Готові списки джерел за темами / Ontology alignment evaluation initiative

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Добірка наукової літератури з теми "Ontology alignment evaluation initiative"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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Статті в журналах з теми "Ontology alignment evaluation initiative"

Dragisic, Zlatan, Valentina Ivanova, Huanyu Li, and Patrick Lambrix. "Experiences from the anatomy track in the ontology alignment evaluation initiative." Journal of Biomedical Semantics 8, no. 1 (2017): 56. https://doi.org/10.1186/s13326-017-0166-5.

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Background: One of the longest running tracks in the Ontology Alignment Evaluation Initiative is the Anatomy track which focuses on aligning two anatomy ontologies. The Anatomy track was started in 2005. In 2005 and 2006 the task in this track was to align the Foundational Model of Anatomy and the OpenGalen Anatomy Model. Since 2007 the ontologies used in the track are the Adult Mouse Anatomy and a part of the NCI Thesaurus. Since 2015 the data in the Anatomy track is also used in the Interactive track of the Ontology Alignment Evaluation Initiative.Results: I

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Vargas-Vera, Maria, and Miklos Nagy. "Experiences on the Evaluation of DSSim." International Journal of Knowledge Society Research 6, no. 2 (2015): 20–50. http://dx.doi.org/10.4018/ijksr.2015040102.

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This paper presents a comprehensive evaluation of DSSim (DSSim stands for Similarity based on Dempster-Shafer), our ontology alignment system. The authors participated several years in the annual evaluation defined by the Ontology Alignment Initiative (OAEI). Each year their DSSim was evolved and participated in more difficult tracks defined by the Ontology Alignment Initiative. In fact, DSSim obtained exceptional results in the OAEI-2008 Evaluation. In this evaluation (OAEI-2008), DSSim participated on all given tracks namely, benchmark, anatomy, fao, directory, mldirectory, library, very lar

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Harrow, Ian, Ernesto Jiménez-Ruiz, Andrea Splendiani, et al. "Matching disease and phenotype ontologies in the ontology alignment evaluation initiative." Journal of Biomedical Semantics 8, no. 1 (2017): 55. https://doi.org/10.1186/s13326-017-0162-9.

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Background: The disease and phenotype track was designed to evaluate the relative performance of ontology matching systems that generate mappings between source ontologies. Disease and phenotype ontologies are important for applications such as data mining, data integration and knowledge management to support translational science in drug discovery and understanding the genetics of disease.Results: Eleven systems (out of 21 OAEI participating systems) were able to cope with at least one of the tasks in the Disease and Phenotype track. AML, FCA-Map, LogM

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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 (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

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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 (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, th

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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

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Xue, Xingsi, Xiaojing Wu, Chao Jiang, Guojun Mao, and Hai Zhu. "Integrating Sensor Ontologies with Global and Local Alignment Extractions." Wireless Communications and Mobile Computing 2021 (February 5, 2021): 1–10. http://dx.doi.org/10.1155/2021/6625184.

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In order to enhance the communication between sensor networks in the Internet of things (IoT), it is indispensable to establish the semantic connections between sensor ontologies in this field. For this purpose, this paper proposes an up-and-coming sensor ontology integrating technique, which uses debate mechanism (DM) to extract the sensor ontology alignment from various alignments determined by different matchers. In particular, we use the correctness factor of each matcher to determine a correspondence’s global factor, and utilize the support strength and disprove strength in the debating p

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Wu, Zhen Le, Ying Li, Yong Bin Wang, and Yan Jiao Zang. "Continual Word Embedding Based for Matching Lightweight Ontologies." Applied Mechanics and Materials 556-562 (May 2014): 6281–85. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.6281.

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Ontology matching is the task of finding alignments between two different ontologies. It has become the key point of building knowledge base and integrating heterogeneous data. In this paper, a novel ontology matching approach that is based on continual word embedding is proposed. We describe in details how is skip-gram model adapted to capture the semantic of words to learn the word embedding. After computing the name similarity of concepts, similarity flooding algorithm is used to fix the initial similarity. Experiments on Ontology Alignment Evaluation Initiative (OAEI) benchmark without ins

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Souza, Jairo Francisco de, Sean Wolfgand Matsui Siqueira, and Bernardo Nunes. "A framework to aggregate multiple ontology matchers." International Journal of Web Information Systems 16, no. 2 (2019): 151–69. http://dx.doi.org/10.1108/ijwis-05-2019-0023.

