Relevant bibliographies by topics / Ontology alignment

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Ontology alignment'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 April 2022

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Journal articles on the topic "Ontology alignment"

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Sampson, Jennifer, John Krogstie, and Csaba Veres. "Ontology Alignment Quality." International Journal of Information System Modeling and Design 2, no. 3 (July 2011): 1–23. http://dx.doi.org/10.4018/jismd.2011070101.

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Recently semantic web technologies, such as ontologies, have been proposed as key enablers for integrating heterogeneous data schemas in business and governmental systems. Algorithms designed to align different but related ontologies have become necessary as differing ontologies proliferate. The process of ontology alignment seeks to find corresponding entities in a second ontology with the same or the closest meaning for each entity in a single ontology. This research is motivated by the need to provide tools and techniques to support the task of validating ontology alignment statements, since it cannot be guaranteed that the results from automated tools are accurate. The authors present a framework for understanding ontology alignment quality and describe how AlViz, a tool for visual ontology alignment, may be used to improve the quality of alignment results. An experiment was undertaken to test the claim that AlViz supports the task of validating ontology alignments. A promising result found that the tool has potential for identifying missing alignments and for rejecting false alignments.
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Ivanova, Tatyana. "Ontology Alignment." International Journal of Knowledge and Systems Science 1, no. 4 (October 2010): 22–40. http://dx.doi.org/10.4018/jkss.2010100102.

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A grand number of ontologies have been developed and are publicly accessible on the Web making techniques for mapping between various ontologies more significant. Research has been made in the area of ontology alignment, a grand number of approaches, algorithms, and tools have been developed in recent years, but are still not “perfect” and excellent knowledge. In this article, the author makes an overall view of the state of ontology alignment, including the latest research, comparing many approaches, and analyzing their strengths and drawbacks. The main motivation behind this work is the fact that despite many component matching solutions that have been developed so far, there is no integrated solution that is a clear success, which can be used for ontology mapping in all cases, making knowledge about developed ontology mapping methods and their clear classification needed.
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Idoudi, Rihab, Karim Saheb Ettabaa, Basel Solaiman, and Kamel Hamrouni. "Ontology Knowledge Mining for Ontology Alignment." International Journal of Computational Intelligence Systems 9, no. 5 (September 2, 2016): 876–87. http://dx.doi.org/10.1080/18756891.2016.1237187.

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Zhou, Lu, Michelle Cheatham, Adila Krisnadhi, and Pascal Hitzler. "GeoLink Data Set: A Complex Alignment Benchmark from Real-world Ontology." Data Intelligence 2, no. 3 (July 2020): 353–78. http://dx.doi.org/10.1162/dint_a_00054.

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Ontology alignment has been studied for over a decade, and over that time many alignment systems and methods have been developed by researchers in order to find simple 1-to-1 equivalence matches between two ontologies. However, very few alignment systems focus on finding complex correspondences. One reason for this limitation may be that there are no widely accepted alignment benchmarks that contain such complex relationships. In this paper, we propose a real-world data set from the GeoLink project as a potential complex ontology alignment benchmark. The data set consists of two ontologies, the GeoLink Base Ontology (GBO) and the GeoLink Modular Ontology (GMO), as well as a manually created reference alignment that was developed in consultation with domain experts from different institutions. The alignment includes 1:1, 1:n, and m:n equivalence and subsumption correspondences, and is available in both Expressive and Declarative Ontology Alignment Language (EDOAL) and rule syntax. The benchmark has been expanded from its original version to contain real-world instance data from seven geoscience data providers that has been published according to both ontologies. This allows it to be used by extensional alignment systems or those that require training data. This benchmark has been incorporated into the Ontology Alignment Evaluation Initiative (OAEI) complex track to help researchers test their automated alignment systems and algorithms. This paper also analyzes the challenges inherent in effectively generating, detecting, and evaluating complex ontology alignments and provides a road map for future work on this topic.
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Huang, Yikun, Xingsi Xue, and Chao Jiang. "Semantic Integration of Sensor Knowledge on Artificial Internet of Things." Wireless Communications and Mobile Computing 2020 (July 25, 2020): 1–8. http://dx.doi.org/10.1155/2020/8815001.

