Semantic Enrichment Across Language: A Case Study of Czech Bibliographic Databases

This paper deals with semantic enrichment of textual resources by means of automatically generated named entity recognizers-linkers and advanced indexing and searching mechanisms that can be integrated into various information retrieval and information extraction systems. It introduces a new system transforming Wikipedia and other available sources into task-specific knowledge bases and employs contextual information to build state-of-the-art entity disambiguation components. Although some components are language-dependent (for example, that responsible for the morphology analysis or the semantic role identification), they can be easily replaced by existing tools providing specific functions. As a case study, we demonstrate an instantiation of the system for the task of semantic annotation of Czech bibliographic databases in the context of the CPK project. We particularly stress the role of problem-specific knowledge sources that can be easily integrated into our system and play a key role in the success of the tool in real applications.