17 results on '"Kalender, Murat"'
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2. A greedy gradient-simulated annealing selection hyper-heuristic
- Author
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Kalender, Murat, Kheiri, Ahmed, Özcan, Ender, and Burke, Edmund K.
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- 2013
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3. Iloprost inhibits fracture repair in rats
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Doğan, Ali, Duygun, Fatih, Kalender, Murat A., Bayram, Irfan, and Sungur, Ibrahim
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- 2014
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4. NEW RAILWAY TUNNELS AND THEIR CONSTRUCTION METHOD AS WELL AS THEIR IMPACT TO TRAIN OPERATION.
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Kalender, Murat and Vojtek, Martin
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RAILROAD tunnels ,TUNNEL design & construction ,EXCAVATION ,TRANSPORTATION corridors - Published
- 2021
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5. A Modified Rotation Flap Design: The S-Flap
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ISIK, DAGHAN, GUNER, SAVAS, KALENDER, MURAT A., ISIK, YASEMIN, and ATIK, BEKIR
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- 2011
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6. Seprafilm® interposition for preventing adhesion formation after tenolysis: An experimental study on the chicken flexor tendons
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Karakurum, Gunhan, Buyukbebeci, Orhan, Kalender, Murat, and Gulec, Akif
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- 2003
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7. Knowledge graph based visual interpretation of web content
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Kalender, Murat, Korkmaz, Emin Erkan, and Bilgisayar Mühendisliği Anabilim Dalı
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Artificial intelligence ,Information display ,Information access system ,Computer Engineering and Computer Science and Control ,Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol - Abstract
İnternetin popülerleşmesi ile internet içeriğine yeni nesil televizyonlar üzerinden erişilmektedir. Ancak internet içeriğinin geniş ekran televizyonlar için tasarlanmamış olmalarından dolayı kullanıcılar internet içeriğine erişmek için televizyonlarını tercih etmemektedir. Kullanıcıların büyük bölümü hala televizyon yerine internet içeriğine erişmek için kişisel bilgisayarlarını kullanmaktadır. Bu tez kapsamında, TV'lerde internet içerik tüketimi ve kullanılabilirlik sorunlarını aşmak için Videolization isimli görselleştirme sistemi geliştirilmiştir. Videolization sistemi Türkçe veya İngilizce internet içeriğinin Anlamsal Ağlar teknolojilerini kullanarak otomatik olarak görselleştirilmesini hedeflemektedir. Sistem internet içeriğinden çıkarımı yapılan anlamsal varlıkların görsel ve anlamsal bilgilerini kullanarak görsel sunum yapabilmektedir. Tez çıktısı internet içeriğinin görsel yorumundan, Bilgisayar Grafiği teknolojilerini kullanarak otomatik video üretilebilmektedir. Bu nedenle, bu çalışmanın ana odak noktası Videolization sisteminin arkasındaki varlık bağlama (entity linking) sistemidir. İngilizce için birçok başarılı varlık bağlama uygulamaları bulunmaktadır. Fakat Türkçe dili için kamuya açık kullanılabilir bir varlık bağlama sistemi bulunmamaktadır. Türkçe içerikleri görselleştirmek için bu tez kapsamında Thinker isimli Türkçe varlık bağlama sistemi geliştirilmiştir. Önerilen Thinker sisteminin başarımını ölçmek için deneyler yapılmıştır. Deneylerde Thinker sistemi belirsizlik giderme performansı açısından daha önceki yöntemlere göre çok daha iyi performans göstermiştir. Web content nowadays can also be accessed through new generation Internet Connected TVs. However these products failed to change users' behavior for consuming online content. Users still prefer their personal computers instead of their TVs when they access Web content. Certainly, most of the online content is still designed to be presented with a personal computer or mobile devices. In order to overcome usability problem of Web content consumption on TVs, this thesis presents Videolization, a knowledge graph based visual interpretation system that automatically interprets visually given Turkish or English textual Web content by using Semantic Web based technologies. The system visualizes textual Web content by utilizing visual representations of extracted entities from the content. The generated visual interpretation of a given Web content could be automatically converted into a video by using Computer Graphics based technologies. Therefore the main focus of this study is entity linking, which is the most critical task in Content Curation process. Entity linking is the generation of assignments from knowledge graph entities to documents. In contrast to many successful applications for English, there is currently no publicly available entity linking system for Turkish. In order to visualize Turkish content, this thesis presents Thinker, a novel entity linking system for linking Turkish text content with entities defined in the Turkish dictionary or Turkish Wikipedia. The effectiveness of Videolization is validated empirically over opinion surveys and the effectiveness of Thinker is validated empirically over generated data sets. The experimental results show that Thinker greatly outperforms previous methods in terms of disambiguation performance. 139