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Artificial Internet of Things (AIoT) integrates Artificial Intelligence (AI) with the Internet of Things (IoT) to create the sensor network that can communicate and process data. To implement the communications and co-operations among intelligent systems on AIoT, it is necessary to annotate sensor data with the semantic meanings to overcome heterogeneity problem among different sensors, which requires the utilization of sensor ontology. Sensor ontology formally models the knowledge on AIoT by defining the concepts, the properties describing a concept, and the relationships between two concepts. Due to human’s subjectivity, a concept in different sensor ontologies could be defined with different terminologies and contexts, yielding the ontology heterogeneity problem. Thus, before using these ontologies, it is necessary to integrate their knowledge by finding the correspondences between their concepts, i.e., the so-called ontology matching. In this work, a novel sensor ontology matching framework is proposed, which aggregates three kinds of Concept Similarity Measures (CSMs) and an alignment extraction approach to determine the sensor ontology alignment. To ensure the quality of the alignments, we further propose a compact Particle Swarm Optimization algorithm (cPSO) to optimize the aggregating weights for the CSMs and a threshold for filtering the alignment. The experiment utilizes the Ontology Alignment Evaluation Initiative (OAEI)’s conference track and two pairs of real sensor ontologies to test cPSO’s performance. The experimental results show that the quality of the alignments obtained by cPSO statistically outperforms other state-of-the-art sensor ontology matching techniques.
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Xue, Xingsi, and Jianhua Liu. "Optimizing Ontology Alignment Through Compact MOEA/D." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 04 (February 2, 2017): 1759004. http://dx.doi.org/10.1142/s0218001417590042.

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In order to support semantic inter-operability in many domains through disparate ontologies, we need to identify correspondences between the entities across different ontologies, which is commonly known as ontology matching. One of the challenges in ontology matching domain is how to select weights and thresholds in the ontology aligning process to aggregate the various similarity measures to obtain a satisfactory alignment, so called ontology meta-matching problem. Nowadays, the most suitable methodology to address the ontology meta-matching problem is through Evolutionary Algorithm (EA), and the Multi-Objective Evolutionary Algorithms (MOEA) based approaches are emerging as a new efficient methodology to face the meta-matching problem. Moreover, for dynamic applications, it is necessary to perform the system self-tuning process at runtime, and thus, efficiency of the configuration search strategies becomes critical. To this end, in this paper, we propose a problem-specific compact Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), in the whole ontology matching process of ontology meta-matching system, to optimize the ontology alignment. The experimental results show that our proposal is able to highly reduce the execution time and main memory consumption of determining the optimal alignments through MOEA/D based approach by 58.96% and 67.60% on average, respectively, and the quality of the alignments obtained is better than the state of the art ontology matching systems.
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Antunes, Cauã Roca, Alexandre Rademaker, and Mara Abel. "A faster and less aggressive algorithm for correcting conservativity violations in ontology alignments." Applied Ontology 16, no. 3 (July 21, 2021): 277–96. http://dx.doi.org/10.3233/ao-210243.

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Ontologies are computational artifacts that model consensual aspects of reality. In distributed contexts, applications often need to utilize information from several distinct ontologies. In order to integrate multiple ontologies, entities modeled in each ontology must be matched through an ontology alignment. However, imperfect alignments may introduce inconsistencies. One kind of inconsistency, which is often introduced, is the violation of the conservativity principle, that states that the alignment should not introduce new subsumption relations between entities from the same source ontology. We propose a two-step quadratic-time algorithm for automatically correcting such violations, and evaluate it against datasets from the Ontology Alignment Evaluation Initiative 2019, comparing the results to a state-of-the-art approach. The proposed algorithm was significantly faster and less aggressive; that is, it performed fewer modifications over the original alignment when compared to the state-of-the-art algorithm.
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Haddad, AbdulHameed, and Akram Selah. "Ontology Alignment with FOAM++." International Journal of Computer Applications 18, no. 8 (March 31, 2011): 14–20. http://dx.doi.org/10.5120/2305-2435.

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Scharffe, François, Ondřej Zamazal, and Dieter Fensel. "Ontology alignment design patterns." Knowledge and Information Systems 40, no. 1 (April 26, 2013): 1–28. http://dx.doi.org/10.1007/s10115-013-0633-y.

