Ontology alignment or matching is the operation that takes two ontologies and produces a set of semantic correspondences usually semantic similarities between some elements of one of them and some elements of the other. A rigorous, efficient and scalable similarity measure is a pre-requisite of an ontology alignment process. This paper presents a semantic similarity measure based on a matrix represention of nodes from an RDF labelled directed graph. An entity is described with respect to how it relates to other entities using N -dimensional vectors, being N the number of selected external predicates.
We adapt a known graph matching algorithm when applying this idea to the alignment of two ontologies.Ontology X Epistemology
Optimizing Ontology Alignment through Improved NSGA-II
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This is a preview of subscription content, log in to check access. Bisson, M. Doan, A. Ehrig, M. Blondel, V. SIAM Rev. Hu, W. Integrating Ontologies Google Scholar. Euzenat, J. In: Proc. Hopcroft, J. SIAM J. Leacock, C. Lee, J. Owl web ontology language overview. Papadimitriou, C.
Prentice-Hall, Inc. Rahm, E. Salton, G. Wu, Z.Ontology alignment is an important part of enabling the semantic web to reach its full potential.
The vast majority of ontology alignment systems use one or more string similarity metrics, but often the choice of which metrics to use is not given much attention.
In this work we evaluate a wide range of such metrics, along with string pre-processing strategies such as removing stop words and considering synonyms, on different types of ontologies. We also present a set of guidelines on when to use which metric. We furthermore show that if optimal string similarity metrics are chosen, those alone can produce alignments that are competitive with the state of the art in ontology alignment systems.
Advertisement Hide. International Semantic Web Conference. String Similarity Metrics for Ontology Alignment. Conference paper. This process is experimental and the keywords may be updated as the learning algorithm improves. Download to read the full conference paper text. Aguirre, J.
In: Proc. Ashburner, M. Bethea, W. Branting, L. ACM Google Scholar. Cheatham, M. Cohen, W. Curino, C. Euzenat, J. QLambrix, P. Lin, D. Maedche, A.6 motivi per cui dovresti sempre indossare i calzini prima di dormire
EKAW Monge, A. Ngo, D.
Ontology Similarity in the Alignment Space
In: Cimiano, P. ESWC LNCS, vol. Stoilos, G. Valarakos, A. In: Vouros, G. SETN Personalised recommendations.
Biomedical ontology alignment: an approach based on representation learning
Cite paper How to cite? ENW EndNote.As the ontology alignment facilitates the knowledge exchange among the heterogeneous data sources, several methods have been introduced in literature. Nevertheless, few of them have been interested in decreasing the problem complexity and reducing the research space of correspondences between the input ontologies. This paper presents a new approach for ontology alignment based on the ontology knowledge mining. The latter consists on producing for each ontology a hierarchical structure of fuzzy conceptual clusters, where a concept can belong to several clusters simultaneously.
Each level of the hierarchy reflects the knowledge granularity degree of the knowledge base in order to improve the effectiveness and speediness of the information retrieval.Maximum distance between two occurrences of same element in array
Actually, such method allows the knowledge granularity analyze between the ontologies and facilitates several ontology engineering techniques. The ontology alignment process is performed iteratively over the produced hierarchical structure of the fuzzy clusters using semantic techniques.
Once the correspondent clusters are identified, we consider both syntactic and structural characteristics of their correspondent entities. The system performs good results in the terms of precision and recall with respect to other alignment system. Ontology, as it represents a mean to formalize the domain knowledge, has become the enabler of the knowledge exchange between the heterogeneous data sources. In practice, it is crucial to employ well specific parts from the offered ontologies to accomplish the best results of knowledge sharing.
Hence, in order to achieve the semantic interoperability among the domain ontologies, it is required to discover correspondences across these knowledge bases. This ontology engineering technique is called the ontology alignment. However, for large and voluminous ontologies, where the research space is noticeably huge, the correspondences establishment becomes more complex and the effectiveness of most of these systems underperforms in terms of execution time, allocated memory size or mappings results precision [ 2 ].
In fact, during an alignment process, most of the existing approaches compare the individual couples of entities using one or more similarity technique and then the results of these techniques are aggregated using a variety of aggregation strategies, such as, the system proposed in [ 3 ]. The latter uses three different matching strategies name-based, metadata-based and instance-based whose results are then filtered and combined. However, the issue arises when ontologies are voluminous with hundreds or thousands of concepts and the alignment task turns out to be hard to handle with multi-attributes entities.
In the perspective of performing both speediness and effectiveness of the ontology alignment process, some researchers have tackled the problem of scalability with the use of the clustering algorithms. Then, it constructs disjoint blocks out of these clusters.
