Efficient entity resolution for large heterogeneous information spaces

Abstract

We have recently witnessed an enormous growth in the volume of structured and semi-structured data sets available on the Web. An important prerequisite for using and combining such data sets is the detection and merge of information that describes the same real-world entities, a task known as Entity Resolution. To make this quadratic task efficient, blocking techniques are typically employed. However, the high dynamics, loose schema binding, and heterogeneity of (semi-)structured data, impose new challenges to entity resolution. Existing blocking approaches become inapplicable because they rely on the homogeneity of the considered data and a-priory known schemata. In this paper, we introduce a novel approach for entity resolution, scaling it up for large, noisy, and heterogeneous information spaces. It combines an attribute-agnostic mechanism for building blocks with intelligent block processing techniques that boost blocks with high expected utility, propagate knowledge about identified matches, and preempt the resolution process when it gets too expensive. Our extensive evaluation on real-world, large, heterogeneous data sets verifies that the suggested approach is both effective and efficient.

Improving Integration Effectiveness of ID Mapping Based Biological Record Linkage

“…Therefore, we are not only required to establish the map between 1453505_a_at and EIF2AK3, we also need to generate the transitive map between 1453505_a_at and PERK via EIF2AK3. In other words, the traditional record linkage [19], [20], [21], [22], [23], [24], [25] and mapping techniques largely do not help, and a higher level mapping method is warranted.…”

“…BioFlow also supports two other statements called combine and link to facilitate entity based [19] (as opposed to tuple based) union and join respectively, which can be used to collect IDs from different sources, and for implementing the extend statement despite representation heterogeneities. The syntax for the two statements are presented below.…”

Embracing Uncertainty in Entity Linking

“…These techniques deploy a variety of different methodologies and directions. These includes string similarity metrics [8,9] for computing the matching being the given textual representations of entities, the use of the available inner-relationships between the entities [17,27], clustering [7], and blocking techniques [30,31] for reducing the required execution time. However, as already explained in Sect.…”

A Type-Based Blocking Technique for Efficient Entity Resolution over Large-Scale Data

“…Entity resolution (ER), also known as record linkage, entity reconciliation, or merge/purge, is the procedure of identifying a group of entities (records) representing the same realworld entity [1][2][3]. Generally speaking, ER has become the first step of data processing and widely used in many application domain, such as digital libraries, smart city, financial transactions, and social networks.…”