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- 2016
8. A New Method Based on Tree Simplification and Schema Matching for Automatic Web Result Extraction and Matching
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Ali, Minnet, Mohammed, Baker, YILDIZ, OKTAY, Yaşar, Gözüdeli, KARACAN, HACER, AKCAYOL, MUHAMMET ALİ, Özcan, Özay, and Kalender, Murat
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- 2015
9. Extraction of Automatic Search Result Records Using Content Density Algorithm Based on Node Similarity
- Author
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KARACAN, HACER, AKCAYOL, MUHAMMET ALİ, Özcan, Özay, YILDIZ, OKTAY, Ali, Minnet, Mohammed, Baker, Kalender, Murat, and Yaşar, Gözüdeli
- Published
- 2014
10. THINKER - Entity Linking System for Turkish Language.
- Author
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Kalender, Murat and Korkmaz, Emin Erkan
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OPENURL (Uniform resource locator) , *UNIFORM Resource Locators , *DOCUMENT type definitions , *COGNITIVE computing , *ARTIFICIAL intelligence - Abstract
Entity linking is one of the problems to be handled in order to process natural language and to enrich the existing unstructured text with metadata. The generation of assignments between knowledge base entities and lexical units is called entity linking. Although a number of systems have been proposed for linking entity mentions in various languages, there is currently no publicly available entity linking system specific to the Turkish language. This paper presents a novel entity linking system—THINKER - for linking Turkish content with entities defined in the Turkish dictionary (tdk.gov.tr) or Turkish Wikipedia (tr.wikipedia.org). Specifically, we first propose a novel machine learning based entity detection algorithm for the Turkish language. Then, we propose a collective disambiguation algorithm which utilizes a set of metrics for the linking task and, which is optimized using a genetic algorithm. The effectiveness of THINKER is validated empirically over generated data sets. The experimental results show that THINKER outperformed the state-of-the-art cross-lingual and multilingual entity linking systems in the literature. High entity linking performance (74.81 percent F1 score) is achieved by extending previous methods with some features specific to Turkish language and by developing a novel method that can learn better representations of entity embeddings. [ABSTRACT FROM PUBLISHER]
- Published
- 2018
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11. Videolization: knowledge graph based automated video generation from web content.
- Author
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Kalender, Murat, Eren, M. Tolga, Wu, Zonghuan, Cirakman, Ozgun, Kutluk, Sezer, Gultekin, Gunay, and Korkmaz, Emin Erkan
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GRAPH theory ,INTERNET content ,COMPUTER users ,STREAMING video & television ,SEMANTIC Web - Abstract
Web content nowadays can also be accessed through new generation of Internet connected TVs. However, these products failed to change users' behavior when consuming online content. Users still prefer personal computers to access Web content. Certainly, most of the online content is still designed to be accessed by personal computers or mobile devices. In order to overcome the usability problem of Web content consumption on TVs, this paper presents a knowledge graph based video generation system that automatically converts textual Web content into videos using semantic Web and computer graphics based technologies. As a use case, Wikipedia articles are automatically converted into videos. The effectiveness of the proposed system is validated empirically via opinion surveys. Fifty percent of survey users indicated that they found generated videos enjoyable and 42 % of them indicated that they would like to use our system to consume Web content on their TVs. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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12. Automated semantic tagging of text documents
- Author
