The Web of Things (WoT) takes place in a variety of application domains (e.g. homes, enterprises, industry, healthcare, city, agriculture...). It builds a Web-based uniform layer on top of the Internet of Things (IoT) to overcome the heterogeneity of protocols present in the IoT networks. WoT applications provide added value by combining access to connected objects and external data sources, as well as standard-based reasoning (RDF-S, OWL 2) to allow for interpretation and manipulation of gathered data as contextual information. Contextual information is then exploited to allow these applications to adapt their components to changes in their environment. Yet, contextual adaptation is a major challenge for theWoT. Existing adaptation solutions are either tightly coupled with their application domains (as they rely on domain-specific context models) or offered as standalone software components that hardly fit inWeb-based and semantic architectures. This leads to integration, performance and maintainability problems. In this thesis, we propose a multi-purpose contextual adaptation solution for WoT applications that addresses usability, flexibility, relevance, and performance issues in such applications. Our work is based on a smart agriculture scenario running inside the avatar-based platformASAWoO. First,we provide a generic context meta-model to build standard, interoperable et reusable context models. Second, we present a context lifecycle and a contextual adaptation workflow that provide parallel raw data semantization and contextualization at runtime, using heterogeneous sources (expert knowledge, device documentation, sensors,Web services, etc.). Third, we present a situation-driven adaptation rule design and generation at design time that eases experts and WoT application designers’ work. Fourth, we provide two optimizations of contextual reasoning for theWeb: the first adapts the location of reasoning tasks depending on the context, and the second improves incremental maintenance of contextual information