A Large Scale Dataset for the Evaluation of Ontology Matching Systems

Recently, the number of ontology matching techniques and systems has increased significantly. This makes the issue of their evaluation and comparison more severe. One of the challenges of the ontology matching evaluation is in building large scale evaluation datasets. In fact, the number of possible correspondences between two ontologies grows quadratically with respect to the numbers of entities in these ontologies. This often makes the manual construction of the evaluation datasets demanding to the point of being infeasible for large scale matching tasks. In this paper we present an ontology matching evaluation dataset composed of thousands of matching tasks, called TaxME2. It was built semi-automatically out of the Google, Yahoo and Looksmart web directories. We evaluated TaxME2 by exploiting the results of almost two dozen of state of the art ontology matching systems. The experiments indicate that the dataset possesses the desired key properties, namely it is error-free, incremental, discriminative, monotonic, and hard for the state of the art ontology matching systems. The paper has been accepted for publication in "The Knowledge Engineering Review", Cambridge Universty Press (ISSN: 0269-8889, EISSN: 1469-8005)

Algoritma matching bobot maskimum dalam graph bipartit komplit berboto

ABSTRAK Suatu matching dalam graph G adalah subgraph 1-regular pada G yang disebabkan oleh kumpulan dart pasangan garis yang tidak adjacent. Suatu matching merupakan matching maksimum bila matching tersebut mempunyai harga pokok maksimum. Matching dalam graph bipartit merupakan matching maksimum apabila tidak adanya path perluasart yang berkenaan dengan matching tersebut. Matching yang mempunyai bobot maksimum disebut matching bobot maksimum. Matching bobot maksimum dalam graph bipartit komplit berbobot diperoleh dengan mencari matching maksimum dalam subgraph pada graph bipartit komplit berbobot, kemudian dibangun sampai didapatkan matching perfek atau setiap titik dalam V merupakan titik matched. A matching in a graph G is a 1-regular subgraph of G, that is, a subgraph induced _by a collection of pairwise nonadjacent edges. A matching is called maximum matching if the matching have maximum cardinality. A matching in a bipartite graphs is a maximum matching if there exists no augmenting path. A matching in which the sum of the weights of maximum its edges is called maximum weight matching. A maximum weight matching in weighted complete bipartite graphs is got to find maximum matching in subgraph to weighted complete bipartite graphs, further its construct to arrived is got perfec matching or each vertex in V is matched vertex

The matching law

This article introduces the quantitative analysis of choice behavior by describing a number of equations developed over the years to describe the relation between the allocation of behavior under concurrent schedules of reinforcement and the consequences received for alternative responses. Direct proportionality between rate of responding and rate of reinforcement was observed in early studies, suggesting that behavioral output matched environmental input in a mathematical sense. This relation is termed "strict matching," and the equation that describes it is referred to as "the matching law." Later data showed systematic departures from strict matching, and a generalized version of the matching equation is now used to describe such data. This equation, referred to as "the generalized matching equation," also describes data that follow strict matching. It has become convention to refer to either of these equations as "the matching law." Empirical support for the matching law is briefly summarized, as is the applied and practical significance of matching analyses

Energy Disaggregation Using Elastic Matching Algorithms

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)In this article an energy disaggregation architecture using elastic matching algorithms is presented. The architecture uses a database of reference energy consumption signatures and compares them with incoming energy consumption frames using template matching. In contrast to machine learning-based approaches which require significant amount of data to train a model, elastic matching-based approaches do not have a model training process but perform recognition using template matching. Five different elastic matching algorithms were evaluated across different datasets and the experimental results showed that the minimum variance matching algorithm outperforms all other evaluated matching algorithms. The best performing minimum variance matching algorithm improved the energy disaggregation accuracy by 2.7% when compared to the baseline dynamic time warping algorithm.Peer reviewedFinal Published versio