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 Wilcoxon signed rank test are proposed and the comparisons over six recently proposed methods are reported.@en

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 of aggregating the preferences of multiple DMs is to use the average operator, e.g., arithmetic or geometric mean. However, averages are sensitive to outliers and provide restricted information regarding the overall preferences of all DMs. In this paper, a Bayesian BWM is introduced to find the aggregated final weights of criteria for a group of DMs at once. To this end, the BWM framework is meaningfully viewed from a probabilistic angle, and a Bayesian hierarchical model is tailored to compute the weights in the presence of a group of DMs. We further introduce a new ranking scheme for decision criteria, called credal ranking, where a confidence level is assigned to measure the extent to which a group of DMs prefers one criterion over one another. A weighted directed graph visualizes the credal ranking based on which the interrelation of criteria and confidences are merely understood. The numerical example validates the results obtained by the Bayesian BWM while it yields much more information in comparison to that of the original BWM.@en

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 alternatives' scores with respect to the criteria and the importance weights of the criteria. Building on the notion of compositional data analysis, the developed RPM regards performance scores and criteria weights as compositions, which solves the issues around the WSM and WPM. Using several examples, we show that the WSM and WPM could lead to erroneous conclusions, whereas the RPM could lead to fully reliable conclusions. Since many MCDM methods rely on some aggregation approaches, the proposed method is a significant contribution to the field and puts forward the correct way to analyze decision problems while respecting the nature and constraints of the input data.@en

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 recognize the strength and shortcomings of their systems, and aid application developers to select a proper alignment system. This article presents a new evaluation and comparison methodology based on multiple performance metrics that accommodates experts’ preferences via a multi-criteria decision-making (MCDM) method, i.e., Bayesian best–worst method (BWM). First, the importance of a performance metric for a specific task/application is determined according to experts’ preferences. The alignment systems are then evaluated based on proposed expert-based collective performance (ECP) that takes into account multiple metrics as well as their calibrated importance. For comparison, the alignment systems are ranked based on a probabilistic scheme, where it includes the extent to which one alignment system is preferred over another. The proposed methodology is applied to six tracks from ontology alignment evaluation initiative (OAEI), where the importance of performance metrics are calibrated by designing a survey and eliciting the preferences of ontology alignment experts. Accordingly, the participating alignment systems in the OAEI 2018 are evaluated and ranked. While the proposed methodology is applied to six OAEI tracks to demonstrate its applicability, it can also be applied to any benchmark or application of ontology alignment.@en

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 objective, we first look into the literature of ontology alignment using a quantitative approach to get a thorough understanding of the available literature and its progress. We particularly identify several research gaps that are studied in the subsequent chapters that serve the objective of this dissertation. An important lesson learned from the literature analysis is that there are two segregated communities that form ontology alignment but work independently and with minimal interactions with each other. Based on the identified research gaps, we develop a new system, SANOM (simulated annealing-based ontology matching), that addresses the non-deterministic polynomialtime( NP) ontology alignment problembased on thewell-knownevolutionary algorithm, simulated annealing. SANOMis equippedwith an extended Soft TF-IDF (termfrequencyinverse document frequency) string similarity metric that can also detect linguistic similarity among the names of entities in two ontologies in question. Structural similarity metrics are also taken into account that increase the alignment performance for more complex ontologies. Simulated annealing with a warm initialization is used as the matching strategy to find an optimal solution to the ontology alignment problem. The experiments show that SANOM has a very competitive performance with the best systems that participated in the ontology alignment evaluation initiative (OAEI) and is particularly faster than other evolutionary algorithm-based alignment systems. To come to a better understanding of which alignment system is preferred, we develop several methods for evaluation and comparison of alignment systems using different statistical techniques and multi-criteria decision-making methods (MCDM). We first study the frequentist approach for comparing alignment systems. More in detail, we compare different statistical tests for comparing alignment systems over single or multiple benchmarks and propose a proper test based on the number of benchmarks and alignment systems. While these techniques are more reliable than those being currently in use, it suffers from the drawbacks of making decisions based on p-values, which can be addressed by Bayesian statistics. @en

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 approach is proposed based on the half-quadratic (HQ) theory. The proposed approach determines an optimal weight for each of the MCDM ranking methods, which are used to compute the aggregated final ranking. The weight of each ranking method is obtained via a minimizer function that is inspired by the HQ theory, which automatically fulfills the basic constraints of weights in MCDM. The proposed framework also provides a consensus index and a trust level for the aggregated ranking. To illustrate the proposed approach, the evaluation and comparison of ontology alignment systems are modeled as an MCDM problem and the proposed framework is applied to the ontology alignment evaluation initiative (OAEI) 2018, for which the ranking of participating systems is of the utmost importance.@en