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Purpose Although ontology matchers are annually proposed to address different aspects of the semantic heterogeneity problem, finding the most suitable alignment approach is still an issue. This study aims to propose a computational solution for ontology meta-matching (OMM) and a framework designed for developers to make use of alignment techniques in their applications. Design/methodology/approach The framework includes some similarity functions that can be chosen by developers and then, automatically, set weights for each function to obtain better alignments. To evaluate the framework, severa

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Zhu, Hai, Xingsi Xue, Chengcai Jiang, and He Ren. "Multiobjective Sensor Ontology Matching Technique with User Preference Metrics." Wireless Communications and Mobile Computing 2021 (March 16, 2021): 1–9. http://dx.doi.org/10.1155/2021/5594553.

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Due to the problem of data heterogeneity in the semantic sensor networks, the communications among different sensor network applications are seriously hampered. Although sensor ontology is regarded as the state-of-the-art knowledge model for exchanging sensor information, there also exists the heterogeneity problem between different sensor ontologies. Ontology matching is an effective method to deal with the sensor ontology heterogeneity problem, whose kernel technique is the similarity measure. How to integrate different similarity measures to determine the alignment of high quality for the u

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Більше джерел

Дисертації з теми "Ontology alignment evaluation initiative"

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 exami

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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 sup

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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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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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Submitted by Caroline Xavier (caroline.xavier@pucrs.br) on 2017-06-30T17:33:51Z No. of bitstreams: 1 TES_BERNARDO_SEVERO_DE_SOUZA_COMPLETO.pdf: 5113763 bytes, checksum: a24628d427a0a60b3a6ea0c5200d5dfd (MD5) Made available in DSpace on 2017-06-30T17:33:51Z (GMT). No. of bitstreams: 1 TES_BERNARDO_SEVERO_DE_SOUZA_COMPLETO.pdf: 5113763 bytes, checksum: a24628d427a0a60b3a6ea0c5200d5dfd (MD5) Previous issue date: 2017-02-20 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 tecnologi

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Gao, Zhiming. "Reducing the Search Space of Ontology Alignment Using Clustering Techniques." Thesis, Linköpings universitet, Databas och informationsteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-141887.

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With the emerging amount of information available in the internet, how to make full use of this information becomes an urgent issue. One of the solutions is using ontology alignment to aggregate different sources of information in order to get comprehensive and complete information. Scalability is a problem regarding the ontology alignment and it can be settled down by reducing the search space of mapping suggestions. In this paper we propose an automated procedure mainly using different clustering techniques to prune the search space. The main focus of this paper is to evaluate different clus

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Umoh, Emem Koffi. "REFINTO : an ontology-based requirements engineering framework for business-IT alignment in financial services." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/refinto-an-ontologybased-requirements-engineering-framework-for-businessit-alignment-in-financial-services(06738060-cedd-47cb-925e-1b897129bfd0).html.

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Business-IT alignment has been a top research topic for three decades now and consistently ranks high on CIO priorities and concerns. In spite of its seeming advantages, sustainable business-IT alignment remains elusive in practice. This can be attributed to the language and knowledge gaps which impede mutual understanding between business and IT stakeholders. It can also be attributed to the limitations imposed by approaching alignment solely from a strategic perspective. This thesis argues for an ontology-based framework that bridges the language and knowledge gaps through closer interaction

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Thiéblin, Elodie. "Génération automatique d'alignements complexes d'ontologies." Thesis, Toulouse 3, 2019. http://www.theses.fr/2019TOU30135.

