DSEL: A DomainSpecific Entity Linking System Entity linking refers to the task of resolving multiple named entity mentions in a document to their correct references in a knowledge base (KB). It can bridge the gap between unstructured nature language documents that computers hardly understand and a structured semantic Knowledge Base which can be easily processed by computers. Existing studies and systems about entity linking mainly focus on the open domain, which may cause three problems: 1. linking to unconcerned entities; 2. time and space consuming; 3. less precision. In this paper, we address the problem by restricting entity linking into specific domains and leveraging domain information to enhance the linking performance. We propose an unsupervised method to generate domain data from Wikipedia and provide a domainspecific neural collective entity linking model for each domain. Based on domain data and domain models, we build a system that can provide domain entity linking for users. Our system, DomainSpecific neural collective Entity Linking system (DSEL), supporting entity linking in 12 domains, is published as an online website, https:dsel.xlore.org.

Using Knowledge Base Semantics in ContextAware Entity Linking Entity linking is a core task in textual document processing, which consists in identifying the entities of a knowledge base (KB) that are mentioned in a text. Approaches in the literature consider either independent linking of individual mentions or collective linking of all mentions. Regardless of this distinction, most approaches rely on the Wikipedia encyclopedic KB in order to improve the linking quality, by exploiting its entity descriptions (web pages) or its entity interconnections (hyperlink graph of web pages). In this paper, we devise a novel collective linking technique which departs from most approaches in the literature by relying on a structured RDF KB. This allows exploiting the semantics of the interrelationships that candidate entities may have at disambiguation time rather than relying on raw structural approximation based on Wikipediau0027s hyperlink graph. The few approaches that also use an RDF KB simply rely on the existence of a relation between the candidate entities to which mentions may be linked. Instead, we weight such relations based on the RDF KB structure and propose an efficient decoding strategy for collective linking. Experiments on standard benchmarks show significant improvement over the state of the art.