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In this work we present GOAL (Genetics for Ontology Align- ments) a new approach to compute the optimal ontology alignment func- tion for a given ontology input set. Although this problem could be solved by an exhaustive search when the number of similarity measures is low, our method,is expected to scale better for a high number,of measures. Our a...
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... it can be seen, we have found the optimal alignment function for the ma- jority of tests. In this way, we could cover matching cases, and therefore increase the chances of success. Some of test cases are solved in the first generation, this is because our application is not very difficult, maybe the problem is, but these specific instances are not. If we look at literature, we can distinguish between individual algorithms (i.e. FCA-MERGE [17] or S-Match [18]) applying only a single method of matching items i.e. linguistic or taxonomical matchers and combinations of the former ones, which intend to overcome their limitations by proposing hybrid and composite solutions. A hybrid approach (i.e.Cupid [19]) follows a black box paradigm, in which various individual matchers are melt together in a new algorithm [20], while the so-called composite matchers allow an increased user interaction (i.e. COMA++ [7], Falcon [21], CtxMatch [22], RiMOM [23]). In Fig. 3, we can see a comparison between some of the most popular tools for matching ontologies. The figure represents the arithmetic means of the values obtained for the standard benchmark for the precision and recall, obtaining the F-Measure and Fall-Out is trivial. The problem is that those kinds of proposals use weights defined by an expert for configuring the composite matchers, while using our approach involves to compute the weigths in an automatic way, so the process can be more flexible, at least, in real scenarios. To avoid the expert intervention, there are two research lines; one line for evaluating the results of an alignment tool and maybe feedback the process [24] [25] and another called ontology meta-matching [26] that tries to optimize automatically the parameters related to matching task. So, our approach could be considered a mechanism for meta-matching. Most outstanding examples for this paradigm are evaluated in the next sections: (i) Exhaustive Search solutions, (ii) Machine Learning solutions, and (iii) Genetic Algorithms solutions. Ontology meta-matching can be solved trivially by an exhaustive search when the number of similarity measures is low. The most popular approach in this sense is eTuner [27] that it is a system which, given a particular matching task, automatically tunes an ontology matching system (computing one-to-one alignments). For that purpose, it chooses the most effective basic matchers, and the best parameters to be used. However, exhaustive searches are very expensive, and unworkable when combining a great number of measures, from a computational point of view. Unfor- tunately, the paper from eTuner [27] has not used an standard benchmark to offer the results, so we cannot show a comparison. Based on Machine Learning meta-matching techniques can be divided into two subtypes: Relevance feedback [28] and Neural Networks ...
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... Linguistic-based ontology alignment algorithms are also known as evolutionary algorithms (EA). An early Genetic algorithm-based Ontology Alignment tool is GOAL [23], which generates optimal weight configurations using weighted average aggregation of several similarity measures. Alternative approaches have since been developed [24] [25] [26]. ...
Ontology alignment is a key component of semantic web interoperability, with a long history of applications in traditional data integration tasks that address the problem of semantic heterogeneity. Ontology alignment tools take two ontologiesas input and determine alignments as output i.e. a set of correspondences between semantically related units of these ontologies. These correspondences can then be used to merge ontologies, link data across knowledge domains, answer semantic queries, navigate through knowledge graphs, and many more. The process of determining alignments, called matching, therefore is a basic requirement for linking knowledge across scientific domains covered by one or more ontologies. A matching exists if entities from different ontologies are semantically equivalent. The goal of this study is to find all semantically equivalent entity pairs given a source ontology Os and a target ontology Ot, each consisting of a set of entities. We propose to use Inverse Document Frequency (IDF) and Jaccard distance to find candidate entities with high precision and less computational effort. Our goal to cross-link different research fields in the biomedical and clinical domains.
... Several systems in this category have been developed such as: COMA [10], ASMOV [11] and RIMON [12]. In this category, we can also mention works based on optimization techniques, as presented in the literature [13][14][15]. ...
