By Fayçal Hamdi, Chantal Reynaud, Brigitte Safar (auth.), Philipp Cimiano, H. Sofia Pinto (eds.)
Knowledge administration and information Engineering is an interesting ?eld of re- 1 seek nowadays. before everything of EKAW , the modeling and acquisition of data was once the privilege of – or particularly a burden for – a couple of wisdom engineers conversant in wisdom engineering paradigms and data rep- sentationformalisms.While the aimhasalwaysbeentomodelknowledgedecl- atively and make allowance for reusability, the information versions produced in those early days have been usually utilized in unmarried and extremely speci?c purposes and barely - replaced. additionally, those versions have been quite often particularly advanced, and so they should be understood purely by means of a couple of professional wisdom engineers. this case has replaced notably within the previous couple of years as essentially indicated via the subsequent developments: – The production of (even formal) wisdom is now turning into an increasing number of collaborative. Collaborative ontology engineering instruments and social software program structures convey the capability to leverage the knowledge of the crowds (or no less than of “the many”) to steer to broader consensus and hence produce shared versions which qualify larger for reuse. – A development is also saw in the direction of constructing and publishing small yet 2 three four high-impactvocabularies(e.g.,FOAF ,DublinCore ,GoodRelations)rather than advanced and big wisdom models.
Read Online or Download Knowledge Engineering and Management by the Masses: 17th International Conference, EKAW 2010, Lisbon, Portugal, October 11-15, 2010. Proceedings PDF
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Extra info for Knowledge Engineering and Management by the Masses: 17th International Conference, EKAW 2010, Lisbon, Portugal, October 11-15, 2010. Proceedings
Fig. 4. Illustration of Pattern-2 10 F. Hamdi, C. Reynaud, and B. Safar Fig. 5. Example of the application of the Pattern-2 Context part of Pattern-2: ∃x∃y (isAStrictInclusion(x, y) ∧ inclusionInLabel(x, “and”, y) ∧ ∃z (isSubClassOf (y, z, OT ) ∧ ¬strictInclusionLabel(z, x))) Solution part of Pattern-2: Delete M apping(x, y, ) ∧ Add M apping(x, y, isM oreGnl) Pattern-3: Let the set SD(c, O) be composed of c and of all its sub-concepts in O. The measure MSD (c1 , O1 , c2 , O2 ) is deﬁned as the ratio between the number of equivalence relations veriﬁed in the mapping table between concepts in SD(c1 , O1 ) and in SD(c2 , O2 ) and the total number of concepts belonging to the union of these two sets.
The expert has validated its results. The new pattern addresses a new case identiﬁed by the expert during iteration 3. Note that the results are unchanged regardless of the order of applying these 2 patterns (the pattern previously deﬁned and the new one). The whole experiment in the topographic ﬁeld led to specify 6 reﬁnement patterns related to 4 alignment techniques of TaxoMap. 25 mappings have been modiﬁed. 23 satisfy the wishes of the expert. Two reﬁnements are incorrect. Pattern-Based Mapping Reﬁnement 13 Table 1.
The TaxoMap Framework has also been designed to allow the speciﬁcation of other treatments such as merging, restructuring and enriching ontologies based on alignment results. Future work will be devoted to the design and the implementation of the modules corresponding to these additional functionalities. It will be devoted also to the extension of the approach for reﬁning the mappings between ontologies that have a more richer axiomatisation. Acknowledgement This research was supported by the French National Research Agency (ANR), through the G´eOnto project ANR-O7-MDCO-005 on Creation, Comparison and Exploitation of Heterogeneous Geographic Ontologies .
Knowledge Engineering and Management by the Masses: 17th International Conference, EKAW 2010, Lisbon, Portugal, October 11-15, 2010. Proceedings by Fayçal Hamdi, Chantal Reynaud, Brigitte Safar (auth.), Philipp Cimiano, H. Sofia Pinto (eds.)