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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]

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]

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]