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The purpose of this paper is to propose an automatic method to generate semantic annotations of football transfer in the news. The current automatic news integration systems on the Web are constantly faced with the challenge of diversity, heterogeneity of sources. The approaches for information representation and storage based on syntax have some certain limitations in news searching, sorting, organizing and linking it appropriately. The models of semantic representation are promising to be the key to solving these problems.

The approach of the author leverages Semantic Web technologies to improve the performance of detection of hidden annotations in the news. The paper proposes an automatic method to generate semantic annotations based on named entity recognition and rule-based information extraction. The authors have built a domain ontology and knowledge base integrated with the knowledge and information management (KIM) platform to implement the former task (named entity recognition). The semantic extraction rules are constructed based on defined language models and the developed ontology.

The proposed method is implemented as a part of the sport news semantic annotations-generating prototype BKAnnotation. This component is a part of the sport integration system based on Web Semantics BKSport. The semantic annotations generated are used for improving features of news searching – sorting – association. The experiments on the news data from SkySport (2014) channel showed positive results. The precisions achieved in both cases, with and without integration of the pronoun recognition method, are both over 80 per cent. In particular, the latter helps increase the recall value to around 10 per cent.

This is one of the initial proposals in automatic creation of semantic data about news, football news in particular and sport news in general. The combination of ontology, knowledge base and patterns of language model allows detection of not only entities with corresponding types but also semantic triples. At the same time, the authors propose a pronoun recognition method using extraction rules to improve the relation recognition process.

The growth of online data and services on the Web have have led to the Web become an indispensable tool for the tourist industry. It is not denied that various approaches bring benefits for visitors, in supporting their searching for tourist attractions, such as interesting places for the visit, eating or staying. However, like a coin has two sides, too much information would present a difficulty for people when planning their journeys. Generally, tourists usually have problems when trying to find satisfactory accommodation if references to nearby restaurants, sights or event locations are lacking. In addition, travelers suffer from the information overload when they look for information about potential destinations, events and related services. Providing relevant and up‐to‐date information for the tourists with different personal interests is still a challenging task for the tourist guide information systems. The purpose of this paper is to propose a semantic approach for searching tourist information and generating travel itinerary.

The paper focus on introducing an ontological model for representation of tourist resources as well as traveler's profile. Based on this model, smart user interfaces facilitating the semantic search have been implemented in the mobile travel guide application. In addition, the authors propose an algorithm for generating travel itinerary which combines semantic matching with ant colony optimization technique.

The Semantic Tourist informAtion Access and Recommending (STAAR) system, which has been implemented to promote the travel activity in Hanoi, reveals the advantages of the semantic approach in the development of smart application with ontology‐based, user‐friendly interface and high precision information search features. An experiment was conducted to show that the proposed algorithm generates travel itinerary relevant to both criteria of itinerary length and user interest.

This paper presents a novel algorithm for planning a travel itinerary that is optimized on the length as well as semantically matching the user interests.