Browsing by Author "Öztürk, Ö"

Now showing 1 - 7 of 7
  • No Thumbnail Available
    Item
    The extension-based inference algorithm for pD*
    Öztürk, Ö; Özacar, T; Ünalir, MO
    In this work, we present a scalable rule-based reasoning algorithm for the OWL pD* language. This algorithm uses partial materialization and a syntactic ontology transformation (the extension-based knowledge model) to provide a fast inference. Because the materialized part of the ontology does not contain assertional data, the time consumed by the process, and the number of inferred triples, remain fixed with varying amounts of assertional data. The algorithm uses database reasoning and a query rewriting technique to handle the remaining inference. The extension-based knowledge model and the database reasoning prevent the expected decreases in query performances, which are the natural result of online reasoning during query time. This work also evaluates the efficiency of the proposed method by conducting experiments using LUBM and UOBM benchmarks. (C) 2011 Published by Elsevier B.V.
  • No Thumbnail Available
    Item
    Hermos: An annotated image dataset for visual detection of grape leaf diseases
    Özacar, T; Öztürk, Ö; Savas, NG
    Powdery mildew, dead arm and vineyard downy mildew diseases are frequently seen in the vineyards in the Gediz River Basin, West Anatolia of Turkey. These diseases can be detected early using artificial intelligence (AI)-based systems that can contribute to crop yields and also reduce the labour of the farmer and the amount of pesticides used. This article presents a dataset - namely, Hermos - for use in such AI-based systems. Hermos contains four classes of grape leaf images: leaves with powdery mildew, leaves with dead arm, leaves with downy mildew and healthy leaves. We have currently 492 images and 13,913 labels in the dataset. We have published Hermos in the Linked Open Data (LOD) cloud in order to make it easier for consumers to access, process and manipulate the data.
  • No Thumbnail Available
    Item
    Onyx: A new Canvas-based tool for visualizing biomedical and health ontologies
    Öztürk, Ö; Açikgöz, HG
    Ontologies provide formal, machine-readable, and human-interpretable representations of domain knowledge. Therefore, ontologies have come into question with the development of Semantic Web technologies. People who want to use ontologies need an understanding of the ontology, but this understanding is very difficult to attain if the ontology user lacks the background knowledge necessary to comprehend the ontology or if the ontology is very large. Thus, software tools that facilitate the understanding of ontologies are needed. Ontology visualization is an important research area because visualization can help in the development, exploration, verification, and comprehension of ontologies. This paper introduces the design of a new ontology visualization tool, which differs from traditional visualization tools by providing important metrics and analytics about ontology concepts and warning the ontology developer about potential ontology design errors. The tool, called Onyx, also has advantages in terms of speed and readability. Thus, Onyx offers a suitable environment for the representation of large ontologies, especially those used in biomedical and health information systems and those that contain many terms. It is clear that these additional functionalities will increase the value of traditional ontology visualization tools during ontology exploration and evaluation.
  • No Thumbnail Available
    Item
    A Motion Sensing Based Assistive System Design for Ice Skating Learners
    Öztürk, Ö; Kahramanli, MM
    In this paper, an ergonomic, simple to install and portable system design is presented for those who are new to learning ice skating. The system will support users to apply basic skating techniques correctly. The system is designed to be open, flexible and modular. Therefore this system can be easily extended for more advanced techniques or other sports, requiring body coordination of the system. The main components of the system are inertial measurement units. The inertial measurement unit in the system determines the direction vector of each part of the body. The directions computed by data collected from these sensors form the basis for directing the user in the system. Once the headphones and sensor modules have been installed, the user is assisted with short, motivating and clearly understandable commands.
  • No Thumbnail Available
    Item
    A Methodology for end user programming of ROS-based service robots using jigsaw metaphor and ontologies
    Öztürk, Ö
    This study proposes a methodology to build an interface that will enable end users to interact with ROS-based service robots. The proposed methodology enables building an easy-to-use interface using the jigsaw metaphor. The methodology exploits ontologies to maximize the data sharing and integration among the users of the system. This methodology is verified on a cocktail robot as a case study and an end user-based survey is conducted to evaluate the built interface. The case study proved the feasibility of the methodology. User-based evaluation results provided positive feedback regarding the usability and preferability of the interface built with the proposed methodology.
  • No Thumbnail Available
    Item
    A case study for block-based linked data generation: Recipes as jigsaw puzzles
    Öztürk, Ö; Özacar, T
    This article is a proof-of-concept case study to evaluate the functionality of a block metaphor-based linked data generator. In this work, we chose to produce linked data repository of recipes, which provide a medium for people to share their regional and healthy recipes with the masses. However, the same approach can also be adapted easily to other domains. Therefore, the applicability of our approach extends well beyond the food domain that we are considering in this article. As a medium for information sharing and understanding between heterogeneous systems, ontologies will play an important role in the realisation of the Internet of things (IoT) vision. Therefore, an ontology-based recipe repository would also be one of the basic blocks of a smart kitchen environment. However, building ontologies is a challenging task, especially for users who are not conversant in the ontology building languages. This article proposes an approach that can be used even by non-experts and facilitates the sharing and searching of recipe data. In our case, we exploit the features of the block paradigm to publish recipes in Linked Data format. In this way, users do not have to know the OWL (Web Ontology Language) syntax and the text input is kept minimal. As far as we know, this article is the first study that produces linked data using Blockly in the literature. We also conducted a user-based evaluation of the proposed approach using the System Usability Scale (SUS) questionnaire.
  • No Thumbnail Available
    Item
    Using data mining for makam recognition in Turkish traditional art music
    Abidin, D; Öztürk, Ö; Öztürk, TÖ
    Computer science has become a popular reseach topic in musicology with the transfer of musical works to digital media. Musical works are used as data in scientific researches and the computational music field is developing rapidly with the work done in this area. Representing Western musical works in symbolic form is easier than Turkish musical works and as a result most of the studies in this area focus on Western Music. However, in the last few years there are some interesting studies on using data mining, machine learning and classification techniques on Turkish maqam system. This study represents an experimental work that uses machine learning to recognize the maqams of the 1261 Turkish musical works. These musical works are assumed to be obtained by note recognition from audio files. We developed a software for using the data in MusicXML format with machine learning. This software also adds four different derived variables to the original data set in order to incerase the performance of the machine learning process. As a result of the study, we observed the perfomance of the Random Forest algorithm as 89.7%.