The ontology matching is an active field of research, which is considered as a key solution to solve the semantic heterogeneity problem. Given two ontologies, the alignment process produces a set of matches each linking two entities. To address this issue, we have reformulated the alignment problem as a binary classification problem using different similarity measures between different ontologies entities as features. In our approach, we propose a model based on deep reinforcement learning, using deep Q-learning network. In this model, the classification action is carried out by the agent at each time step, and the environment evaluates the agent’s decisions and returns a reward to this latter. In order to improve the performance of our model, we have made a comparative study with state-of-the-art approaches tackling the ontology matching problem. The approach is evaluated on two datasets from the Ontology Alignment Evaluation Initiative campaign (OAEI). Our experiments demonstrate that our proposed models outperform some machine learning-based approaches and are comparable in performance to other existing systems that use different techniques.
... Доцільно спиратися на визначення онтології як формальної абстракції подання об'єктів аналізу (доменів), використовуючи конкретну інформацію, таку як типи об'єктів, властивості (характеристики) та відносини щодо певної області і за певною структурою [2]. Онтологічні моделі використовуються як форма репрезентації предметної області або певної частини [3] задля чіткого подання, уточнення та осмислення набору більшості репрезентативних типів об'єктів, які досліджуються у визначеній предметній області. ...
... SIA has emerged as an effective and efficient approach, and until now, many SIAs and their modified algorithms have been applied to determine the ontology alignment [18]. GAOM [19] and GOAL [20] are two representative ontology matching systems which utilize the genetic algorithm (GA) as an optimization method. GAOM constructs a maximized optimization model of entity matching to compute the alignment between two ontologies directly, while GOAL treats the ontology matching problem as a meta-matching problem, which optimizes the weight configuration of aggregating several similarity measures. ...
As an effective method of addressing ontology heterogeneity problem, ontology matching becomes increasingly important for knowledge sharing and inter-system communication. Ontology meta-matching, which aims at finding optimal ways of integrating different similarity measures, is an effective method of determining high-quality ontology alignment. However, the existing ontology meta-matching techniques suffer from the following defects: first, most of them are depending on the reference alignment that ought to be given by experts in advance, which is not available in the practical scenarios; second, they tend to get stuck in the local optima, which makes the alignment unsatisfactory. In order to solve the above problems, in this work, an optimization model for ontology meta-matching problem is constructed on the basis of a new proposed evaluation metric on the alignment’s quality. After that, a multi-strategy adaptive co-firefly algorithm (MACFA), which is able to trade off the algorithm’s exploitation and exploration, is proposed to overcome the premature convergence. The testing cases in Ontology Alignment Evaluation Initiative (OAEI) is utilized to verify the effectiveness of our approach. Experimental results show that the optimization model as well as MACFA improves the ontology alignment’s quality, and compared with OAEI’s participants, the proposed matching system achieves competitive results.
... Another category is the ontology meta-matching technology, which can be considered to automatically select or configure appropriate weights and filtering thresholds during optimization process [9] . The earliest metamatching method is GOAL [10] . Instead of directly optimizing the candidate alignment between two ontologies, this method uses a genetic algorithm to set the weights, and to find a suboptimal alignment. ...
Ontology alignment is an essential and complex task to integrate heterogeneous ontology. The meta-heuristic algorithm has proven to be an effective method for ontology alignment. However, it only applies the inherent advantages of meta-heuristics algorithm and rarely considers the execution efficiency, especially the multi-objective ontology alignment model. The performance of such multi-objective optimization models mostly depends on the well-distributed and the fast-converged set of solutions in real-world applications. In this paper, two multi-objective grasshopper optimization algorithms (MOGOA) are proposed to enhance ontology alignment. One is ε -dominance concept based GOA (EMO-GOA) and the other is fast Non-dominated Sorting based GOA (NS-MOGOA). The performance of the two methods to align the ontology is evaluated by using the benchmark dataset. The results demonstrate that the proposed EMO-GOA and NS-MOGOA improve the quality of ontology alignment and reduce the running time compared with other well-known metaheuristic and the state-of-the-art ontology alignment methods.
... How to combine and tune different similarity measures to improve the ontology alignment's quality is a challenging problem [9], and EA is a state-of-the-art methodology to face it [10]. Martinez et al. [11] first propose to improve ontology alignment through EA. ey are dedicated to finding a suitable weight set for aggregating three kinds of similarity measures in parallel. After that, Ginsca et al. [12] and Naya et al. [13] further optimize another parameter for the matching process, i.e., the threshold for determining the final alignment. ...