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Lin, Weiwei, and Reiko Haga. "Matching Cyber Security Ontologies through Genetic Algorithm-Based Ontology Alignment Technique." Security and Communication Networks 2021 (November 30, 2021): 1–7. http://dx.doi.org/10.1155/2021/4856265.

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Security ontology can be used to build a shared knowledge model for an application domain to overcome the data heterogeneity issue, but it suffers from its own heterogeneity issue. Finding identical entities in two ontologies, i.e., ontology alignment, is a solution. It is important to select an effective similarity measure (SM) to distinguish heterogeneous entities. However, due to the complex semantic relationships among concepts, no SM is ensured to be effective in all alignment tasks. The aggregation of SMs so that their advantages and disadvantages complement each other directly affects the quality of alignments. In this work, we formally define this problem, discuss its challenges, and present a problem-specific genetic algorithm (GA) to effectively address it. We experimentally test our approach on bibliographic tracks provided by OAEI and five pairs of security ontologies. The results show that GA can effectively address different heterogeneous ontology-alignment tasks and determine high-quality security ontology alignments.
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Dissertations / Theses on the topic "Ontology alignment"

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Zamazal, Ondřej. "Pattern-based Ontology Matching and Ontology Alignment Evaluation." Doctoral thesis, Vysoká škola ekonomická v Praze, 2006. http://www.nusl.cz/ntk/nusl-77051.

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Ontology Matching is one of the hottest topic within the Semantic Web of recent years. There is still ample of space for improvement in terms of performance. Furthermore, current ontology matchers mostly concentrate on simple entity to entity matching. However, matching of whole structures could bring some additional complex relationships. These structures of ontologies can be captured as ontology patterns. The main theme of this thesis is an examination of pattern-based ontology matching enhanced with ontology transformation and pattern-based ontology alignment evaluation. The former is examined due to its potential benefits regarding complex matching and matching as such. The latter is examined because complex hypotheses could be beneficial feedback as complement to traditional evaluation methods. These two tasks are related to four different topics: ontology patterns, ontology transformation, ontology alignment evaluation and ontology matching. With regard to those four topics, this work covers the following aspects: * Examination of different aspects of ontology patterns. Particularly, description of relevant ontology patterns for ontology transformation and for ontology matching (such as naming, matching and transformation patterns). * Description of a pattern-based method for ontology transformation. * Introduction of new methods for an alignment evaluation; including using patterns as a complex structures for more detailed analysis. * Experiments and demonstrations of new concepts introduced in this thesis. The thesis first introduces naming pattern and matching pattern classification on which ontology transformation framework is based. Naming patterns are useful for detection of ontology patterns and for generation of new names for entities. Matching patterns are basis for transformation patterns in terms of sharing some building blocks. In comparison with matching patterns, transformation patterns have transformation links that represent way how parts of ontology patterns are transformed. Besides several evaluations and implementations, the thesis provides a demonstration of getting complex matching due to ontology transformation process. Ontology transformation framework has been implemented in Java environment where all generic patterns are represented as corresponding Java objects. Three main implemented services are made generally available as RESTful services: ontology pattern detection, transformation instruction generation and ontology transformation.
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Ivanova, Valentina. "Fostering User Involvement in Ontology Alignment and Alignment Evaluation." Doctoral thesis, Linköpings universitet, Databas och informationsteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-143034.

