**Preface to "Semantic Web Technology and Recommender Systems"**

Semantic Web (SW) technologies define and analyze Web data linked or not to enable semantic interconnection. Related SW technologies allow data analysts, application designers, and cross-domain experts (linguists, cognitive scientists, machine learning experts and, user interface designers) to uncover, model, and eventually, utilize data semantics to build and work on approaches and ideas that require a deep (human and machine) understanding of the data. Semantics- and data-driven methods in computational intelligence (CI), and especially in recommender systems (RS), analyze single-source big data to identify and select recommendable content for users and applications. Multi-source multi-modal and heterogeneous data are, however, a larger challenge that SW and CI technologies need to face. Such data are of immense value for understanding the users' expectations and redefining the goals for content recommendation. The challenge is that to semantically integrate and combine data from disparate and heterogeneous sources, and for an undefined or unknown original target, they must go through a layer of data understanding, i.e., a semantic data management layer. Advanced semantic data management and knowledge graphs (KG) are potential means of achieving the interlinking of data from original, social, cognitive, and world sources.

In this book (Volume I), 13 papers have been published on different topics of the wide research areas of Semantic Web and Recommender systems. These papers have been carefully selected based on the peer review of several respectful reviewers organized by MDPI's *BDCC* journal. This issue has attracted well-known international research teams, who we would like to thank for their work.

> **Konstantinos Kotis and Dimitris Spiliotopoulos** *Editors*