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Le web de données liées (LOD) est composé de nombreux entrepôts de données. Ces données sont décrites par différents vocabulaires (ou ontologies). Chaque ontologie a une terminologie et une modélisation propre ce qui les rend hétérogènes. Pour lier et rendre les données du web de données liées interopérables, les alignements d'ontologies établissent des correspondances entre les entités desdites ontologies. Il existe de nombreux systèmes d'alignement qui génèrent des correspondances simples, i.e., ils lient une entité à une autre entité. Toutefois, pour surmonter l'hétérogénéité des ontologies

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Частини книг з теми "Ontology alignment evaluation initiative"

Euzenat, Jérôme, Christian Meilicke, Heiner Stuckenschmidt, Pavel Shvaiko, and Cássia Trojahn. "Ontology Alignment Evaluation Initiative: Six Years of Experience." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22630-4_6.

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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. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93417-4_42.

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Hollink, Laura, Mark van Assem, Shenghui Wang, Antoine Isaac, and Guus Schreiber. "Two Variations on Ontology Alignment Evaluation: Methodological Issues." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-68234-9_30.

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Hariri, Babak Bagheri, and Hassan Abolhassani. "A New Evaluation Method for Ontology Alignment Measures." In The Semantic Web – ASWC 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11836025_25.

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Khiat, Abderrahmane, Gayo Diallo, Beyza Yaman, Ernesto Jiménez-Ruiz, and Moussa Benaissa. "ABOM and ADOM: Arabic Datasets for the Ontology Alignment Evaluation Campaign." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26148-5_37.

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Ivanova, Valentina, Patrick Lambrix, and Johan Åberg. "Requirements for and Evaluation of User Support for Large-Scale Ontology Alignment." In The Semantic Web. Latest Advances and New Domains. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18818-8_1.

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Ivanova, Valentina, Benjamin Bach, Emmanuel Pietriga, and Patrick Lambrix. "Alignment Cubes: Towards Interactive Visual Exploration and Evaluation of Multiple Ontology Alignments." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68288-4_24.

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Isaac, Antoine, Henk Matthezing, Lourens van der Meij, Stefan Schlobach, Shenghui Wang, and Claus Zinn. "Putting Ontology Alignment in Context: Usage Scenarios, Deployment and Evaluation in a Library Case." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-68234-9_31.

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Schadd Frederik C. and Roos Nico. "Anchor-Profiles for Ontology Mapping with Partial Alignments." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2013. https://doi.org/10.3233/978-1-61499-330-8-235.

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Анотація:

Ontology mapping is a crucial step for the facilitation of information exchange between knowledge sources. In the industry this process is often performed semi-automatically, with a domain expert supervising the process. Such an expert can supply a partial alignment, known as anchors, which can be exploited with more elaborate mapping techniques in order to identify the remaining correspondences. To do this we propose a novel approach, referred to as anchor-profiles. For each concept its degree of similarity to each anchor is gathered into a profile for comparison. We evaluated our approach on

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Szwabe Andrzej, Misiorek Pawel, and Walkowiak Przemyslaw. "Tensor-based Relational Learning for Ontology Matching." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2012. https://doi.org/10.3233/978-1-61499-105-2-509.

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In this paper we propose the Tensor-based Reective Relational Learning System (TRRLS) as a first tensor-based approach to decision support in the area of ontology alignment. The system may be seen as realizing a probabilistic inference with regard to the relation representing the &lsquo;semantic equivalence&rsquo; of ontology classes or their properties. Despite the fact that TRRLS is based on the new idea of algebraic modeling of multi-relational data, it provides similar results to the best approaches of the Ontology Alignment Evaluation Initiative (OAEI) competitors to the task of m

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Тези доповідей конференцій з теми "Ontology alignment evaluation initiative"

Abbassi, Faten, Yousra Hlaoui, and Faouzi Ben Charrada. "Machine Learning for Ontology Alignment." In 20th International Conference on Evaluation of Novel Approaches to Software Engineering. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013425700003928.

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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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Kozierkiewicz, Adrianna, and Marcin Pietranik. "Updating Ontology Alignment on the Relation Level based on Ontology Evolution." In 15th International Conference on Evaluation of Novel Approaches to Software Engineering. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009142002410248.

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Khan, Muhammad Humayun, Sadaqat Jan, Imran Khan, and Ibrar Ali Shah. "Evaluation of linguistic similarity measurement techniques for ontology alignment." In 2015 International Conference on Emerging Technologies (ICET). IEEE, 2015. http://dx.doi.org/10.1109/icet.2015.7389207.

