Abstract-The quantity of data published on the Web according to principles of Linked Data is increasing intensely. However, this data is still largely limited to be used up by domain professionals and users who understand Linked Data technologies. Therefore, it is essential to develop tools to enhance intuitive perceptions of Linked Data for lay users. The features of Linked Data point to various challenges for an easy-to-use data presentation. In this paper, Semantic Web and Linked Data technologies are overviewed, challenges to the presentation of Linked Data is stated, and LOD Explorer is presented with the aim of delivering a simple application to discover triplestore resources. Furthermore, to hide the technical challenges behind Linked Data and provide both specialist and non-specialist users, an interactive and effective way to explore RDF resources.
Abstract-The ever-increasing amount of data available on the web is the result of the simplicity of sharing data over the current Web. To retrieve relevant information efficiently from this huge dataspace, a sophisticated search technology, which is further complicated due to the various data formats used, is crucial. Semantic Web (SW) technology has a prominent role in search engines to alleviate this issue by providing a way to understand the contextual meaning of data so as to retrieve relevant, high-quality results. An Exploratory Search System (ESS), is a featured data looking and search approach which helps searchers learn and explore their unclear topics and seeking goals through a set of actions. To retrieve high-quality retrievals for ESSs, Linked Open Data (LOD) is the optimal choice. In this paper, SW technology is reviewed, an overview of the search strategies is provided, and followed by a survey of the state of the art Linked Data Browsers (LDBs) and ESSs based on LOD. Finally, each of the LDBs and ESSs is compared with respect to several features such as algorithms, data presentations, and explanations.
Named Entity Recognition (NER) from text constitutes the first step in many text mining applications. The most important preliminary step for NER systems using machine learning approaches is tokenization where raw text is segmented into tokens. This study proposes an enhanced rule based tokenizer, ChemTok, which utilizes rules extracted mainly from the train data set. The main novelty of ChemTok is the use of the extracted rules in order to merge the tokens split in the previous steps, thus producing longer and more discriminative tokens. ChemTok is compared to the tokenization methods utilized by ChemSpot and tmChem. Support Vector Machines and Conditional Random Fields are employed as the learning algorithms. The experimental results show that the classifiers trained on the output of ChemTok outperforms all classifiers trained on the output of the other two tokenizers in terms of classification performance, and the number of incorrectly segmented entities.
Classifier ensembling approach is considered for biomedical named entity recognition task. A vote-based classifier selection scheme having an intermediate level of search complexity between static classifier selection and real-valued and class-dependent weighting approaches is developed. Assuming that the reliability of the predictions of each classifier differs among classes, the proposed approach is based on selection of the classifiers by taking into account their individual votes. A wide set of classifiers, each based on a different set of features and modeling parameter setting are generated for this purpose. A genetic algorithm is developed so as to label the predictions of these classifiers as reliable or not. During testing, the votes that are labeled as being reliable are combined using weighted majority voting. The classifier ensemble formed by the proposed scheme surpasses the full object F-score of the best individual classifier by 2.75% and it is the highest score achieved on the data set considered.
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