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Recent growth of social media has produced a new market for branding of people and businesses. Facebook provides Facebook Pages (Pages in short) for public figures and businesses (we call entities) to communicate with their fans through a Like button. Because Like counts sometimes reflect the popularity of entities, techniques to increase the Like count can be a matter of interest, and might be known as social media marketing. From an academic perspective, Like counts of Pages depend not only on the popularity of the entity, but also on the popularity of semantically related entities. For example, Lady Gaga's Page has many Likes; her song "Poker Face" does too. We can infer that her next song will acquire many Likes immediately. Important questions are these: How does the Like count of Lady Gaga affect the Like count of her song? Alternatively, how does the Like count of her song constitute some fraction of the Like count of Lady Gaga herself? As described in this paper, we strive to reveal the mutual influences of Like counts among semantically related entities. To measure the influence of related entities, we propose a problem called the Like prediction problem (LPP). It models Like counts of a given entity using information of related entities. The semantic relations among entities, expressed as RDF predicates, are obtained by linking each Page with the most similar DBpedia entity. Using the model learned by support vector regression (SVR) on LPP, we can estimate the Like count of a new entity e.g., Lady Gaga's new song. More importantly, we can analyze which RDF predicates are important to infer Like counts, providing a mutual influence network among entities. Our study comprises three parts: (1) crawling the Pages and their Like counts, (2) linking Pages to DBpedia, and (3) constructing features to solve the LPP. Our study, based on 20 million Pages with 30 billion Likes, is the largest-scale study of Facebook Likes ever reported. This research constitutes a new attempt to integrate unstructured emotional data such as Likes, with Linked data, and to provide new insights for branding with social media.