FEEL: Framework for the Integration of Entity Extraction and Linking Systems

Abstract

Entity extraction and linking (EEL) is an important task of the Semantic Web that allows us to identify real-world objects from text and associate them with their respective resources from a Knowledge Base. Thus, one purpose of the EEL task is to extract knowledge from text. In recent years, several systems have been proposed for addressing such a task in several domains, languages, and knowledge bases. In this sense, some systems that combine the benets of varied EEL systems have been proposed in a kind of ensemble system (like in Machine Learning) to provide better performance and extractions than using a single system. However, there are no clear indications for the selection, conguration, and result integration of EEL systems in an ensemble setting. This paper proposes a framework for the integration of EEL systems by providing recommendations for the selection of systems, the conguration of input parameters, the execution of systems, and the nal integration of results through a ltering strategy that measures the occurrence of entities and detects the overlapping of entities. Based on the proposed framework, we implemented a system using existing EEL systems (through publicly available APIs). The experiments were performed through the GERBIL framework. Our results demonstrate an improvement of the micro/macroprecision and recall of the implemented system regarding the selected individual EEL systems over seven datasets.