Simplifying Entity Resolution on Web Data with Schema-agnostic, Non-iterative Matching

International audienceEntity Resolution (ER) aims to identify different descriptions in various Knowledge Bases (KBs) that refer to the same entity. ER is challenged by the Variety, Volume and Veracity of descriptions published in the Web of Data. To address them, we propose the MinoanER framework that fulfills full automation and support of highly heterogeneous entities. MinoanER leverages a token-based similarity of entities to define a new metric that derives the similarity of neighboring entities from the most important relations, indicated only by statistics. For high efficiency, similarities are computed from a set of schema-agnostic blocks and processed in a non-iterative way that involves four threshold-free heuristics. We demonstrate that the effectiveness of MinoanER is comparable to existing ER tools over real KBs exhibiting low heterogeneity in terms of entity types and content. Yet, MinoanER outperforms state-of-the-art ER tools when matching highly heterogeneous KBs

MinoanER: Schema-Agnostic, Non-Iterative, Massively Parallel Resolution of Web Entities

Entity Resolution (ER) aims to identify different descriptions in various Knowledge Bases (KBs) that refer to the same entity. ER is challenged by the Variety, Volume and Veracity of entity descriptions published in the Web of Data. To address them, we propose the MinoanER framework that simultaneously fulfills full automation, support of highly heterogeneous entities, and massive parallelization of the ER process. MinoanER leverages a token-based similarity of entities to define a new metric that derives the similarity of neighboring entities from the most important relations, as they are indicated only by statistics. A composite blocking method is employed to capture different sources of matching evidence from the content, neighbors, or names of entities. The search space of candidate pairs for comparison is compactly abstracted by a novel disjunctive blocking graph and processed by a non-iterative, massively parallel matching algorithm that consists of four generic, schema-agnostic matching rules that are quite robust with respect to their internal configuration. We demonstrate that the effectiveness of MinoanER is comparable to existing ER tools over real KBs exhibiting low Variety, but it outperforms them significantly when matching KBs with high Variety.Comment: Presented at EDBT 2001

Simplifying Entity Resolution on Web Data with Schema-agnostic, Non-iterative Matching

Entity Resolution (ER) aims to identify different descriptions in various Knowledge Bases (KBs) that refer to the same entity. ER is challenged by the Variety, Volume and Veracity of descriptions published in the Web of Data. To address them, we propose the MinoanER framework that fulfills full automation and support of highly heterogeneous entities. MinoanER leverages a token-based similarity of entities to define a new metric that derives the similarity of neighboring entities from the most important relations, indicated only by statistics. For high efficiency, similarities are computed from a set of schema-agnostic blocks and processed in a non-iterative way that involves four threshold-free heuristics. We demonstrate that the effectiveness of MinoanER is comparable to existing ER tools over real KBs exhibiting low heterogeneity in terms of entity types and content. Yet, MinoanER outperforms state-of-the-art ER tools when matching highly heterogeneous KBs