The online spread of rumours in disasters can create panic and anxiety and disrupt crisis operations. Hence, it is crucial to take measure against such a distressing phenomenon since it can turn into a crisis by itself. In this work, the automatic rumour detection in natural disasters is addressed from an imbalanced learning perspective due to the rumour dearth versus non-rumour abundance in social networks. We first provide two datasets by collecting and annotating tweets regarding the Hurricane Florence and Kerala flood. We then capture the properties of rumours and non-rumours in those disasters using 83 theory-based and early-available features, 47 of which are proposed for the first time. The proposed features show a high discrimination power that help us distinguish rumours from non-rumours more reliably. Next, We build the rumour identification models using imbalanced learning to address the scarcity of rumours compared to non-rumour. Additionally, to replicate the rumour detection in the real-world situation, we practice cross-incident learning by training the classifier with the samples of one incident and test it with the other one. In the end we measure the impact of imbalanced learning using Bayesian Wilcoxon Signed-rank test and observe a significant improvement in the classifiers performance.@en

The online spread of rumours in disasters can create panic and anxiety and disrupt crisis operations. Hence, it is crucial to take measure against such a distressing phenomenon since it can turn into a crisis by itself. In this work, the automatic rumour detection in natural disasters is addressed from an imbalanced learning perspective due to the rumour dearth versus non-rumour abundance in social networks. We first provide two datasets by collecting and annotating tweets regarding the Hurricane Florence and Kerala flood. We then capture the properties of rumours and non-rumours in those disasters using 83 theory-based and early-available features, 47 of which are proposed for the first time. The proposed features show a high discrimination power that help us distinguish rumours from non-rumours more reliably. Next, We build the rumour identification models using imbalanced learning to address the scarcity of rumours compared to non-rumour. Additionally, to replicate the rumour detection in the real-world situation, we practice cross-incident learning by training the classifier with the samples of one incident and test it with the other one. In the end we measure the impact of imbalanced learning using Bayesian Wilcoxon Signed-rank test and observe a significant improvement in the classifiers performance.@en

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 evaluation of systems is partly transitioned to the HOBBIT platform. This paper contains the description of our alignment system, simulated annealing-based ontology matching (SANOM), and its adaption into the HOBBIT platform. The outcomes of SANOM on the HOBBIT for several OAEI tracks are reported, and the results are compared with other competing systems in the corresponding tracks.@en

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 dealing with large datasets due to its constrained optimization problem. To deal with this challenge, the variant SVM (VSVM) has been utilized which has the fraction ({1}/{2})b{2} in its primal objective function, where b is the bias of the desired hyperplane. The VSVM has been solved with different optimization techniques in more time- and memory-efficient fashion. However, there is no guarantee that its optimal solution is the same as the standard SVM. In this paper, we introduce the generalized VSVM (GVSVM) which has the fraction ({1}/{2t})b{2} in its primal objective function, for a fixed positive scalar t. Further, we present the thorough theoretical insights that indicate the optimal solution of the GVSVM tends to the optimal solution of the standard SVM as t rightarrow infty . One vital corollary is to derive a closed-form formula to obtain the bias term in the standard SVM. Such a formula obviates the need of approximating it, which is the modus operandi to date. An efficient neural network is then proposed to solve the GVSVM dual problem, which is asymptotically stable in the sense of Lyapunov and converges globally exponentially to the exact solution of the GVSVM. The proposed neural network has less complexity in architecture and needs fewer computations in each iteration in comparison to the existing neural solutions. Experiments confirm the efficacy of the proposed recurrent neural network and the proximity of the GVSVM and the standard SVM solutions with more significant values of t. @en

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 juxtaposition of computed scores, and specify that one system is superior to one another provided that its score is higher. Nonetheless, the comparison based solely on two figures would not quantify the significance of difference and would not determine the extent to which one system is better. The problem compounds for comparison over multiple benchmarks since averages and micro-averages of performance scores are considered. In this paper, the evaluation of alignment systems is translated into a statistical inference problem by introducing the notion of risk for alignment systems. The risk with respect to a performance score is shown to follow a binomial distribution and is equivalent to the complement of the score, e.g., precision risk =-1 precision. It is also demonstrated that the maximum likelihood estimation (MLE) is precisely equivalent to the conventional evaluation by using ratios. Instead of using the MLE, the Bayesian model is developed to estimate the risk with respect to a score (or equivalently, the score itself) as a probability distribution from the performance of the systems over single or multiple benchmarks. As a result, the evaluation outcome is a distribution instead of a figure, which provides a broader view of the overall system performance. A Bayesian test is also devised to compare various systems based on their estimated risks, which can compute the confidence that one system is superior to one another. We report the result of applying the proposed methodology to multiple tracks from the ontology alignment evaluation initiative (OAEI).@en

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 with the state-of-the-art systems. In particular, SANOM has the highest recall rate among the participated systems in the conference track, and is competitive with AML, the best performing system, in terms of F-measure. SANOM is also competitive with LogMap on the anatomy track, which is the best performing system in this track with no usage of particular biomedical background knowledge. SANOM has been adapted to the HOBBIT platfrom and is now available for the registered users. abstract environment.@en