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Obraczka, Daniel, and Erhard Rahm. "An Evaluation of Hubness Reduction Methods for Entity Alignment with Knowledge Graph Embeddings." In 13th International Conference on Knowledge Engineering and Ontology Development. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010646400003064.

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Calegari, Daniel, and Andrea Delgado. "Supporting software evolution actions with process mining." In Congresso Ibero-Americano em Engenharia de Software. Sociedade Brasileira de Computação, 2023. http://dx.doi.org/10.5753/cibse.2023.24692.

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There are several forces driving software evolution. One is the business process (BP) misalignment, i.e., when the behavior of the information systems supporting the BPs, or its users, is not aligned with the intended behavior of the BPs identified during the requirement engineering phase. Process Mining (PM) is an essential strategy for BP alignment evaluation. Nevertheless, PM initiatives do not usually focus on connecting BP alignment misfits with concrete software requirements for software evolution. This paper provides initial insights into how PM can support software evolution actions by

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Correia, Anacleto, Mario Simões-Marques, and Pedro Água. "Ontologies for emergency management." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002135.

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In emergency management (EM), different domain vocabularies are used by distinct specialized actors involved in catastrophes’ response. Ontologies enables information sharing among them. This review of ontologies is an exploratory work aimed at collecting references of already proposed ontologies for the realm of EM; a first step for the proposal of a coherent and integrated architecture for EM ontologies. This work conforms to the PRISMA method and was performed by systematically searching several electronic databases for identifying proposed EM ontologies, published between the period of 197

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Al Rashdi, Hamood Nasser, and Khalid Hamdan Al Hatmi. "Unlocking the Potential of Flare Gas Recovery: A Case Study in PDO's Zero-Routine Flaring Initiative." In SPE Annual Technical Conference and Exhibition. SPE, 2024. http://dx.doi.org/10.2118/220781-ms.

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ABSTRACT Flare Gas Recovery is a pivotal strategy in achieving Zero-Routine Flaring objectives in the oil and gas sector. This paper presents key insights from one of Petroleum Development Oman's (PDO) largest flare recovery endeavors, focusing on the design of Atmospheric Flares (AP) and Gas Recovery systems. The project targeted the recovery of associated gas from a major PDO oil production station, encompassing 12 atmospheric tanks. The recovered gas routed through Gas Recovery system, is to be integrated into the available high-pressure grid for reinjection system as a final sink. The chal

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Dascalu, Maria iuliana, Iuliana Marin, Constantanicoleta Bodea, and Mirabela Navlea. "FORMATIVE E-ASSESSMENT TOOL TO FACILITATE THE TRAINING OF PROJECT MANAGEMENT PROFESSIONALS." In eLSE 2015. Carol I National Defence University Publishing House, 2015. http://dx.doi.org/10.12753/2066-026x-15-078.

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Professional certifications sustain lifelong learning through the continuous development of competencies required in a workplace. The current paper proposes a tool to facilitate the training of professionals who want to obtain a project management certification awarded by the International Project Management Association (IPMA). The tool makes the automatic evaluation of answers to open questions and gives feedback, thus providing the users the chance to learn from their own mistakes. The text-based answers are transformed into ontological representations, exploiting several technologies and op

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Kahfie, R. M. "Kalimantan Borderless Operation, Best Practice of Successful Implementation to Maximize Revenue, Minimize Capital Investment and Operating Cost in Pertamina Hulu Indonesia." In Indonesian Petroleum Association - 46th Annual Convention & Exhibition 2022. Indonesian Petroleum Association, 2022. http://dx.doi.org/10.29118/ipa22-f-118.

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PT Pertamina Hulu Indonesia (PHI) is the holding company for Pertamina upstream KKKS in Kalimantan region. There are six production phase blocks operated by Pertamina, three of them are acquisition blocks in 2017 – 2018 period. The company also manage several exploration phase blocks and non-operated working areas in Kalimantan. All Kalimantan producers perform their own production system inherited by previous operator. Alignment has been conducted since the beginning of consolidation of PHI subsidiaries to distribute every strength point and best practice for all producers. Those alignments a

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