the result of the subsumption relationship evaluation (see figure 5). In that figure, C1,C2,C3, ··· ,Cn denote the query concept under its normal form and D1,D2,D3,...,Dm denotes the concepts “known from” the mediators, i.e. every Dj has to be viewed under its normal form D 1 j ,D2 j , ··· ,Dnj j . Then Table Of Test[Dj,Ci] = true means that D i j ⊑ Ci. When the value returned by the function Subsumes(C, Dj) is “false” (i.e. the concept Dj does not fully satisfy the concept C.), therefore we need to determine a possible complement of Dj relatively to C. Using the Table Of Test it is easy to get the complement of the concept Dj relatively to the concept C. For example, “MODERN-TEAM subsumes SMALL-TEAM” is false, “(atmost 4 member), (atleast 1 chef) and (all chef WOMAN)” is false, then Comp(MODERN- TEAM,SMALL-TEAM)=(and (atmost 4 member) (atleast 1 chef) (all chef WOMAN)). Then using Table Of Test the definition of Comp() is: Comp(C, D) = � r k=1 Ck, ∀k|T ableOfT est[k] = false 4 . C1 C2 ... Cn D1 False False ... True D2 False True ... True ... ... ... ... ... Dm False False ... False ORoD False True ... True ORoS ANDoS True False Figure 5: Three parameters of “Table Of Test” That means that the complement is given by the conjunction of all the atomic concepts for which the corresponding values in “Table Of Test” are “false”. The composition of the truth values will permit us to determine the cases of satisfaction. Let’s consider a table ORoD[1..n] as ORoD[i] = �m j=1 T [Dj,Ci]. ORoD[i] = true means that the concept Ci is satisfied by at least a Dk. If the conjunction of 4 In the following, we will use T[x] instead of Table- OfTest[x] FEDERATED MEDIATORS FOR QUERY COMPOSITE ANSWERS MSSC MGSC ORoS ANDoS CASE X X True True 1 X ⊥ True True 2 ⊤ ⊥ True True 3 ⊤ ⊥ True False 4 ⊤ ⊥ False False 5 Figure 6: The analyse of the satisfaction case �the values of ORoD, noted ANDoS, is true (i.e. n i=1 ORoD[i] = True), it means that all the Cisare satisfied and therefore the query. When ANDoS is false, the logical disjunction of the values of ORoD, noted ORoS, enables to determine a possible partial satisfaction. Indeed if ORoS = �n i=1 ORoD[i] = True, it means that there exist some Ck that are satisfied. If both ORoS and ANDoS are false then no atomic concept Dk j (j ∈ 1..m) satisfies a Ci. The figure 65 summarizes this discussion (in this figure, the numbers in the CASE column refers to the satisfaction cases listed in section 2). By this analyse with the classification algorithm (Boudjlida, 2002), we can get a complete method to determine the satisfaction cases that were listed in section 2. 3.3 Status of The Implementation 265 Based on these algorithms, an experimental platform has been developed in Java. Some services, like testing the subsomption relationship, determining the complement of a concept and computing a composite answer, have been implemented on this platform. All these services can be accessed through a Web Server in a “classical” client/server architecture over the Web. The services accept the DL concepts written DAML+OIL (Horrocks, 2002a), an ontology language in XML. The DL concepts are encoded in XML and transmittedtotheWeb Server who in turn transmit them to the appropriate mediator service. Then a normalization class transforms them into Java objects and an experimental knowledge base, described in XML, is loaded and normalized into a TBox object when the service is started. All the algorithms of the mediator’s services are implemented in the TBox class. The services’ outputs are also encoded in XML. Furhermore, XML is also used to exchange information and concepts between the mediators when mediators’ copperation is needed. For example, in the service that computes a composite answer for a query concept, if the query concept is not satisfied in the local knowledge base, the complement concept is sent to next mediator as 5 In this table, X is a concept, ⊤ is the concept TOP and ⊥ is the concept BOTTOM.
266 ENTERPRISE INFORMATION SYSTEMS VI an XML focument. In the current status of the implementation, a mediator “discovers” an other mediator using a static mediator address list. More complex and dynamic discovery techniques will be supported in the coming versions. Moreover, we deliberately ignored the search of the actual individuals (ABox) that satisfy a query, i.e. in the current work, we only consider TBoxes. 4 CONCLUSION In this paper, we presented a method and an algorithm for testing the subsumption relationship, determining concept complements and finding composite answers. Some service of subsumption test and determine Complement has been implemented in Java, that is based on a normalization-comparison algorithm and we can access these services by Web. One of the originality of this work is in the type of query answering we provide and also in the way we used and implemented the complement concept. Indeed, to the best of our knowledge, using the complement concept for query answers composition does not figure in the literature we had in hands nor in systems that implement DLs, like RACER (Haaslev and Moller, 2003) one of the most representative DL system. Future work may consider the complexity of the algorithm, and the cooperation of mediators in heterogeneous environments, i.e. environments where mediators’ knowledge bases are described in different languages. REFERENCES Beeri, C., Levy, A., and Rousset, M.-C. (1997). Rewriting Queries Using Views in Description Logics. In ACM Symposium on Principles Of Database Systems, pages 99–108, Tucson, Arizona. Borgida, A. (1995). Description Logics in Data Management. IEEE Transactions on Knowledge and Data Engineering, 7(5):671–682. Borgida, A. and Devanhu, P. (1999). Adding more ”DL” to IDL: Towards more Knowledgeable Component Interoperability. In 21rst International Conference on Software Engineering, ICSE’99, pages 378–387, Los Angeles, CA. ACM Press. Bouchikhi, M. and Boudjlida, N. (1998). Using Larch to Specify the Behavior of Objects in Open Distributed Environments. In Proceedings of the 1998 Maghrebian Conference on Software Engineering and Artificial Intelligence, pages 275–287, Tunis, Tunisia. 98- R-300. Boudjlida, N. (1995). Knowledge in Interoperable and Evolutionary Systems. In Dreschler-Fischer, L. and Pribbenow, S., editors, KRDB’95, Workshop on “Reasoning about Structured Objets: Knowledge Representation Meets Databases”, pages 25–26, Bielefeld, Germany. (Position Paper). Boudjlida, N. (2002). A Mediator-Based Architecture for Capability Management. In Hamza, M., editor, Proceedings of the 6th International Conference on Software Engineering and Applications, SEA 2002, pages 45–50, MIT, Cambridge, MA. Calvanese, D., de Giacomo, D., Lenzerini, M., Nardi, D., and Rosati, R. (1998). Information Integration: Coceptual Modeling and Reasoning Support. In 6th International Conference on Cooperative Information Systems, CoopIS’98, pages 280–291. DL-org (2003). Description logics. http//dl.kr.org/. Dyck, T. (2002). Uddi 2.0 provides ties that bind. (http://www.eweek.com/). Haaslev, V. and Moller, R. (2003). Racer: Renamed abox and concept expression reasoner. http://www.fhwedel.de/mo/racer/index.html. Han, T.