Data Cleaning publication categorizer

The reference reconciliation problem consists of deciding whether different identifiers refer to the same world entity. Some existing reference reconciliation approaches use key constraints to infer reconciliation decisions. In the context of the Linked Open Data, this knowledge is not available. We propose KD2R, a method which allows automatic discovery of key constraints associated to OWL2 classes. These keys are discovered from RDF data which can be incomplete. The proposed algorithm allows this discovery without having to scan all the data.

The OKKAM initiative1 has recently highlighted the need
of moving from the traditional web towards a \web of entities", where
real-world objects descriptions could be retrieved, univocally identied,
and shared over the web. In this paper, we propose our vision of the
entity recognition problem and, in particular, we propose methods and
techniques to capture the \identity" of a real entity in the SemanticWeb.
We claim that automatic techniques are needed to compare dierent
RDF descriptions of a domain with the goal of automatically detect

The Linked Movie Database (LinkedMDB) project provides
a demonstration of the first open linked dataset connecting
several major existing (and highly popular) movie web
resources. The database exposed by LinkedMDB contains
millions of RDF triples with hundreds of thousands of RDF
links to existing web data sources that are part of the growing
Linking Open Data cloud, as well as to popular movierelated
web pages such as IMDb. LinkedMDB uses a novel
way of creating and maintaining large quantities of high
quality links by employing state-of-the-art approximate join

Schema-level heterogeneity represents an obstacle for auto-
mated discovery of coreference resolution links between in-
dividuals. Although there is a multitude of existing schema
matching solutions, the Linked Data environment diers
from the standard scenario assumed by these tools. In par-
ticular, large volumes of data are available, and repositories
are connected into a graph by instance-level mappings. In
this paper we describe how these features can be utilised to
produce schema-level mappings which facilitate the instance

Schema heterogeneity issues often represent an obstacle for discovering coreference links between individuals in semantic data repositories. In this paper we present an approach, which performs ontology schema matching in order to improve instance coreference resolution performance. A novel feature of the approach is its use of existing instance-level coreference links defined in third-party repositories as background knowledge for schema matching techniques.

Finding RDF individuals that refer to the same real-world entities but have different URIs is necessary for the efficient use of data across sources. The requirements for such instance-level integration of RDF data are different from both database record linkage and ontology schema matching scenarios. Flexible configuration and reuse of different methods is needed to achieve good performance.

The Web of Data is built upon two simple ideas: Employ the RDF
data model to publish structured data on the Web and to set
explicit RDF links between entities within different data sources.
This paper presents the Silk – Link Discovery Framework, a tool
for finding relationships between entities within different data
sources. Data publishers can use Silk to set RDF links from their
data sources to other data sources on the Web. Silk features a
declarative language for specifying which types of RDF links
should be discovered between data sources as well as which

The Web of Data has emerged as a way of exposing structured
linked data on the Web. It builds on the central building
blocks of the Web (URIs, HTTP) and benets from its
simplicity and wide-spread adoption. It does, however, also
inherit the unresolved issues such as the broken link problem.
Broken links constitute a major challenge for actors
consuming Linked Data as they require them to deal with
reduced accessibility of data. We believe that the broken
link problem is a major threat to the whole Web of Data
idea and that both Linked Data consumers and providers

We tackle the problem of disambiguating entities on the Web. We
propose a user-driven scheme where graphs of entities – represented
by globally identifiable declarative artifacts – self-organize
in a dynamic and probabilistic manner. Our solution has the following
two desirable properties: i) it lets end-users freely define
associations between arbitrary entities and ii) it probabilistically infers
entity relationships based on uncertain links using constraintsatisfaction
mechanisms. We outline the interface between our

We present Linkage Query Writer (LinQuer), a system for
generating SQL queries for semantic link discovery over re-
lational data. The LinQuer framework consists of (a) LinQL,
a language for specification of linkage requirements; (b) a
web interface and an API for translating LinQL queries to
standard SQL queries; (c) an interface that assists users in
writing LinQL queries. We discuss the challenges involved in
the design and implementation of a declarative and easy to
use framework for discovering links between different data

Object matching or object consolidation is a crucial task for data integration
and data cleaning. It addresses the problem of identifying
object instances in data sources referring to the same real world
entity. We propose a flexible framework called MOMA for mapping-
based object matching. It allows the construction of match
workflows combining the results of several matcher algorithms on
both attribute values and contextual information. The output of a
match task is an instance-level mapping that supports information