This proximity is based on anchors shared entities. The more these clusters share anchors, the more similar they are. It starts by transforming ontologies into graphs. Then, clustering algorithm is applied to partition the graphs into disjoint clusters. The use of limited information about the cluster may result in less alignment quality. To dependently cluster ontologies, TaxoMap [ 6 ] uses a co-clustering technique, the system determines similar modules through the clustering process.
The system provides one-to-many mappings between single concepts. In [ 7 ], the author proposed a clustering approach based on structural nodes similarity. Therefore, each cluster of the source ontology has to be aligned with only one subset of the target ontology. The method outputs a set of alignments between concepts within semantically similar subsets. The authors in [ 9 ] address the problem of aligning large class hierarchies by introducing a partition-based block approach.
The process is based on predefined anchors and uses structural and linguistic similarities to partition class hierarchies into small blocks. A structural clustering method based on network analysis was proposed in [ 10 ]. Although, those approaches contribute slightly in enhancing the alignment results they suffer from several limitations.
Therefore, a generic approach called FHCbM Fuzzy Hierarchical clustering based method based on the ontology knowledge mining is proposed to address the challenge of the increased concepts sets size to be treated. Such reorganization allows the knowledge granularity levels analyze as well as the ontology alignment process enhancing by reducing the problem complexity. The use of fuzzy clustering allows each element to belong with distinct degrees to many clusters, this leads to a flexible representation.Measuring similarity between ontologies can be very useful for different purposes, e.
Classical measures compute similarities or distances in an ontology space by directly comparing the content of ontologies. We introduce a new family of ontology measures computed in an alignment space: they evaluate the similarity between two ontologies with regard to the available alignments between them.
International Journal of Computational Intelligence Systems
We define two sets of such measures relying on the existence of a path between ontologies or on the ontology entities that are preserved by the alignments. The former accounts for known relations between ontologies, while the latter reflects the possibility to perform actions such as instance import or query translation. All these measures have been implemented in the OntoSim library, that has been used in experiments which showed that entity preserving measures are comparable to the best ontology space measures.Chess mega database 2019
Moreover, they showed a robust behaviour with respect to the alteration of the alignment space. Documents: Advanced Search Include Citations. Abstract Abstract. Powered by:.Measuring similarity between ontologies can be very useful for different purposes, e. Classical measures compute similarities or distances in an ontology space by directly comparing the content of ontologies.
We introduce a new family of ontology measures computed in an alignment space: they evaluate the similarity between two ontologies with regard to the available alignments between them. We define two sets of such measures relying on the existence of a path between ontologies or on the ontology entities that are preserved by the alignments.
The former accounts for known relations between ontologies, while the latter reflects the possibility to perform actions such as instance import or query translation. All these measures have been implemented in the OntoSim library, that has been used in experiments which showed that entity preserving measures are comparable to the best ontology space measures.
Moreover, they showed a robust behaviour with respect to the alteration of the alignment space. Skip to main content Skip to sections.
Conference paper. This process is experimental and the keywords may be updated as the learning algorithm improves. Download to read the full conference paper text.
David, J. In: Sheth, A. ISWC LNCS, vol. Euzenat, J. EKAW Hu, B. In: Staab, S. In: Franconi, E. ESWC Springer, Heidelberg Google Scholar. In: Proc. Ehrig, M. Delbru, R. In: Aroyo, L.
Tverski, A.Over the past decades, a large number of complex optimization problems have been widely addressed through multiobjective evolutionary algorithms MOEAsand the knee solutions of the Pareto front PF are most likely to be fitting for the decision maker DM without any user preferences. This work investigates the ontology matching problem, which is a challenge in the semantic web SW domain. To this end, a popular MOEA, i. Over the past decades, a large number of complex optimization problems have been widely addressed through multiobjective evolutionary algorithms MOEAs [ 1 — 3 ].
Generally, the objectives are clashing, preventing concurrent optimization for each objective, which is a challenging and realistic issue [ 2 ]. The general approach is to obtain a set of solutions, the so-called Pareto front PFthat do not dominate each other, and the knee solution of PF is most likely to be fitting for the decision maker DM without any user predilections [ 45 ].
In this scenario, the problem of ontology alignment in the domain of semantic web SW is studied. Ontology, as the kernel technique in SW, can represent a formal definition on the domain knowledge. And, matching ontologies can help find their heterogeneous entities, which can speed up the translation discovery and integrate the knowledge [ 6 ].
Meanwhile, a variety of hybrid optimization approaches have been introduced in the recent years to improve the accuracy and speed of convergence to true optimum solutions which integrate EA with the local search algorithm LS [ 311 — 13 ]. This combination allows gaining high diversity of population due to the high optimization ability which can augment the speed of convergence and reduce the probability of prematurity constringency.
