TY - CONF A1 - Zwicklbauer, Stefan A1 - Seifert, Christin A1 - Granitzer, Michael T1 - Robust and Collective Entity Disambiguation through Semantic Embeddings N2 - Entity disambiguation is the task of mapping ambiguous terms in natural-language text to its entities in a knowledge base. It finds its application in the extraction of structured data in RDF (Resource Description Framework) from textual documents, but equally so in facilitating artificial intelligence applications, such as Semantic Search, Reasoning and Question & Answering. We propose a new collective, graph-based disambiguation algorithm utilizing semantic entity and document embeddings for robust entity disambiguation. Robust thereby refers to the property of achieving better than state-of-the-art results over a wide range of very different data sets. Our approach is also able to abstain if no appropriate entity can be found for a specific surface form. Our evaluation shows, that our approach achieves significantly (>5%) better results than all other publicly available disambiguation algorithms on 7 of 9 datasets without data set specific tuning. Moreover, we discuss the influence of the quality of the knowledge base on the disambiguation accuracy and indicate that our algorithm achieves better results than non-publicly available state-of-the-art algorithms. KW - Entity Linking KW - Entity Disambiguation KW - Neuronal Networks KW - Embeddings Y1 - 2016 UR - https://opus4.kobv.de/opus4-uni-passau/frontdoor/index/index/docId/370 UR - https://nbn-resolving.org/urn:nbn:de:bvb:739-opus4-3704 ER -