Data Cleaning publication categorizer

The reference reconciliation problem consists in deciding whether different identifiers refer to the same data, i.e. correspond to the same real world entity. In this article we present a reference reconciliation approach which combines a logical method for reference reconciliation called L2R and a numerical one called N2R. This approach exploits the schema and data semantics, which is translated into a set of Horn FOL rules of reconciliation. These rules are used in L2R to infer exact decisions both of reconciliation and non-reconciliation.

This paper gives an overview of Personal Name Matching. Personal
name matching is of great importance for all applications that deal
with personal names. The problem with personal names is that they
are not unique and sometimes even for one name many variations
exist. This leads to the fact that databases on the one hand may
have several entries for one and the same person and on the other
hand have one entry for many different persons. For the evaluation
of Personal Name Matching algorithms test collections are of great

Entity Resolution (ER) matches and merges records that refer to the same real-world entities, and is typically a compute-intensive process due to complex matching functions and high data volumes. We present a family of algorithms, D-Swoosh, for distributing the ER workload across multiple processors. The algorithms use generic match and merge functions, and ensure that new merged records are distributed to processors that may have matching records. We perform a detailed performance evaluation on a testbed of 15 processors.

In this article, we address the problem of reference disambiguation. Specifically, we consider a situation where entities in the database are referred to using descriptions (e.g., a set of instantiated attributes). The objective of reference disambiguation is to identify the unique entity to which each description corresponds. The key difference between the approach we propose (called RelDC) and the traditional techniques is that RelDC analyzes not only object features but also inter-object relationships to improve the disambiguation quality.

The role of data resources in today's business environment is multi-faceted. Primarily, they support the operational needs of an organization or a company. Secondarily, they can be used for decision support and management. The quality of the data, used to support the operational needs, is usually below the quality required for decision support and management.

Similarity-based grouping of data entries in one or more data sources is a task underlying many different data management tasks, such as, structuring search results, removal of redundancy in databases and data integration. Similarity-based grouping of data entries is not a trivial task in the context of life science data sources as the stored data is complex, highly correlated and represented at different levels of granularity. The contribution of this paper is two-fold. 1) We propose a method for similarity-based grouping and 2) we show results from test cases.