A Hybrid Modified Semantic Matching Algorithm Based on Instances Detection With Case Study on Renewable Energy

Abstract

This Matching input keywords with historical or information domain is an important point in modern computations in order to find the best match information domain for specific input queries. Matching algorithms represents hot area of researches in computer science and artificial intelligence. In the area of text matching, it is more reliable to study semantics of the pattern and query in terms of semantic matching. This paper improves the semantic matching results between input queries and information ontology domain. The contributed algorithm is a hybrid technique that is based on matching extracted instances from booth, the queries and in information domain. The instances extraction algorithm that is presented in this paper are contributed which is based on mathematical and statistical analysis of objects with respect to each other and also with respect to marked objects. The instances that are instances from the queries and information domain are subjected to semantic matching to find the best match, match percentage, and to improve the decision making process. An application case was studied in this paper which is related to renewable energy, where the input queries represents the customer requirements input and the knowledge domain is renewable energy vendors profiles. The comparison was made with most known recent matching researches.

Energy Efficient Localization Technique Using Multilateration for Reduction of Spatially and Temporally Correlated Data in RFID System

“…As it stores all the data, it leads to false positive rates and false negative rates [11]. To overcome these limitations, various approaches are based on bloom filters, namely, Time Distance Bloom Filter (TDBF) [12], Time and Space Bloom Filter (TSBF) [13], Approximate Synthesis Time Interval Bloom Filter (TIBF) [14]…”