Accurate and efficient entity resolution is an open challenge of particular relevance to intelligence organisations that collect large datasets from disparate sources with differing levels of quality and standard. Starting from a first-principles formulation of entity resolution, this paper presents a novel Entity Resolution algorithm that introduces a data-driven blocking and record linkage te...

When querying data providers on the web, one has no guarantee that they will reply within a given time. Some providers may even not answer at all. This makes it infeasible to wait for a complete result before beginning with the entity resolution. In order to solve this problem, we present a query-time entity resolution approach that takes the asynchronous nature of the replies from data provide...

Chemical entities are ubiquitous through the biomedical literature and the development of text-mining systems that can efficiently identify those entities are required. Due to the lack of available corpora and data resources, the community has focused its efforts in the development of gene and protein named entity recognition systems, but with the release of ChEBI and the availability of an ann...

Entity-resolution (also known as deduplication, record linkage, and reference reconciliation, among others) was one of the original motivating applications [6] for the Trio system, which has been under development at Stanford over the past several years. • Entity-resolution is the process of determining when multiple data records are likely to represent the same real-world entity, and possibly ...

In the Web of data, entities are described by interlinked data rather than documents on the Web. In this talk, we focus on entity resolution in the Web of data, i.e., on the problem of identifying descriptions that refer to the same real-world entity within one or across knowledge bases in the Web of data. To reduce the required number of pairwise comparisons among descriptions, methods for ent...

Entity resolution, which seeks to identify records that represent the same entity, is an important step in many data integration and data cleaning applications. However, entity resolution is challenging both in terms of scalability (all-against-all comparisons are computationally impractical) and result quality (syntactic evidence on record equivalence is often equivocal). As a result, end-to-e...

Increasingly, organizations have employed methods to understand unstructured text across the web. Entity resolution is used to identify mentions in large, streaming text corpora. Sampling-based entity resolution using Markov Chain Monte Carlo (MCMC) techniques guarantees convergence to a stationary distribution and can jump out of a local optimum. When performing entity resolution over streams ...