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By Manfred Krifka, Austinlsaarbriicken

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At each level of this threshold, a H-mean F-measure is computed over all test cases in the dataset. 3 Results and Discussion Fig. 2 shows the results of this comparison. html Opening the Black Box of Ontology Matching 21 filter increases. When the filter threshold increases, it appears that only highly similar or identical labels are be passed. Therefore, as Fig. 97. The same trend is observed for HADAPT and LC because they are linear combinations of local methods. Fig. 2. Mapping Selection for the Terminological Matcher Module The experiment shows that the two global methods, ML and IR outperform the other techniques within the terminological matcher module.

Another way is to use external knowledge sources. For instance, there is much work on finding relationships between terms in the ontology learning area [1]. Regarding the detection of is-a relations, one paradigm is based on linguistics using lexico-syntactic patterns. The pioneering research conducted in this line is in [9], which defines a set of patterns indicating is-a relationships between words in the text. Another paradigm is based on machine learning and statistical methods. Further, guidelines based on logical patterns can be used [3].

As input for Experiment 1 and 2 we used the two ontologies from the Anatomy track of OAEI 2011 - AMA contains 2,737 concepts and 1,807 asserted is-a relations, and NCI-A contains 3,298 concepts and 3,761 asserted isa relations. The input for the last experiment contained the reference alignment (1516 equivalence mappings between AMA and NCI-A) together with the two ontologies. The reference alignment was used indirectly as external knowledge during the validation phase in the first two experiments.

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A Compositional Semantics for Multiple Focus by Manfred Krifka, Austinlsaarbriicken

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