Advances in the technologies of smart mobile devices and tiny sensors together with the increase in the number of web resources open up a plethora of new mobile information services where people can acquire and disseminate information at any place and any time. Location-based services (LBS) are characterized by providing users with useful and local information, i.e. information that belongs to a particular domain of interest to the user and can be of use while the user remains in a particular area. In addition, LBS need to take into account the interactions and dependencies between services, user and context for the information filtering and delivery in order to fulfill the needs and constraints of mobile users. We argue that consequently it brings up a series of technical challenges in terms of data semantics and infrastructure, context-awareness and personalization, as well as query formulation and answering etc. They can not be simply extended from existing traditional data management strategies. Instead, they need a new solution. Firstly, we propose a semantic LBS infrastructure on the basis of the modularized ontologies approach. We elaborate a core ontology which is mainly composed of three modules describing the services, users and contexts. The core ontology aims at presenting an abstract view (a model) of all information in LBS. In contrast, data describing the instances (of services user and actual contextual data) are stored in three independent data stores, called the service profiles, user profiles and context profiles. These data are semantically aligned with the concepts in the core ontology through a set of mappings. This approach enables the distributed data sources to be maintained in a autonomous manner, which is well adapted to the high dynamics and mobility of the data sources. Secondly, we separately address the function, features, and our modelling approach of the three major players, i.e. service, context and user in LBS. Then, we define a set of constructs to represent their interactions and inter-dependencies and illustrate how these semantic constructs can contribute to personalized and contextualized query processing. Service classes are organized in a taxonomy, which distinguishes the services by their business functions. This concept hierarchy helps to analyze and reformulate the users' queries. We introduce three new kinds of relationships in the service module to enhance the semantics of interactions and dependencies between services. We identify five key components of contexts in LBS and regard them as a semantic contextual basis for LBS. Component contexts are related together by specific composition relationships that can describe spatio-temporal constraints. A user profile contains personal information about a given user and possibly a set of self-defined rules, which offer hints on what the user likes or dislikes, and what could attract him or her. In the core ontology clustering users with common features can help the cooperative query answering. Each of the three modules of the core ontology is an ontology in itself. They are inter-related by relationships that link concepts belonging to two different modules. The LBS fully benefits from the modularized structure of the core ontology. It allows restricting the search space, as well as facilitating the maintenance of each module. Finally, we studied the query reformulation and processing issues in LBS. How to make the query interface tangible and provide rapid and relevant answers are typical concerns in all information services. Our query format not only fully obeys the "simple, tangible and effective" golden-rules of user-interface design, but also satisfies the needs of domain-independent interface and emphasizes the importance of spatio-temporal constraints in LBS. With pre-defined spatio-temporal operators, users can easily specify in their queries the spatio-temporal availability they need for the services they are looking for. This allows eliminating most of irrelevant answers that are usually generated by keyword-based approaches. Constraints in the various dimensions (what, when, where and what-else) can be expressed by a conjunctive query, and then be smoothly translated to RDF-patterns. We illustrate our query answering strategy by using the SPARQL syntax, and explain how the relaxation can be done with rules specified in the query relaxation profile.