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 ground truth that is often unavailable, or they utilize the population-based EAs in a way that they require massive computation and memory. This article presents a new ontology alignment system, called SANOM, which uses the well-known simulated annealing as the principal technique to find the mappings between two given ontologies while no ground truth is available. In contrast to population-based EAs, the simulated annealing need not generate populations, which makes it significantly swift and memory-efficient for the ontology alignment problem. This article models the ontology alignment problem as optimizing the fitness of a state whose optimum is obtained by using the simulated annealing. A complex fitness function is developed that takes advantage of various similarity metrics including string, linguistic, and structural similarities. A randomized warm initialization is specially tailored for the simulated annealing to expedite its convergence. The experiments illustrate that SANOM is competitive with the state-of-the-art and is significantly superior to other EA-based systems.@en

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 theoretically and empirically reviewed. For comparison of two systems, the Wilcoxon signed-rank and McNemar's mid-p and asymptotic tests are recommended due to their robustness and statistical safety in different circumstances. The Friedman and Quade tests with their corresponding post-hoc procedures are studied for comparison of multiple systems, and their [dis]advantages are discussed. The statistical methods are then applied to benchmark and multifarm tracks from the ontology matching evaluation initiative (OAEI) 2015 and their results are reported and visualized by critical difference diagrams.@en

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, we first convert it into a smooth quadratic minimization by splitting the desired variable into its positive and negative parts. Accordingly, a novel neural network is proposed to solve the resulting problem, which is guaranteed to converge to the solution of the problem. Furthermore, the rate of the convergence is dependent on a scaling parameter, not to the size of datasets. The proposed neural network is further adjusted to encompass the total variation regularization. Extensive experiments on the l1 and total variation regularized problems illustrate the reasonable performance of the proposed neural network.@en

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 to deal with the nondifferentiable penalty. Thus, the resulting algorithms are both time- and memory-inefficient. This paper proposes a compact neural network to solve the FLSA. The neural network has a one-layer structure with the number of neurons proportionate to the dimension of the given signal, thanks to the utilization of consecutive projections. The proposed neural network is stable in the Lyapunov sense and is guaranteed to converge globally to the optimal solution of the FLSA. Experiments on several applications from signal processing and biomedical engineering confirm the reasonable performance of the proposed neural network. @en

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 with the state-of-the-art systems. In particular, SANOM has the highest recall rate among the participated systems in the conference track, and is competitive with AML, the best performing system, in terms of F-measure. SANOM is also competitive with LogMap on the anatomy track, which is the best performing system in this track with no usage of particular biomedical background knowledge. SANOM has been adapted to the HOBBIT platfrom and is now available for the registered users. abstract environment.@en

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 ontologies. The current trend in the alignment evaluation is to put forward a new score (e.g., precision, weighted precision, semantic precision, etc.) and to compare various alignments by juxtaposing the obtained scores. However, it is substantially provocative to select one measure among others for comparison. On top of that, claiming if one system has a better performance than one another cannot be substantiated solely by comparing two scalars. In this article, we propose the statistical procedures that enable us to theoretically favor one system over one another. The McNemar's test is the statistical means by which the comparison of two ontology alignment systems over one matching task is drawn. The test applies to a 2 × 2 contingency table, which can be constructed in two different ways based on the alignments, each of which has their own merits/pitfalls. The ways of the contingency table construction and various apposite statistics from the McNemar's test are elaborated in minute detail. In the case of having more than two alignment systems for comparison, the family wise error rate is expected to happen. Thus, the ways of preventing such an error are also discussed. A directed graph visualizes the outcome of the McNemar's test in the presence of multiple alignment systems. From this graph, it is readily understood if one system is better than one another or if their differences are imperceptible. The proposed statistical methodologies are applied to the systems participated in the OAEI 2016 anatomy track, and also compares several well-known similarity metrics for the same matching problem.@en

In most machine learning problems, the labeling of the training data is an expensive or even impossible task. Crowdsourcing-based learning uses uncertain labels from many non-expert annotators instead of one reference label. Crowdsourcing based linear regression is an efficient way for function estimation when many labels are available for each instance. However, methods in literature have a poor performance against large noise and outliers in labels. To tackle this problem, we proposed a novel robust crowdsourcing-based linear regression derived from information theoretic learning. The proposed problem is not convex, but it can be efficiently solved by half quadratic programming. The proposed model has a close relation with weighted crowdsourcing-based linear regression, in which labels of each annotator weight adaptively and iteratively. The Performance of the proposed method evaluated on several artificial data sets in different circumstances. Experimental Results demonstrate the efficacy and robustness of the proposed method.@en

In most machine learning problems, the labeling of the training data is an expensive or even impossible task. Crowdsourcing-based learning uses uncertain labels from many non-expert annotators instead of one reference label. Crowdsourcing based linear regression is an efficient way for function estimation when many labels are available for each instance. However, methods in literature have a poor performance against large noise and outliers in labels. To tackle this problem, we proposed a novel robust crowdsourcing-based linear regression derived from information theoretic learning. The proposed problem is not convex, but it can be efficiently solved by half quadratic programming. The proposed model has a close relation with weighted crowdsourcing-based linear regression, in which labels of each annotator weight adaptively and iteratively. The Performance of the proposed method evaluated on several artificial data sets in different circumstances. Experimental Results demonstrate the efficacy and robustness of the proposed method.@en