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Kalender, Murat, Üsküdarlı, Suzan, and Bilgisayar Mühendisliği Anabilim Dalı
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Artificial intelligence ,Natural language processing ,Information display ,Information access ,Computer Engineering and Computer Science and Control ,Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol - Abstract
Belgelerin katlanarak büyümesi mevcut arama ve içerik yönetim teknolojilerini zorlamaktadır. Bu sorunu azaltmak için bir yaklaşım belgeların kullanıcılar tarafından seçilen belgelerde geçen önemli kelimelerle etiketlenmesidir. Ancak bu yaklaşımın etiketleri sınırlıdır çünkü etiketler i) bağlam ve form özgür, ii) belgeleri tanımlamadan farklı amaçlarda kullanılabiliyor iii) genellikle belirsiz kalıyorlar. Etiketleme gönüllü bir eylem olduğundan dolayı çok sayıda belge etiketlenmemektedir. Son olarak, belgelere atanan etiketlerin yorumlanmasıda ayrı bir zorluktur.Anlamsal web kaynakları ve teknolojileri, bu zorlukları aşmak ve otomatik olarak semantik etiketler oluşturmak için kullanılabilir. Semantik etiketler belgelerin içeriğini daha iyi ifade etme dışında, daha iyi arama sonuçları elde etmemizi sağlamaktadır. Ontoloji kapsamı, terimlerin ontolojide doğru kavramlarla ilişkilendirilmesi ve anlamsal etiketlerin ağırlıklarının belirlenmesi anlamsal etiketleme sistemlerinde çözülmesi gereken önemli sorunlardır.İngilizce için önde gelen ontoloji olan WordNet başarıyla anlamsal etiketleme için kullanılmaktadır. Ancak bu yaklaşım yeni kavramlar içeren belgeleri etiketlemede yetersiz kalmaktadır.Bu çalışma belgeler için otomatik olarak anlamsal etiketler oluşturan bir sistem önermektedir. Bu amaçla, ilk katkımız ontolojik bilgi tabanı platformu olan UNIpedia' dır. UNIpedia çağdaş referansları içeren bir bilgi tabanı sağlamaktır. Burada, çağdaş kelimesi web de geçen güncel kelimeler bağlamında kullanılmaktadır. UNIpedia çeşitli ontolojik bilgi tabanlarını WordNet kavramlarıyla ilişkilendirmektedir. Güncel ve güvenilir bilgi içeren Wikipedia ve OpenCyc bilgi tabanları WordNet kavramları ile eşleştirilmiştir. Bilgi tabanlarını ilişkilendirmek için kavramların ontolojik ve istatistiksel özelliklerini kullanan kural tabanlı sezgiseller kullanılmıştır.Konuşma dillerinin çok anlamlılığından dolayı UNIpedia' da tanımlı terimler birden fazla anlam içerebilmektedir. Bu çok anlamlı kelimeler dökümanın içeriğine göre farklı anlamlar alabilmektedirler. Belgede geçen terimler çok anlamlıysa doğrudan UNIpedia kavramlarıyla ilişkilendirilememektedir. Terimlerin doğru anlamlarını bulabilmek için otomatik anlamsal etiketleme sistemi olan Semantic TagPrint geliştirilmiştir. Bu eserin ikinci katkısı olanSemantic TagPrint anlam belirginleştirmesi için doğrusal zamanda çalışan kelime zincirlerini kullanmaktadır. Buna ek olarak, Semantik TagPrint belgenin içeriğini açıklayan anlamsal etiketlerin önemini belirler ve önerir. Anlamsal etiketleme ve önerme algoritmaları UNIpedia da tanımlı olan kavramların istatistiksel ve anlamsal özelliklerini kullanmaktadır. Semantik TagPrint sisteminin potansiyel yararlarını göstermek için Anlamsal Bilgi Yönetimi Aracı (SKMT) uygulanması tasarlanmış ve geliştirilmiştir. Bu eserin üçüncü katkısı olan SKMT semantik belgeleri etiketlemek için Semantik TagPrint için erişilebilir bir platform sunar ve semantik arama yapar. The exponential growth of documents is challenging the existing search and content management technology. An approach for mitigating this issue is user-generated tags, a simple method by which users associate keywords to documents. However, the improvements, from this approach are limited because tags are i) free from context and form, ii) used for purposes other than description, and iii) often remain ambiguous. Since user tagging is a voluntary action, many documents remain untagged. Finally, the interpretation of the tags associated with documents also remains a challenge.To overcome these challenges, semantic web resources and technologies can be utilized to automatically generate semantic tags. Semantic tags not only reflect document content more accurately, they also enable better search results. Ontology coverage, word sense disambiguation and weighting significant ontological entities within a context are key challenges in semantic tagging systems.The leading ontology for the English language, Wordnet, has been successfully used for semantic tagging. However, this approach falls short in tagging documents that refer to new concepts and instances.The main focus of this work is automatically generating semantic tags for arbitrary documents. For this purpose, the first contribution is an ontological knowledge base plat- form called UNIpedia. UNIpedia aims to provide a knowledge base with contemporary references. Here, contemporary should be understood as in line with