Browsing by Author "Kodaz, Halife"

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Citation Count: Yumuşak, Semih...et al (2018). "A Discovery and Analysis Engine for Semantic Web", Companıon Proceedıngs Of The World Wıde Web Conference 2018 , pp. 1497-1505.
A Discovery and Analysis Engine for Semantic Web
(2018) Yumuşak, Semih; Kamilaris, Andreas; Doğdu, Erdoğan; Kodaz, Halife; Uysal, Elif; Aras, Riza Emre
The Semantic Web promotes common data formats and exchange protocols on the web towards better interoperability among systems and machines. Although Semantic Web technologies are being used to semantically annotate data and resources for easier reuse, the ad hoc discovery of these data sources remains an open issue. Popular Semantic Web endpoint repositories such as SPARQLES, Linking Open Data Project (LOD Cloud), and LODStats do not include recently published datasets and are not updated frequently by the publishers. Hence, there is a need for a web-based dynamic search engine that discovers these endpoints and datasets at frequent intervals. To address this need, a novel web meta-crawling method is proposed for discovering Linked Data sources on the Web. We implemented the method in a prototype system named SPARQL Endpoints Discovery (SpEnD). In this paper, we describe the design and implementation of SpEnD, together with an analysis and evaluation of its operation, in comparison to the aforementioned static endpoint repositories in terms of time performance, availability, and size. Findings indicate that SpEnD outperforms existing Linked Data resource discovery methods.
Citation Count: Kasnesis, Panagiotis; Tatlas, Nicolaos-Alexandros; Mitilineos, Stelios A.; et al., "Acoustic Sensor Data Flow for Cultural Heritage Monitoring and Safeguarding", Acoustic Sensor Data Flow for Cultural Heritage Monitoring and Safeguarding, Vol. 19, No. 7, pp. 99-107, (2018).
Classification of Linked Data Sources Using Semantic Scoring
(Ieice-Inst Electronics Information Communications Eng, 2018) Yumuşak, Semih; Doğdu, Erdoğan; Kodaz, Halife; 142876
Cultural heritage sites, apart from being the tangible link to a country's history and culture, actively contribute to the national economy, offering a foundation upon which cultural tourism can develop. This importance at the cultural and economic level, advocates for the need for preservation of cultural heritage sites for the future generations. To this end, advanced monitoring systems harnessing the power of sensors are deployed near the sites to collect data which can fuel systems and processes aimed at protection and preservation. In this paper we present the use of acoustic sensors for safeguarding cultural sites located in rural or urban areas, based on a novel data flow framework. We developed and deployed Wireless Acoustic Sensors Networks that record audio signals, which are transferred to a modular cloud platform to be processed using an efficient deep learning algorithm (f1-score: 0.838) to identify audio sources of interest for each site, taking into account the materials the assets are made of. The extracted information is presented exploiting the designed STORM Audio Signal ontology and then fused with spatiotemporal information using semantic rules. The results of this work give valuable insight to the cultural experts and are publicly available using the Linked Open Data format.
Citation Count: Yumusak, Semih; Aras, Rıza Emre; Uysal, Elif, "SpEnD portal: linked data discovery using SPARQL endpoints", 2017 IEEE International Conference On Big Data (Big Data), pp.200-2202, (2017).
SpEnD portal: linked data discovery using SPARQL endpoints
(IEEE, 2017) Yumuşak, Semih; Aras, Rıza Emre; Uysal, Elif; Doğdu, Erdoğan; Kodaz, Halife; Öztoprak, Kasım
We present the project SpEnD, a complete SPARQL endpoint discovery and analysis portal. In a previous study, the SPARQL endpoint discovery and analysis steps of the SpEnD system were explained in detail. In the SpEnD portal, the SPARQL endpoints are extracted from the web by using web crawling techniques, monitored and analyzed by live querying the endpoints systematically. After many sustainability improvements in the SpEnD project, the SpEnD system is now online as a portal. SpEnD portal currently serves 1487 SPARQL endpoints, out of which 911 endpoints are uniquely found by SpEnD only when compared to the other existing SPARQL endpoint repositories. In this portal, the analytic results and the content information are shared for every SPARQL endpoint. The endpoints stored in the repository are monitored and updated continuously.