... (3) evaluate population 1 by f origin ; (4) evaluate population 2 by f help ; (5) gen � 0; (6) While gen < MaxGen do (7) parent 1 � Random_Selection(population 1 , N/2); (8) parent 2 � Random_Selection(population 2 , N/2); (9) offspring 1 � Generate_Offspring(population 1 , N/2); (10) offspring 2 � Generate_Offspring(population 2 , N/2); (11) population 1 � population 1È offspring 1È offspring 2 ; (12) population 2 � population 2È offspring 2È offspring 1 ; (13) evaluate population 1 by f origin ; (14) evaluate population 2 by f help ; (15) Non − dorminated_Sorting(population 1 ); Security and Communication Networks with the single-point crossover operator and flip-bit mutation [37]. Afterwards, population 1 and population 2 are both combined with two offspring populations, offspring 1 and offspring 2 , which are, respectively, evaluated by f origin and f help . ...
... e algorithm's configurations are determined through the empirical experiments, and their robustness against different heterogeneous matching tasks is verified through the experimental results. ree similarity measures are the classical ones that belong to three categories of similarity measures in ontology matching domains, which have been proved to have mutual benefits in enhancing the results' confidence [11]. ...
Since Internet of Everything (IoE) makes all the connections that come online more relevant and valuable, they are subject to numerous security and privacy concerns. Cybersecurity ontology is a shared knowledge model for tackling the security information heterogeneity issue on IoE, which has been widely used in the IoE domain. However, the existing CSOs are developed and maintained independently, yielding the CSO heterogeneity problem. To address this issue, we need to use the similarity measure (SM) to calculate two entities’ similarity value in two CSOs and, on this basis, determine the entity correspondences, i.e., CSO alignment. Usually, it is necessary to integrate various SMs to enhance the result’s correctness, but how to combine and tune these SMs to improve the alignment’s quality is still a challenge. To face this challenge, this work first models CSO matching problem as a Constrained Multiobjective Optimization Problem (CMOOP) and then proposes a Coevolutionary Multiobjective Evolutionary Algorithm (CE-MOEA) to effectively address it. In particular, CE-MOEA uses the multiobjective evolutionary paradigm to avoid the solutions’ bias improvement and introduces the coevolutionary mechanism to trade off Pareto Front’s (PF’s) diversity and convergence. The experiment uses Ontology Alignment Evaluation Initiative’s (OAEI’s) bibliographic track and conference track and five real CSO matching tasks to test CE-MOEA’s performance. Comparisons between OAEI’s participants and EA- and MOEA-based matching techniques show that CE-MOEA is able to effectively address various heterogeneous ontology matching problems and determine high-quality CSO alignments.
... From this seminal work, the study turned to the proposal of machine learning methods to aggregate the fundamental matchers, with GAOM [6] and GOAL [7,8] being the first studies being able to construct the ensemble using genetic algorithms. The fundamental notion was that it might optimize the precision, recall, or combination of the two, for example a harmonic mean called f-measure. ...
The problem of ontology matching consists of finding the semantic correspondences between two ontologies that, although belonging to the same domain, have been developed separately. Ontology matching methods are of great importance today since they allow us to find the pivot points from which an automatic data integration process can be established. Unlike the most recent developments based on deep learning, this study presents our research efforts on the development of novel methods for ontology matching that are accurate and interpretable at the same time. For this purpose, we rely on a symbolic regression model (implemented via genetic programming) that has been specifically trained to find the mathematical expression that can solve the ground truth provided by experts accurately. Moreover, our approach offers the possibility of being understood by a human operator and helping the processor to consume as little energy as possible. The experimental evaluation results that we have achieved using several benchmark datasets seem to show that our approach could be promising.
... Table 1 shows the shortcomings of diffenrent RA and PRA-based matching systems. In the process of ontology matching, the RA-based matching method compare the solution with the reference alignment, and these systems are mainly found in the literature [25][26][27]46] and [4]. Although it can improve the precision of the matching result to some extent, but it is not reasonable: because it is time & labor-consuming to build the reference alignment in practice. ...
