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Browsing by Author "Uysal, Elif"

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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.
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    Citation Count: Uysal, Elif...et al. "Sentiment Analysis for the Social Media: A Case Study for Turkish General Elections", ACM SE '17: Proceedings of the SouthEast Conference, April 2017, pp. 215-218.
    Sentiment Analysis for the Social Media: A Case Study for Turkish General Elections
    (2017) Uysal, Elif; Yumuşak, Semih; Öztoprak, Kasım; Doğdu, Erdoğan
    The ideas expressed in social media are not always compliant with natural language rules, and the mood and emotion indicators are mostly highlighted by emoticons and emotion specific keywords. There are language independent emotion keywords (e.g. love, hate, good, bad), besides every language has its own particular emotion specific keywords. These keywords can be used for polarity analysis for a particular sentence. In this study, we first created a Turkish dictionary containing emotion specific keywords. Then, we used this dictionary to detect the polarity of tweets that are collected by querying political keywords right before the Turkish general election in 2015. The tweets were collected based on their relatedness with three main categories: the political leaders, ideologies, and political parties. The polarity of these tweets are analyzed in comparison with the election results.
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    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.