In recent years, "News Curation Services" that recommend news articles on the Internet to users are getting attention. In this paper, we propose a news curation service that collects and recommends "news articles" that users feel interested by using semantic relationships between terms in the articles. We define "interested" news articles as articles that users have curiosity and serendipity. The semantic relations between events terms are represented by Linked Data. We create News Articles Linked Data (candidates for recommendation to users) and User's preferences Linked Data (users' preferences). In order to recommend news articles to users, we first search common subgraphs between two kinds of Linked Data. The experiment showed that the curiosity score is 3.30 (min:0, max:4), and the serendipity score is 2.93 in our approach, although a baseline method showed the curiosity score is 3.03, and the serendipity score is 2.79. Thus, we confirmed that our approach is more effective than the baseline method.
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