A New Approach for Combining the Similarity Values in Ontology Alignment

The general representation and reasoning framework that we propose includes: 1) a declarative language to specify networks of ontologies and alignments, with inde- pendent control

(Note that there is no sufficient adjustment set (of measured variables) according to this DAG as the paths PKD vs. other nephropathies←Major concurrent illnesses→Death, PKD vs.

In order to test the efficiency of the CMA Es method for Learning parameters in DBNs, we look at the following trend following algorithm based on the following DBN network where

In order to correctly assess the nutritional quality of a meal or a manufactured food product in a given country, the first step is to assess the nutritional

Deep Learning models proposed so far [11] have used external informa- tion to enrich entities, such as synonyms from the ontology, definitions from Wikipedia and context from

We presented features capturing a few separate sources of information, producing alignments on the order of those given by unsymmetrized IBM Model 4 (using labeled training data

Semantic similarity is used to include similarity measures that use an ontology’s structure or external knowledge sources to determine similarity between

The proposed method, converts every document to a number of parts with specific length using the graph approach idea and then if necessary, it converts each part to a