-D., Purao, S., and Storey, V. (1999). A Methodology for Building a Repository of Object-Oriented Design Fragments. In 18th International Conference on Conceptual Modelling, ER’99, pages 203–217, Paris. Spriger Verlag. LNCS 1728. Horrocks, I. (2002a). DAML+OIL: a description logic for the semantic web. IEEE Data Engineering Bulletin, 25(1):4–9. Horrocks, I. (2002b). Description Logic: Axioms and Rules. Talk given at Dagstuhl ”Rule Markup Technique” Workshop. http://www.cs.man.ac.uk/ horrocks/Slides/. Napoli, A. (1997). Une introduction aux logiques de description. Technical Report RR No 3314, INRIA- LORIA, Nancy. Nonaka, I. and Takeuchi, H. (1995). The Knowledge Creating Company; How Japanese Companies Create the Dynamics of Innovation. Oxford University Press. OMG (1992). The Object Model Architecture Guide. Technical Report 91.11.1, Revision 2.0, Object Management Group. Schmidt-Schauss, M. and Smolka, G. (1991). Attribute Concepts Description with Complements. Artificial Intelligence Journal, 48(1):1–26. uddi.org (2000). UDDI: Universal Description, Discovery and Integration. Technical White Paper. (http://uddi.org). W3C (2003a). Semantic Web. http://www.w3.org/2001/sw. W3C (2003b). Web Ontology. http://www.w3.org/2001/sw/WebOnt. W3C (2003c). Web Services. http://www.w3.org/2002/ws.
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vi TABLE OF CONTENTS PART 1 - DATAB
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viii TABLE OF CONTENTS A WIRELESS A
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CONFERENCE COMMITTEE Honorary Presi
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Hung, P. (AUSTRALIA) Jahankhani, H.
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Invited Papers
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2 ENTERPRISE INFORMATION SYSTEMS VI
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10 ENTERPRISE INFORMATION SYSTEMS V
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EVOLUTIONARY PROJECT MANAGEMENT: MU
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MANAGING COMPLEXITY OF ENTERPRISE I
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ASSESSING EFFORT PREDICTION MODELS
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ORGANIZATIONAL AND TECHNOLOGICAL CR
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ASAP Processes W Project Kickoff A
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since it means changing the relevan
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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Hit Rate (%) ACME-DB: AN ADAPTIVE C
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5.3.6 Effect on all Policies by Int
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ACKNOWLEDGEMENTS We would like to t
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This paper describes the data sampl
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The major limit A of the operation
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RELATIONAL SAMPLING FOR DATA QUALIT
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TOWARDS DESIGN RATIONALES OF SOFTWA
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((Brown et al., 1998), pp. 159-190,
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sive data filtering, presentation (
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• implementation changes in servi
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MEMORY MANAGEMENT FOR LARGE SCALE D
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Data Rate [bytes/sec] 1600000 14000
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E.g., DV Camcorder Camera Packets (
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Procedure CheckOptimal (n rs 1 ...n
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MEMORY MANAGEMENT FOR LARGE SCALE D
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PART 2 Artificial Intelligence and
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110 ENTERPRISE INFORMATION SYSTEMS
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DYNAMIC MULTI-AGENT BASED VARIETY F
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AN EXPERIENCE IN MANAGEMENT OF IMPR
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ANALYSIS AND RE-ENGINEERING OF WEB
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Module p0 Customer Itinerary Send I
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departments: the marketing dept. se
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• An acyclic module M is usable,
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BALANCING STAKEHOLDER’S PREFERENC
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The scenarios will provide an abstr
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BALANCING STAKEHOLDER'S PREFERENCES
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BALANCING STAKEHOLDER'S PREFERENCES
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A POLYMORPHIC CONTEXT FRAME TO SUPP
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A POLYMORPHIC CONTE XT FRAME TO SUP
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FEATURE MATCHING IN MODEL-BASED SOF
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combination of solution domains - a
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• features for all the concepts:
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Existence In both steps it is possi
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matching strategies are maximal, mi
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TOWARDS A META MODEL FOR DESCRIBING
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elementary message (ae-message) can
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problems on a pragmatic level, whic
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TOWARDS A META MODEL FOR DESCRIBING
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INTRUSION DETECTION SYSTEMS USING A
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PERSONALISED RESOURCE DISCOVERY SEA
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profile, the user defines his curre
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Abdelkafi, N. .....................
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