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The abundance of data at our disposal empowers data-driven applications and decision making. The knowledge captured in the data, however, has not been utilized to full potential, as it is only accessible to human interpretation and data are distributed in heterogeneous repositories. Ontologies are a key technology unlocking the knowledge in the data by providing means to model the world around us and infer knowledge implicitly captured in the data. As data are hosted by independent organizations we often need to use several ontologies and discover the relationships between them in order to support data and knowledge transfer. Broadly speaking, while ontologies provide formal representations and thus the basis, ontology alignment supplies integration techniques and thus the means to turn the data kept in distributed, heterogeneous repositories into valuable knowledge. While many automatic approaches for creating alignments have already been developed, user input is still required for obtaining the highest-quality alignments. This thesis focuses on supporting users during the cognitively intensive alignment process and makes several contributions. We have identified front- and back-end system features that foster user involvement during the alignment process and have investigated their support in existing systems by user interface evaluations and literature studies. We have further narrowed down our investigation to features in connection to the, arguably, most cognitively demanding task from the users’ perspective—manual validation—and have also considered the level of user expertise by assessing the impact of user errors on alignments’ quality. As developing and aligning ontologies is an error-prone task, we have focused on the benefits of the integration of ontology alignment and debugging. We have enabled interactive comparative exploration and evaluation of multiple alignments at different levels of detail by developing a dedicated visual environment—Alignment Cubes—which allows for alignments’ evaluation even in the absence of reference alignments. Inspired by the latest technological advances we have investigated and identified three promising directions for the application of large, high-resolution displays in the field: improving the navigation in the ontologies and their alignments, supporting reasoning and collaboration between users.
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Ivanova, Valentina. "Integration of Ontology Alignment and Ontology Debugging for Taxonomy Networks." Licentiate thesis, Linköpings universitet, Databas och informationsteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-102953.

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Semantically-enabled applications, such as ontology-based search and data integration, take into account the semantics of the input data in their algorithms. Such applications often use ontologies, which model the application domains in question, as well as alignments, which provide information about the relationships between the terms in the different ontologies. The quality and reliability of the results of such applications depend directly on the correctness and completeness of the ontologies and alignments they utilize. Traditionally, ontology debugging discovers defects in ontologies and alignments and provides means for improving their correctness and completeness, while ontology alignment establishes the relationships between the terms in the different ontologies, thus addressing completeness of alignments. This thesis focuses on the integration of ontology alignment and debugging for taxonomy networks which are formed by taxonomies, the most widely used kind of ontologies, connected through alignments. The contributions of this thesis include the following. To the best of our knowledge, we have developed the first approach and framework that integrate ontology alignment and debugging, and allow debugging of modelling defects both in the structure of the taxonomies as well as in their alignments. As debugging modelling defects requires domain knowledge, we have developed algorithms that employ the domain knowledge intrinsic to the network to detect and repair modelling defects. Further, a system has been implemented and several experiments with real-world ontologies have been performed in order to demonstrate the advantages of our integrated ontology alignment and debugging approach. For instance, in one of the experiments with the well-known ontologies and alignment from the Anatomy track in Ontology Alignment Evaluation Initiative 2010, 203 modelling defects (concerning incomplete and incorrect information) were discovered and repaired.
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Ehrig, Marc. "Ontology alignment : bridging the semantic gap /." New York, NY : Springer, 2007. http://www.loc.gov/catdir/toc/fy0707/2006928852.html.

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McCurdy, Helena Brooke. "WikiMatcher: Leveraging Wikipedia for Ontology Alignment." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1461710743.

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Souza, Bernardo Severo de. "An adaptative approach for ontology alignment visualization." Pontif?cia Universidade Cat?lica do Rio Grande do Sul, 2017. http://tede2.pucrs.br/tede2/handle/tede/7471.