Sensor ontology is a standard conceptual model that describes information of sensor device, which includes the concepts of various sensor modules and the relationships between them. The problem of heterogeneity between sensor ontologies is introduced because different sensor ontology engineers have different ways of describing sensor devices and different structures for the construction of sensor ontologies. Addressing the heterogeneity of sensor ontologies contributes to facilitate the semantic fusion of two sensor ontologies, enabling the sharing and reuse of sensor information. To solve the above problem, an ontology meta-matching method is proposed by this paper to find out the correspondence between entities in distinct sensor ontologies. How to measure the degree of similarity between entities with a set of suitable similarity measures and how to better integrate multiple measures to determine the equivalent entities are the challenges of the ontology meta-matching problem. In this paper, two approximate measurement methods of the quality for ontology matching results are designed, and a multi-objective optimization model for the ontology meta-matching problem is constructed based on these methods. Eventually, a multi-objective particle swarm optimization (MOPSO) algorithm is propounded to dispose of the problem and optimize the quality of ontology meta-matching results, which is named density and distribution-based competitive mechanism multi-objective particle swarm algorithm (D $$^{2}$$ 2 CMOPSO). The sophistication of the D $$^{2}$$ 2 CMOPSO based sensor ontology meta-matching method is verified through experiments. Comparing with other matching systems and advanced systems of Ontology Alignment Evaluation Initiative (OAEI), the proposed method can improve the quality of matching results more effectively.
... Essentially, the ontology aligning problem can be regarded as an optimizing issue that aims at maximizing the quality of final alignment, and EAbased aligning techniques have become a popular methodology to address this problem. The first EA-based ontology aligning technique is proposed by Martinez-Gil et al. (2008) which tries to find an optimal way of combining different similarity measures for determining the final alignment. Later on, researchers have done a lot to improve this category of EA-based aligning techniques. ...
Smart Environment (SE) focuses on the initiatives for healthy living, where ecological issues and biodiversity play a vital role in the environment and sustainability. To manage the knowledge on ecology and biodiversity and preserve the ecosystem and biodiversity simultaneously, it is necessary to align the data entities in different ecology and biodiversity ontologies. Since the problem of Ecology and Biodiversity Ontology Alignment (EBOA) is a large-scale optimization problem with sparse solutions, finding high-quality EBOA is an open challenge. Evolutionary Algorithm (EA) is a state-of-the-art technique in the ontology aligning domain, and this study further proposes an Adaptive Compact EA (ACEA) to address the problem of EBOA, which uses semantic reasoning to reduce searching space and adaptively guides searching direction to improve the algorithm's performance. In addition, we formally model the problem of EBOA as a discrete optimization problem, which maximizes the alignment's completeness and correctness through determining an optimal entity corresponding set. After that, a hybrid entity similarity measure is presented to distinguish the heterogeneous data entities, and an ACEA-based aligning technique is proposed. The experiment uses the famous Biodiversity and Ecology track to test ACEA's performance, and the experimental results show that ACEA-based aligning technique statistically outperforms other EA-based and state-of-the-art aligning techniques.
... These authors were the first to propose that instead of using a matching algorithm (also known as a matcher), machine learning techniques should be used to create the most effective and efficient ensemble of matchers. From this seminal work, research turned to the proposal of machine learning techniques to aggregate the basic matchers, being GAOM (Wang et al., 2006) and GOAL (Martinez-Gil et al., 2008) two of the first works that were able to generate the ensemble using genetic algorithms. The main idea was that it could be possible to optimize the precision value, the recall value, or a combination of both called f-measure. ...
Ontology meta-matching techniques have been consolidated as one of the best approaches to face the problem of discovering semantic relationships between knowledge models that belong to the same domain but have been developed independently. After more than a decade of research, the community has reached a stage of maturity characterized by increasingly better results and aspects such as the robustness and scalability of solutions have been solved. However, the resulting models remain practically intelligible to a human operator. In this work, we present a novel approach based on Mamdani fuzzy inference exploiting a model very close to natural language. This fact has a double objective: to achieve results with high degrees of accuracy but at the same time to guarantee the interpretability of the resulting models. After validating our proposal with several ontological models popular in the biomedical field, we can conclude that the results obtained are promising.
