Unveiling and Unraveling Aggregation and Dispersion Fallacies in Group MCDM

Priorities in multi-criteria decision-making (MCDM) convey the relevance preference of one criterion over another, which is usually reflected by imposing the non-negativity and unit-sum constraints. The processing of such priorities is different than other unconstrained data, but this point is often neglected by researchers, which results in...

Ratio product model: A rank-preserving normalization-agnostic multi-criteria decision-making method

This paper presents a new multi-criteria decision-making (MCDM) method, namely the ratio product model (RPM). We first overview two popular aggregating models: the weighted sum model (WSM) and the weighted product model (WPM). Then, we argue that the two models suffer from some fundamental issues mainly due to ignoring the ratio nature of the...

Generalized Variant Support Vector Machine

With the advancement in information technology, datasets with an enormous amount of data are available. The classification task on these datasets is more time- and memory-consuming as the number of data increases. The support vector machine (SVM), which is arguably the most popular classification technique, has disappointing performance in...

A Compact Neural Network for Fused Lasso Signal Approximator

The fused lasso signal approximator (FLSA) is a vital optimization problem with extensive applications in signal processing and biomedical engineering. However, the optimization problem is difficult to solve since it is both nonsmooth and nonseparable. The existing numerical solutions implicate the use of several auxiliary variables in order...

Ontology alignment: Simulated annealing-based system, statistical evaluation, and application to logistics interoperability

The primary motivation of this dissertation is to investigate how to enable interoperability in the logistics domain by the aid of ontology alignment. More in detail, the primary research objective of this dissertation is To address interoperability between heterogeneous IT systems in logistics by using ontology alignment. To accomplish the...

Ensemble ranking: Aggregation of rankings produced by different multi-criteria decision-making methods

One of the essential problems in multi-criteria decision-making (MCDM) is ranking a set of alternatives based on a set of criteria. In this regard, there exist several MCDM methods which rank the alternatives in different ways. As such, it would be worthwhile to try and arrive at a consensus on this important subject. In this paper, a new...

SANOM-HOBBIT: Simulated annealing-based ontology matching on HOBBIT platform

Ontology alignment is an important and inescapable problem for the interconnections of two ontologies stating the same concepts. Ontology alignment evaluation initiative (OAEI) has been taken place for more than a decade to monitor and help the progress of the field and to compare systematically existing alignment systems. As of 2018, the...

Evaluating and comparing ontology alignment systems: An MCDM approach

Ontology alignment is vital in Semantic Web technologies with numerous applications in diverse disciplines. Due to diversity and abundance of ontology alignment systems, a proper evaluation can portray the evolution of ontology alignment and depicts the efficiency of a system for a particular domain. Evaluation can help system designers...

Bayesian evaluation and comparison of ontology alignment systems

Ontology alignment systems are evaluated by various performance scores, which are usually computed by a ratio related directly to the frequency of the true positives. However, such ratios provide little information regarding the uncertainty of the overall performance of the corresponding systems. The comparison is also drawn merely by the...

Simulated Annealing-based Ontology Matching

Ontology alignment is a fundamental task to reconcile the heterogeneity among various information systems using distinct information sources. The evolutionary algorithms (EAs) have been already considered as the primary strategy to develop an ontology alignment system. However, such systems have two significant drawbacks: they either need a...

Bayesian best-worst method: A probabilistic group decision making model

The best-worst method (BWM) is a multi-criteria decision-making method which finds the optimal weights of a set of criteria based on the preferences of only one decision-maker (DM) (or evaluator). However, it cannot amalgamate the preferences of multiple decision-makers/evaluators in the so-called group decision-making problem. A typical way...

SANOM results for OAEI 2019

Simulated annealing-based ontology matching (SANOM) participates for the second time at the ontology alignment evaluation initiative (OAEI) 2019. This paper contains the configuration of SANOM and its results on the anatomy and conference tracks. In comparison to the OAEI 2017, SANOM has improved significantly, and its results are competitive...

Comparison of ontology alignment systems across single matching task via the McNemar's test

Ontology alignment is widely used to find the correspondences between different ontologies in diverse fields. After discovering the alignments, several performance scores are available to evaluate them. The scores typically require the identified alignment and a reference containing the underlying actual correspondences of the given...

A Comparative Study of Ontology Matching Systems via Inferential Statistics

Comparing ontology matching systems are typically performed by comparing their average performances over multiple datasets. However, this paper examines the alignment systems using statistical inference since averaging is statistically unsafe and inappropriate. The statistical tests for comparison of two or multiple alignment systems are...

A novel one-layer recurrent neural network for the l₁-regularized least square problem

The l₁-regularized least square problem has been considered in diverse fields. However, finding its solution is exacting as its objective function is not differentiable. In this paper, we propose a new one-layer neural network to find the optimal solution of the l₁-regularized least squares problem. To solve the problem,...

SANOM results for OAEI 2018

Simulated annealing-based ontology matching (SANOM) participates for the second time at the ontology alignment evaluation initiative (OAEI) 2018. This paper contains the configuration of SANOM and its results on the anatomy and conference tracks. In comparison to the OAEI 2017, SANOM has improved significantly, and its results are competitive...

Results of the ontology alignment evaluation initiative 2017

Ontology matching consists of finding correspondences between semantically related entities of different ontologies. The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity (from simple...

Ontology matching evaluation: A statistical perspective

This paper proposes statistical approaches to test if the difference between two ontology matchers is real. Specifically, the performances of the matchers over multiple data sets are obtained and based on their performances, the conclusion can be drawn whether one method is better than one another or not. To do so, the paired t-test and...