The remainder of the paper is organized as follows: Section 2 demonstrates the related work. Section 3 provides definition of the ontology matching problem and detailed depiction of similarity measures and the aggregation strategy; Section 4 shows the iNSGA-II-based ontology matching technique in detail; Section 5 demonstrates the experimental results; and eventually, Section 6 delineates the conclusion. A variety of EA-based matchers with the trait of effectively tackling the ontology alignment problem have been introduced in the recent years.
Naya et al. After that, a memetic algorithm is proposed by Acampora et al. Acampora et al. Biniz et al.
Xue et al. The second category of the ontology matching technique based on EA devotes to determine the optimum alignment set of entities.
Wang et al. Chu et al. A memetic algorithm was proposed by Xue et al. Several approaches have been proposed which can efficiently determine the alignments using EA involving user coordination [ 131819 ]. An ontology consists of classes, data type properties, and object properties, which are generally called ontology entities [ 22 ].Wooden model plans
The purpose of ontology matching is determining the entity correspondence set, which is the so-called ontology alignment. Traditionally, an f -measure was often utilized to assess the quality of alignments, which is defined as follows: where R is the reference alignment originated from domain experts and A is the alignment derived from the ontology matcher. But in the real world, obtaining the reference alignments is particularly expensive, especially when handing the large-scale ontologies.
In this paper, we utilize three approximate measures, i.Metrics details. While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching.
Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors.
The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. Our system obtained overall F-scores of Our proposed representation learning approach leverages terminological embeddings to capture semantic similarity.
Our results provide evidence that the approach produces embeddings that are especially well tailored to the ontology matching task, demonstrating a novel pathway for the problem. Ontologies seek to alleviate the Tower of Babel effect by providing standardized specifications of the intended meanings of the terms used in given domains. Ideally, in order to achieve a unique specification for each term, ontologies would be built in such a way as to be non-overlapping in their content.
In many cases, however, domains have been represented by multiple ontologies and there thus arises the task of ontology matchingwhich consists in identifying correspondences among entities types, classes, relations across ontologies with overlapping content. Different ontological representations draw on the different sets of natural language terms used by different groups of human experts [ 2 ]. In this way, different and sometimes incommensurable terminologies are used to describe the same entities in reality.
This issue, known as the human idiosyncrasy problem [ 1 ], constitutes the main challenge to discovering equivalence relations between terms in different ontologies.
Ontological terms are typically common nouns or noun phrases. According to whether they do or do not include prepositional clauses [ 3 ], the latter may be either composite for example Neck of femur or simple for example First tarsometatarsal joint or just Joint.
Such grammatical complexity of ontology terms needs to be taken into account in identifying semantic similarity. Yet the two expressions are not semantically similar. The sorts of large ontologies that are the typical targets of semantic similarity identification contain a huge number of such descriptively associated term pairs.
This difficulty in distinguishing similarity from descriptive association is a well-studied problem in both cognitive science [ 5 ] and NLP [ 6 ].
Traditionally, feature engineering has been the predominant way to approach the ontology matching problem [ 7 ]. In machine learning, a feature is an individual measurable property of a phenomenon in the domain being observed [ 8 ].
Feature engineering consists in crafting features of the data that can be used by machine learning algorithms in order to achieve specific tasks. Unfortunately determining which hand-crafted features will be valuable for a given task can be highly time consuming. To make matters worse, as Cheatham and Hitzler have recently shown, the performance of ontology matching based on such engineered features varies greatly with the domain described by the ontologies [ 10 ].
As a complement to feature engineering, attempts have been made to develop machine-learning strategies for ontology matching based on binary classification [ 11 ]. This means a classifier is trained on a set of alignments between ontologies in which correct and incorrect mappings are identified with the goal of using the trained classifier to predict whether an assertion of semantic equivalence between two terms is or is not true.
In general, the number of true alignments between two ontologies is several orders of magnitude smaller than the number of all possible mappings, and this introduces a serious class imbalance problem [ 12 ].
This abundance of negative examples hinders the learning process, as most data mining algorithms assume balanced data sets and so the classifier runs the risk of degenerating into a series of predictions to the effect that every alignment comes to be marked as a misalignment.
Both standard approaches thus fail: feature engineering because of the failure of generalization of the engineered features, and supervised learning because of the class imbalance problem. Our proposal is to address these limitations through the exploitation of unsupervised learning approaches for ontology matching drawing on the recent rise of distributed neural representations DNRsin which for example words and sentences are embedded in a high-dimensional Euclidean space [ 13 — 17 ] in order to provide a means of capturing lexical and sentence meaning in an unsupervised manner.Ohada uniform act on cooperative societies
The way this works is that the machine learns a mapping from words to high-dimensional vectors which take account of the contexts in which words appear in a plurality of corpora.
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