Keyword search provides an easy-to-use interface for retrieving information. This thesis contributes to the problems of keyword search over schema-less datasets and semantic trajectories based on RDF. To address the keyword search over schema-less RDF datasets problem, this thesis introduces an algorithm to automatically translate a user-specified keyword-based query K into a SPARQL query Q so that the answers Q returns are also answers for K. The algorithm does not rely on an RDF schema, but it synthesizes SPARQL queries by exploring the similarity between the property domains and ranges, and the class instance sets observed in the RDF dataset. It estimates set similarity based on set synopses, which can be efficiently precomputed in a single pass over the RDF dataset. The thesis includes two sets of experiments with an implementation of the algorithm. The first set of experiments shows that the implementation outperforms a baseline RDF keyword search tool that explores the RDF schema, while the second set of experiments indicate that the implementation performs better than the stateof- the-art TSA+BM25 and TSA+VDP keyword search systems over RDF datasets based on the virtual documents approach. Finally, the thesis also computes the effectiveness of the proposed algorithm using a metric based on the concept of graph relevance. The second problem addressed in this thesis is the keyword search over RDF semantic trajectories problem. Stop-and-move semantic trajectories are segmented trajectories where the stops and moves are semantically enriched with additional data. A query language for semantic trajectory datasets has to include selectors for stops or moves based on their enrichments, and sequence expressions that define how to match the results of selectors with the sequence the semantic trajectory defines. The thesis first proposes a formal framework to define semantic trajectories and introduces stop and move sequence expressions, with well-defined syntax and semantics, which act as an expressive query language for semantic trajectories. Then, it describes a concrete semantic trajectory model in RDF, defines SPARQL stop-and-move sequence expressions, and discusses strategies to compile such expressions into SPARQL queries. Next, the thesis specifies user-friendly keyword search expressions over semantic trajectories based on the use of keywords to specify stop and move queries, and the adoption of terms with predefined semantics to compose sequence expressions. It then shows how to compile such keyword search expressions into SPARQL queries. Finally, it provides a proof-of-concept experiment over a semantic trajectory dataset constructed with user-generated content from Flickr, combined with Wikipedia data.