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LHD 2011 : Workshop on Discovering Meaning On the Go in Large Heterogeneous Data 2011 (LHD-11)


When Jul 16, 2011 - Jul 16, 2011
Where Barcelona, Spain
Submission Deadline Mar 14, 2011
Notification Due Apr 25, 2011
Final Version Due May 16, 2011
Categories    data   ontology

Call For Papers

An interdisciplinary approach is necessary to discover and match meaning dynamically in a world of increasingly large data. This workshop aims to bring together practitioners from academia, industry and government for interaction and discussion. The workshop will feature:

* A panel discussion representing industrial and governmental input, entitled “Big Society meets Big Data: Industry and Government Applications of Mapping Meaning”. Panel members will include: Peter Mika (Yahoo!), Alon Halevy (Google), and Tom McCutcheon (Dstl).
* An invited talk from Fausto Giunchglia, discussing the relationship between social computing and ontology matching;
* Paper and poster presentations;
* Workshop sponsored by: Yahoo! Research, W3C and others

Workshop Description

The problem of semantic alignment - that of two systems failing to understand one another when their representations are not identical - occurs in a huge variety of areas: Linked Data, database integration, e-science, multi-agent systems, information retrieval over structured data; anywhere, in fact, where semantics or a shared structure are necessary but centralised control over the schema of the data sources is undesirable or impractical. Yet this is increasingly a critical problem in the world of large scale data, particularly as more and more of this kind of data is available over the Web.

In order to interact successfully in an open and heterogeneous environment, being able to dynamically and adaptively integrate large and heterogeneous data from the Web “on the go” is necessary. This may not be a precise process but a matter of finding a good enough integration to allow interaction to proceed successfully, even if a complete solution is impossible.

Considerable success has already been achieved in the field of ontology matching and merging, but the application of these techniques - often developed for static environments - to the dynamic integration of large-scale data has not been well studied.

Presenting the results of such dynamic integration to both end-users and database administrators - while providing quality assurance and provenance - is not yet a feature of many deployed systems. To make matters more difficult, on the Web there are massive amounts of information available online that could be integrated, but this information is often chaotically organised, stored in a wide variety of data-formats, and difficult to interpret.

This area has been of interest in academia for some time, and is becoming increasingly important in industry and - thanks to open data efforts and other initiatives - to government as well. The aim of this workshop is to bring together practitioners from academia, industry and government who are involved in all aspects of this field: from those developing, curating and using Linked Data, to those focusing on matching and merging techniques.

Topics of interest include, but are not limited to:

* Integration of large and heterogeneous data
* Machine-learning over structured data
* Ontology evolution and dynamics
* Ontology matching and alignment
* Presentation of dynamically integrated data
* Incentives and human computation over structured data and ontologies
* Ranking and search over structured and semi-structured data
* Quality assurance and data-cleansing
* Vocabulary management in Linked Data
* Schema and ontology versioning and provenance
* Background knowledge in matching
* Extensions to knowledge representation languages to better support change
* Inconsistency and missing values in databases and ontologies
* Dynamic knowledge construction and exploitation
* Matching for dynamic applications (e.g., p2p, agents, streaming)
* Case studies, software tools, use cases, applications
* Open problems
* Foundational issues

Applications and evaluations on data-sources that are from the Web and Linked Data are particularly encouraged.

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