web pace. UNIpedia maps various ontological knowledge bases to WordNet concepts. The Wikipedia and OpenCyc knowledge bases, which are known to contain up to date instances and reliable metadata about them, were mapped to WordNet. A rule based heuristics, which uses the ontological and statistical features of concepts and instances, is introduced for the mapping process.UNIpedia terms may have several senses because of the natural language ambiguity. These so called polysemous terms get different meanings according to the context. A term passing in a document cannot be mapped to an UNIpedia concept or instance directly, if the term is polysemous. In order to identify the correct sense of the polysemous terms, an automated semantic tagging system called Semantic TagPrint was devised. Semantic TagPrint is the second contribution of this work that uses a linear time lexical chaining Word Sense Disambiguation algorithm for semantic annotation. In addition, Semantic TagPrint weighs and recommends semantic tags which describe the content of a document well. The semantic annotation and semantic tag weighting algorithms use both semantic and statistical features of UNIpedia.The potential benefits of Semantic TagPrint are demonstrated by the design and implementation of the Semantic Knowledge Management Tool (SKMT). SKMT is the third contribution of this work that provides a user accessible platform for Semantic TagPrint to semantically tag documents, and performs semantic searches. 122
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- 2010
13. Turkish entity discovery with word embeddings.
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KALENDER, Murat and KORKMAZ, Emin Erkan
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ARTIFICIAL neural networks , *NATURAL language processing , *THEORY of knowledge , *EMBEDDINGS (Mathematics) , *INFORMATION storage & retrieval systems - Abstract
Entity-linking systems link noun phrase mentions in a text to their corresponding knowledge base entities in order to enrich a text with metadata. Wikipedia is a popular and comprehensive knowledge base that is widely used in entity-linking systems. However, long-tail entities are not popular enough to have their own Wikipedia articles. Therefore, a knowledge base created by using Wikipedia entities would be limited to only popular entities. In order to overcome the knowledge base coverage limitation of Wikipedia-based entity-linking systems, this paper presents an entity-discovery system that can detect semantic types of entities that are not defined in Wikipedia. The effectiveness of the proposed system was validated empirically through the use of generated data sets for the Turkish language. The experimental results show that, in terms of accuracy, our system performs competitively in comparison to the previous methods in the literature. Its high performance is achieved through a method that learns word embeddings for candidate entities. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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14. A greedy gradient-simulated annealing hyper-heuristic for a curriculum-based course timetabling problem.
- Author
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Kalender, Murat, Kheiri, Ahmed, Ozcan, Ender, and Burke, Edmund K.
- Abstract
The course timetabling problem is a well known constraint optimization problem which has been of interest to researchers as well as practitioners. Due to the NP-hard nature of the problem, the traditional exact approaches might fail to find a solution even for a given instance. Hyper-heuristics which search the space of heuristics for high quality solutions are alternative methods that have been increasingly used in solving such problems. In this study, a curriculum based course timetabling problem at Yeditepe University is described. An improvement oriented heuristic selection strategy combined with a simulated annealing move acceptance as a hyper-heuristic utilizing a set of low level constraint oriented neighbourhood heuristics is investigated for solving this problem. The proposed hyper-heuristic was initially developed to handle a variety of problems in a particular domain with different properties considering the nature of the low level heuristics. On the other hand, a goal of hyper-heuristic development is to build methods which are general. Hence, the proposed hyper-heuristic is applied to six other problem domains and its performance is compared to different state-of-the-art hyper-heuristics to test its level of generality. The empirical results show that the proposed method is sufficiently general and powerful. [ABSTRACT FROM PUBLISHER]
- Published
- 2012
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15. SKMT: A Semantic Knowledge Management Tool for Content Tagging, Search and Management.
