PUB - Publikationen an der Universität Bielefeld

Named Entity Recognition (NER) is a subtask of informationextraction and aims to identify atomic entities in text that fall intopredefined categories such as person, location, organization, etc.Recent efforts in NER try to extract entities and link them tolinked data entities. Linked data is a term used for data resourcesthat are created using semantic web standards such as DBpedia.There are a number of online tools that try to identify namedentities in text and link them to linked data resources. Althoughone can use these tools via their APIs and web interfaces, they usedifferent data resources and different techniques to identify namedentities and not all of them reveal this information. One of themajor tasks in NER is disambiguation that is identifying the rightentity among a number of entities with the same names; forexample “apple” standing for both “Apple, Inc.” the company andthe fruit. We developed a similar tool called NERSO, short forNamed Entity Recognition Using Semantic Open Data, toautomatically extract named entities, disambiguating and linkingthem to DBpedia entities. Our disambiguation method is based onconstructing a graph of linked data entities and scoring them usinga graph-based centrality algorithm. We evaluate our system bycomparing its performance with two publicly available NER tools.The results show that NERSO performs better.