51,792 research outputs found
A framework for utility data integration in the UK
In this paper we investigate various factors which prevent utility knowledge from being
fully exploited and suggest that integration techniques can be applied to improve the
quality of utility records. The paper suggests a framework which supports knowledge
and data integration. The framework supports utility integration at two levels: the
schema and data level. Schema level integration ensures that a single, integrated geospatial
data set is available for utility enquiries. Data level integration improves utility data
quality by reducing inconsistency, duplication and conflicts. Moreover, the framework
is designed to preserve autonomy and distribution of utility data. The ultimate aim of
the research is to produce an integrated representation of underground utility infrastructure
in order to gain more accurate knowledge of the buried services. It is hoped that
this approach will enable us to understand various problems associated with utility data,
and to suggest some potential techniques for resolving them
Nonparametric Bayesian Modeling for Automated Database Schema Matching
The problem of merging databases arises in many government and commercial
applications. Schema matching, a common first step, identifies equivalent
fields between databases. We introduce a schema matching framework that builds
nonparametric Bayesian models for each field and compares them by computing the
probability that a single model could have generated both fields. Our
experiments show that our method is more accurate and faster than the existing
instance-based matching algorithms in part because of the use of nonparametric
Bayesian models
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
Towards information profiling: data lake content metadata management
There is currently a burst of Big Data (BD) processed and stored in huge raw data repositories, commonly called Data Lakes (DL). These BD require new techniques of data integration and schema alignment in order to make the data usable by its consumers and to discover the relationships linking their content. This can be provided by metadata services which discover and describe their content. However, there is currently a lack of a systematic approach for such kind of metadata discovery and management. Thus, we propose a framework for the profiling of informational content stored in the DL, which we call information profiling. The profiles are stored as metadata to support data analysis. We formally define a metadata management process which identifies the key activities required to effectively handle this.We demonstrate the alternative techniques and performance of our process using a prototype implementation handling a real-life case-study from the OpenML DL, which showcases the value and feasibility of our approach.Peer ReviewedPostprint (author's final draft
Ontology mapping: the state of the art
Ontology mapping is seen as a solution provider in today's landscape of ontology research. As the number of ontologies that are made publicly available and accessible on the Web increases steadily, so does the need for applications to use them. A single ontology is no longer enough to support the tasks envisaged by a distributed environment like the Semantic Web. Multiple ontologies need to be accessed from several applications. Mapping could provide a common layer from which several ontologies could be accessed and hence could exchange information in semantically sound manners. Developing such mapping has beeb the focus of a variety of works originating from diverse communities over a number of years. In this article we comprehensively review and present these works. We also provide insights on the pragmatics of ontology mapping and elaborate on a theoretical approach for defining ontology mapping
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