- Author
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Kalender, Murat and Dang, Jiangbo
- Abstract
With an ever increasing amount of content, we heavily rely on search engine to locate documents. Manual content Tagging can improve search results by allowing search engines to exploit tags generated by content authors. Manual tagging method introduces issues of bias and inconsistency from ad-hoc tagging, and increase burden to the authors. In this paper, we first introduce a semantic tagging engine that automatically generates semantic tags for the given documents. This tagging engine provides the ground for realizing semantic search, based on meanings of search terms and content tags. Then we present a Semantic Knowledge Management Tool (SKMT) as a semantic search and knowledge management platform to search, analyze and manage enterprise content. SKMT can scan different content sources and generate indexes of semantic keywords. Its user-friendly interface allows users to manage various data sources, search, explore and visualize search results at semantic level. Higher precision of semantic search and semantic data visualization are also demonstrated with examples as benefits of SKMT. [ABSTRACT FROM PUBLISHER]
- Published
- 2012
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16. Effect of Using Different Kinds and Ratios of Vegetable Oils on Ice Cream Quality Characteristics.
- Author
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Güven, Mehmet, Kalender, Murat, and Taşpinar, Tansu
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ICE cream, ices, etc. ,HAZELNUTS ,MILKFAT ,VEGETABLE oils ,OLIVE oil - Abstract
The aim of this study was to develop ice cream products using different types of oils, a sensory ballot to focus on the textural attributes of new ice cream products, evaluate physicochemical properties of these products and physical measurements. Milkfat, hazelnut oil and olive oil were mixed at different concentrations for a total of 12% fat. Control sample contains 12% milk fat while the other formulations contain different proportion of milk fat, hazelnut oil and olive oil as the fat content. The combination of the different proportion of milk fat, hazelnut oil and olive oil are given as % milk fat, % hazelnut oil and % olive oil respectively; 12:0:0, 0:12:0, 0:0:12, 6:6:0, 6:0:6, 0:6:6, 4:4:4. The pH, free acidity, total solid ingredient, b* value and volume increase rate were statistically significant (p < 0.05). Sensory analysis results showed that: samples were 50% hazelnut oil-50% olive oil had the highest color and appearance scores. On the other hand, the highest score in body and texture scores were belongs to the sample of used 50% milk fat-50% hazelnut oil and 50% milk fat-50% olive oil, 50% milk fat-50% olive oil the most preferred ones in total quality criterions. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
17. A new method in tendon repair: angular technique of interlocking (ATIK).
- Author
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Atik B, Tan O, Dogan A, Kalender M, Tekes L, Korkmaz M, and Uslu M
- Subjects
- Biomechanical Phenomena, Humans, Sutures, Plastic Surgery Procedures methods, Suture Techniques, Trigger Finger Disorder surgery
- Abstract
Background: The risk of adhesion following flexor tendon repair, despite provision of rehabilitation by mobilization of the tendon with passive exercises without the risk of rupture, is not negligible. Active mobilization of tendons has recently been more frequently recommended to prevent adhesions of tendons. The tendon repair zone, which should withstand active traction forces, should maintain its strength until complete recovery of the tendon. For this purpose, a new treatment method named angular technique of interlocking (ATIK) has been developed. This method was compared with the Modified Kessler method, in vivo and in vitro., Materials and Methods: In four groups, each consisting of 10 chickens, severed flexor tendons repaired with the Modified Kessler and ATIK techniques were compared for biomechanical properties., Results: Although there were no differences between these techniques in vitro, this new technique's superiority was statistically significant in in vivo studies., Conclusions: The second and third postoperative weeks are periods during which the number of fibroblasts and the amount of collagen are the highest. In these periods, edema resolves and sutures begin to loosen. In this situation, the force withstanding the active movements is the support of the suture materials and the degree of recovery of the tendon. Following this recommended suture technique and active movements, the healing potential of the tendon increases and the risk of tendon rupture owing to decrease in the force exerted per unit area decreases.
- Published
- 2008
- Full Text
- View/download PDF
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