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O aumento do volume de dados n?o estruturados na Web nas ?ltimas d?cadas tem sido impulsionado pelo surgimento de novos meios de comunica??o, dispositivos e tecnologias. Neste contexto se desenvolve a Web Sem?ntica, cujo objetivo ? o de atribuir uma camada de representa??o de conhecimento a esses dados, facilitando o tratamento por processos automatizados. Ontologias s?o elementos chave da Web Sem?ntica, oferecendo uma descri??o dos conceitos e dos relacionamentos entre os mesmos para um dom?nio espec?fico. Entretanto, ontologias de um mesmo dom?nio podem divergir em sua estrutura, granularidade ou terminologia, necessitando que um processo de mapeamento entre as mesmas seja realizado, produzindo um conjunto de correspond?ncias entre entidades semanticamente relacionadas (alinhamento). Um n?mero crescente de abordagens de mapeamento tem surgido na literatura e a necessidade de avaliar e comparar qualitativamente os alinhamentos produzidos se faz presente. Tarefas que fazem uso de alinhamentos passaram a demandar melhores representa??es gr?ficas dos mesmos. Neste contexto, foi realizada uma pesquisa com especialistas em alinhamentos para identificar os aspectos mais importantes em uma visualiza??o de alinhamentos. Este trabalho apresenta ent?o uma abordagem adaptativa de visualiza??o para alinhamentos, que permite ao usu?rio escolher como e o que visualizar, de acordo com prefer?ncias pr?prias ou para uma atividade sendo realizada no momento (cria??o, manipula??o, avalia??o, etc.). Por fim, um prot?tipo foi constru?do com o intuito de validar a solu??o. Os resultados obtidos da avalia??o dos usu?rios com o prot?tipo mostram que a abordagem lida com os problemas que se prop?e a resolver, com uma margem para trabalhos futuros em formas de visualiza??o de alinhamentos.
The increase in the volume of unstructured web data in recent decades has been driven by the arising of new media, devices and technologies. In this context, the Semantic Web was developed, whose objective is to provide a layer of knowledge representation to that data, facilitating the treatment by automated processes. Ontologies are key elements of the Semantic Web, providing a description of the concepts and relationships between them, for a specific domain. However, ontologies of the same domain may differ in structure, granularity or terminology, requiring a process of matching between them to be performed, producing a set of correspondences between semantically related entities (alignment). A growing number of matching approaches have emerged in the literature, and the need to evaluate and qualitatively compare the produced alignments is presented. Tasks that make use of alignments started to demand better graphical representations for it. In this context, a survey was conducted with alignment specialists to identify the most important aspects in an alignment visualization. This work presents an adaptative approach for alignment visualization, that allows users to choose how and what to visualize, according to their own preferences or the task being performed at that moment (creation, manipulation, evaluation, etc.). Finally, a prototype was built with the purpose of validating the solution. The results obtained from the prototype validation with users show that the approach handles the problems it proposes to solve, with a margin for future work on alignment visualization.
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Boujari, Tahereh. "Instance-based ontology alignment using decision trees." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-84918.

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Using ontologies is a key technology in the semantic web. The semantic web helps people to store their data on the web, build vocabularies, and has written rules for handling these data and also helps the search engines to distinguish between the information they want to access in web easier. In order to use multiple ontologies created by different experts we need matchers to find the similar concepts in them to use it to merge these ontologies. Text based searches use the string similarity functions to find the equivalent concepts inside ontologies using their names.This is the method that is used in lexical matchers. But a global standard for naming the concepts in different research area does not exist or has not been used. The same name may refer to different concepts while different names may describe the same concept. To solve this problem we can use another approach for calculating the similarity value between concepts which is used in structural and constraint-based matchers. It uses relations between concepts, synonyms and other information that are stored in the ontologies. Another category for matchers is instance-based that uses additional information like documents related to the concepts of ontologies, the corpus, to calculate the similarity value for the concepts. Decision trees in the area of data mining are used for different kind of classification for different purposes. Using decision trees in an instance-based matcher is the main concept of this thesis. The results of this implemented matcher using the C4.5 algorithm are discussed. The matcher is also compared to other matchers. It also is used for combination with other matchers to get a better result.
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Carbonetto, Andrew August. "Ontology alignment in the presence of a domain ontology : finding protein homology." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/821.

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Cheap electronic storage and Internet bandwidth has increased the amount of online data. Large quantities of metadata are created to manage this wealth of information. Methods to organize and structure metadata has led to the development of ontologies - data that is organized to describe the relation between elements. The creation of large ontologies has brought forth the need for ontology management strategies. Ontology alignment and merging techniques are standard operations for ontology management. Accurate ontology alignment methods are typically semi-automatic, meaning they require periodic user input. This becomes infeasible on large ontologies and the accuracy and efficiency drops significantly when these algorithms are forced to align without human interaction. Bioinformatics, for example, has seen the influx of large ontologies, such as signal pathway sets with thousands of elements or protein-protein interaction (PPI) databases with hundreds of thousands of elements. This drives the need for a reliable method of large-scale ontology alignment. Many bioinformatics ontologies contain references to domain ontologies - manually curated ontologies describing additional, general information about the terms in the ontologies. For example, more than 2/3 of proteins in PPI data sets contain at least one annotation to the domain ontology the Gene Ontology. We use the domain ontology references as features to compute similarity between elements. However, there are few efficient ways to compute similarity from structured features. We present a novel, automatic method for aligning ontologies based on such domain ontology features. Specifically, we use simulated annealing to reduce the complexity of the domain ontologys structure by finding approximate relevant clusters of elements. An intermediate step performs hierarchical clustering based on the similarity between elements of the ontology. Then the mapping between clusters across aligning ontologies is built. The final step builds an alignment between matched clusters. To evaluate our methods, we perform an alignment between Human (Homo Sapiens) and Yeast (Saccharomyces cerevisiae) signal pathways provided by the Reactome database. The results were compared against reliable homology studies of proteins. The final mapping produces alignments that are significantly more accurate than the traditional ontology alignment methods, without any human involvement.
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Taye, Mohammad Mustafa. "Ontology alignment mechanisms for improving web-based searching." Thesis, De Montfort University, 2009. http://hdl.handle.net/2086/2423.

