Reference reconciliation in complex information spaces

Dong, Xin Luna; Halevy, Alon; Madhavan, Jayant

doi:10.1145/1066157.1066168

Cited by 393 publications

(350 citation statements)

References 15 publications

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“…At first sight, the entity search task has a strong similarity with entity linkage techniques [14], also known as data matching [4,9], deduplication [27], resolution [3], merge-purge [13], entity identification [21], and reference reconciliation [10]. Entity Linkage is the process that decides whether two descriptions refer to the same real world entity (see [12] for an overview).…”

Section: Entity Searchmentioning

confidence: 99%

From Web Data to Entities and Back

Miklós

Bonvin

Bouquet

et al. 2010

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

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Abstract. We present the Entity Name System (ENS), an enabling infrastructure, which can host descriptions of named entities and provide unique identifiers, on large-scale. In this way, it opens new perspectives to realize entity-oriented, rather than keyword-oriented, Web information systems. We describe the architecture and the functionality of the ENS, along with tools, which all contribute to realize the Web of entities.

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Section: Entity Searchmentioning

confidence: 99%

From Web Data to Entities and Back

Miklós

Bonvin

Bouquet

et al. 2010

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

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“…Web application and integration systems are progressively moving from tuple and value-based towards entity-based solutions, i.e., systems in which the basic data unit is an entity, independently of its logical modeling [10]. Furthermore, web integration systems, in order to achieve interoperability, may need to provide unique identification for the entities in the data they exchange [11].…”

Section: Introductionmentioning

confidence: 99%

Modeling Concept Evolution: A Historical Perspective

Rizzolo

Velegrakis

Mylopoulos

et al. 2009

Conceptual Modeling - ER 2009

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Abstract. The world is changing, and so must the data that describes its history. Not surprisingly, considerable research effort has been spent in Databases along this direction, covering topics such as temporal models and schema evolution. A topic that has not received much attention, however, is that of concept evolution. For example, Germany (instance-level concept) has evolved several times in the last century as it went through different governance structures, then split into two national entities that eventually joined again. Likewise, a caterpillar is transformed into a butterfly, while a mother becomes two (maternally-related) entities. As well, the concept of Whale (a class-level concept) changed over the past two centuries thanks to scientific discoveries that led to a better understanding of what the concept entails. In this work, we present a formal framework for modeling, querying and managing such evolution. In particular, we describe how to model the evolution of a concept, and how this modeling can be used to answer historical queries of the form "How has concept X evolved over period Y". Our proposal extends an RDF-like model with temporal features and evolution operators. Then we provide a query language that exploits these extensions and supports historical queries.

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“…Xing Dong et al [10] propose to reconcile the object references in three steps. The first step is constructing the dependency graph, which describes the relationships between object pairs.…”

Section: Related Workmentioning

confidence: 99%

“…After we get the selected class matching pairs, we adopt an object reconciliation algorithm developed by Dong, Halevy and Madhavan in [10] to reconcile the instances of classes from two ontologies. The original algorithm considers the relationship of matching pairs and determines whether two data objects in different databases represent the same real-world entity.…”

Section: Object Reconciliationmentioning

confidence: 99%

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Discovering Executable Semantic Mappings Between Ontologies

Qin

Dou

LePendu

On the Move to Meaningful Internet Systems 2007: CoopIS, DOA, ODBASE, GADA, and IS

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Abstract.Creating executable semantic mappings is an important task for ontology-based information integration. Although it is argued that mapping tools may require interaction from humans (domain experts) for best accuracy, in general, automatic ontology mapping is an AI-Complete problem. Finding matchings (correspondences) between the concepts of two ontologies is the first step towards solving this problem but matchings are normally not directly executable for data exchange or query translation. This paper presents an systematic approach to combining ontology matching, object reconciliation and multi-relational data mining to find the executable mapping rules in a highly automatic manner. Our approach starts from an iterative process to search the matchings and do object reconciliation for the ontologies with data instances. Then the result of this iterative process is used for mining frequent queries. Finally the semantic mapping rules can be generated from the frequent queries. The results show our approach is highly automatic without losing much accuracy compared with human-specified mappings.

show abstract

Reference reconciliation in complex information spaces

Cited by 393 publications

References 15 publications

From Web Data to Entities and Back

From Web Data to Entities and Back

Modeling Concept Evolution: A Historical Perspective

Discovering Executable Semantic Mappings Between Ontologies

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