"... In PAGE 5: ... Let us assume that we need to establish semantic correspondences between two heterogeneous ontologies mod- eling the bibliography domain. Table2 shows the deflnition of the source ontology S, and the target ontology T . The corresponding mappings M are shown in Table 3 and visu- alized in Figure 1.... ..."

"... In PAGE 4: ... Applications that commit to this ontology may retain their internal data models4. As discussed before, In DOGMA framework, ontology is decomposed into ontology base, as a set of lexons, and into instances of their explicit ontological commitments that form a commitment layer; respectively, Table1 and Table 2 represent the ontology base and the commitment layer of the ontology drawn in fig 2. The representation of the rules in the commitment layer is not restricted to a particular ontology language or standard, but we adopt a notational convention to specify which rules system/standard is used, in the form of a prefix of the rule.... ..."

"... In PAGE 16: ...8 The tourism domain ontology consists of 293 concepts, while the finance domain ontology is bigger with a total of 1223 concepts9. Table2 summarizes some facts about the concept hierarchies of the ontologies, such as the total number of concepts, the total number of leave concepts, the average and maximal length of the paths from a leave to the root node as well as the average and maximal number of children of a concept (without considering leave concepts). As domain-specific text collection for the tourism domain we use texts acquired from the above mentioned web sites, i.... ..."

"... In PAGE 16: ...8 The tourism domain ontology consists of 293 concepts, while the nance domain ontology is bigger with a total of 1223 concepts9. Table2 summarizes some facts about the concept hierarchies of the ontologies, such as the total number of concepts, the total number of leave concepts, the average and maximal length of the paths from a leave to the root node as well as the average and maximal number of children of a concept (without considering leave concepts). As domain-speci c text collection for the tourism domain we use texts acquired from the above mentioned web sites, i.... ..."

"... In PAGE 2: ... We further distinguish and relate two or more ontologies with the following predicates: same_as, part_of, is_a, equal_to, disjoin_to, overlap_to, and relate_to. Table1 shows details about ontology relationships. Table 1.... ..."

"... In PAGE 12: ... In other words, these four documents do not have mutual categories with the rest of the documents dealing with the same events. The contents of our ontology is listed in Table5 . The data is based on mate- rial provided by Statistics Finland 3.... ..."

"... In PAGE 15: ...List of Tables Table1 : Ontology Vector.... In PAGE 51: ... These object sets give us the information most helpful for separating instances of the primary object set while more indirectly related object sets have more of a potential for ambiguity and conflicts or for being completely unrelated. The dimensions selected by this algorithm from our genealogy ontology appear in Table1 along with the averages that make up the OV and the vector itself. For each candidate record, another vector records the matches found in a portion of the document.... ..."