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Ontology has been developed to offer a commonly agreed understanding of a domain that is required for knowledge representation, knowledge exchange and reuse across domains. Therefore, ontology organizes information into taxonomies of terms (i.e., concepts, attributes) and shows the relationships between them. In fact, it is considered to be helpful in reducing conceptual confusion for users who need to share applications of different kinds, so it is widely used to capture and organize knowledge in a given domain. Although ontologies are considered to provide a solution to data heterogeneity, from another point of view, the available ontologies could themselves introduce heterogeneity problems. In order to deal with these problems, ontologies must be available for sharing or reusing; therefore, semantic heterogeneity and structural differences need to be resolved among ontologies. This can be done, in some cases, by aligning or matching heterogeneous ontologies. Thus, establishing the relationships between terms in the different ontologies is needed throughout ontology alignment. Semantic interoperability can be established in ontology reconciliation. The original problem is called the ―ontology alignment‖. The alignment of ontologies is concerned with the identification of the semantic relationships (subsumption, equivalence, etc.) that hold between the constituent entities (which can be classes, properties, etc.) of two ontologies. In this thesis, an ontology alignment technique has been developed in order to facilitate communication and build a bridge between ontologies. An efficient mechanism has been developed in order to align entities from ontologies in different description languages (e.g. OWL, RDF) or in the same language. This approach tries to use all the features of ontologies (concept, attributes, relations, structure, etc.) in order to obtain efficiency and high quality results. For this purpose, several matching techniques have been used such as string, structure, heuristic and linguistic matchingtechniques with thesaurus support, as well as human intervention in certain cases, to obtain high quality results. The main aim of the work is to introduce a method for finding semantic correspondences among heterogeneous ontologies, with the intention of supporting interoperability over given domains. The approach brings together techniques in modelling, string matching, computation linguistics, structure matching and heuristic matching, in order to provide a semi-automatic alignment framework and prototype alignment system to support the procedure of ontology alignment in order to improve semantic interoperability in heterogeneous systems. This technique integrates some important features in matching in order to achieve high quality results, which will help when searching and exchanging information between ontologies. Moreover, an ontology alignment system illustrates the solving of the key issues related to heterogeneous ontologies, which uses combination-matching strategies to execute the ontology-matching task. Therefore, it can be used to discover the matching between ontologies. This thesis also describes a prototype implementation of this approach in many real-world case studies extracted from various Web resources. Evaluating our system is done throughout the experiments provided by the Ontology Alignment Evaluation Initiative. The system successfully achieved 93% accuracy for ontology matching. Finally, a comparison between our system and well-known tools is achieved so that our system can be evaluated.
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Hu, Xueheng. "SEMANTIC SIMILARITY IN THE EVALUATION OF ONTOLOGY ALIGNMENT." Miami University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=miami1323323230.

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Books on the topic "Ontology alignment"

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Ontology Alignment. Boston, MA: Springer US, 2007. http://dx.doi.org/10.1007/978-0-387-36501-5.

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Ehrig, Marc. Ontology Alignment: Bridging the Semantic Gap. Springer, 2010.

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Ehrig, Marc. Ontology Alignment: Bridging the Semantic Gap (Semantic Web and Beyond). Springer, 2006.

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Book chapters on the topic "Ontology alignment"

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Lambrix, Patrick, and He Tan. "Ontology Alignment and Merging." In Anatomy Ontologies for Bioinformatics, 133–49. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84628-885-2_6.

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Euzenat, Jérôme. "Algebras of Ontology Alignment Relations." In Lecture Notes in Computer Science, 387–402. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88564-1_25.

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Dragisic, Zlatan, Valentina Ivanova, Patrick Lambrix, Daniel Faria, Ernesto Jiménez-Ruiz, and Catia Pesquita. "User Validation in Ontology Alignment." In Lecture Notes in Computer Science, 200–217. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46523-4_13.

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Euzenat, Jérôme. "An API for Ontology Alignment." In The Semantic Web – ISWC 2004, 698–712. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30475-3_48.

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Fan, Zhengjie. "Data Linking with Ontology Alignment." In Lecture Notes in Computer Science, 854–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30284-8_70.

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Lanzenberger, Monika, and Jennifer Sampson. "Human-Mediated Visual Ontology Alignment." In Lecture Notes in Computer Science, 394–403. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73354-6_43.

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Wu, Xiaojing, Xingsi Xue, and Wenyu Hu. "Argumentation Based Ontology Alignment Extraction." In Advances in Intelligent Systems and Computing, 1028–37. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69717-4_96.

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Thiéblin, Élodie, Ollivier Haemmerlé, Nathalie Hernandez, and Cassia Trojahn. "Task-Oriented Complex Ontology Alignment: Two Alignment Evaluation Sets." In The Semantic Web, 655–70. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93417-4_42.

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Szwabe, Andrzej, Pawel Misiorek, and Przemyslaw Walkowiak. "Reflective Relational Learning for Ontology Alignment." In Advances in Intelligent and Soft Computing, 519–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28765-7_62.

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Do, Chau, and Eric J. Pauwels. "Harnessing Mathematics for Improved Ontology Alignment." In Lecture Notes in Computer Science, 249–54. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03647-2_20.

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Conference papers on the topic "Ontology alignment"

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Ermolayev, Vadim, and Maxim Davidovsky. "Agent-based ontology alignment." In the 2nd International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2254129.2254136.

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Jan, Sadaqat, Maozhen Li, Ghaidaa Al-Sultany, and Hamed Al-Raweshidy. "Ontology alignment using Rough Sets." In 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011). IEEE, 2011. http://dx.doi.org/10.1109/fskd.2011.6020069.

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Raad, Elie, and Joerg Evermann. "Is ontology alignment like analogy?" In SAC 2014: Symposium on Applied Computing. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2554850.2554853.

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Ivanova, Tatyana Ivanova. "Extending Ontology Alignment Evaluation Data Sets for Evaluation of Bulgarian Language – Labelled Ontology Alignment." In 2020 International Conference on Information Technologies (InfoTech). IEEE, 2020. http://dx.doi.org/10.1109/infotech49733.2020.9211054.

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Kim, Minhwan, Jongmo Kim, Kunyoung Kim, and Mye Sohn. "Ontology-aided Word2vec based Synonym Identification for Ontology Alignment." In 2020 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2020. http://dx.doi.org/10.1109/bigcomp48618.2020.00-35.

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Doulaverakis, Charalampos, Stefanos Vrochidis, and Ioannis Kompatsiaris. "Exploiting Visual Similarities for Ontology Alignment." In 7th International Conference on Knowledge Engineering and Ontology Development. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005588200290037.

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"FuzzyAlign - A Fuzzy Method for Ontology Alignment." In International Conference on Knowledge Engineering and Ontology Development. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0004139500980107.

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"DAMO - Direct Alignment for Multilingual Ontologies." In International Conference on Knowledge Engineering and Ontology Development. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003669601100117.

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Cross, V. V., P. Silwal, and Xi Chen. "Semantic similarity measures in ontology alignment." In 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS). IEEE, 2013. http://dx.doi.org/10.1109/ifsa-nafips.2013.6608441.

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Karimi, Hamed, and Ali Kamandi. "Ontology alignment using inductive logic programming." In 2018 4th International Conference on Web Research (ICWR). IEEE, 2018. http://dx.doi.org/10.1109/icwr.2018.8387247.

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Reports on the topic "Ontology alignment"

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Starz, James, and Joe Roberts. Automated Ontology Alignment with Fuselets for Community of Interest (COI) Integration. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada486676.

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