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CALL FOR PAPERS
DiDaS 2012 – 1st Workshop on Distributional Data Semantics
September 21, 2012, Palermo, Italy
at the 6th IEEE International Conference on Semantic Computing (ICSC)
http://didas.org
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Efficient means for capturing and representing computational semantics of data are critical for coping with current limitations of information systems, especially if one wants to make sense of large amounts of information coming from heterogeneous and/or poorly structured resources. Efforts aimed at representing meaning of data in a machine-readable way (i.e., Semantic Web or deductive databases) have achieved some level of success. There are alternatives to the top-down, assertional approaches to semantics, which can work even without (too much) expensive human involvement. One of the most widely and successfully used are distributional semantics models that have been researched within the field of computational linguistics. These models, based on the distributional hypothesis, provide a bottom-up approach to the computational representation of meaning, where the statistical co-occurrence of words in unstructured corpora can provide a basis for the construction of simplified but comprehensive and extensible models of semantic content.
Most of the research activity on distributional semantics has been targeting theoretical and empirical aspects of distributional semantic models with the bulk of the progress been made to date by the natural language processing community. However, a high demand for robust and comprehensive computational models of meaning is present in different areas such as databases, information retrieval, semantic web, artificial intelligence, human-computer interaction, among other areas. This demand, meeting with the availability of mature distributional models and with large-scale unstructured and structured data resources, brings the opportunity of leveraging robust semantic models in all these fields.
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Objective
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DiDaS 2012 aims at connecting distributional semantics with areas that could benefit from a distributional model of meaning. The workshop targets the exploration of both applied and theoretical aspects of these cross-disciplinary interactions. One of the main objectives of the workshop is to bridge the existing gap between distributional semantics and research areas which can strongly benefit from distributional (i.e., bottom-up) models of meaning that would complement the traditional top-down approaches to semantics (e.g., ontologies or database schemata). Additionally, the workshop encourages the participation of domain experts and industry practitioners focused on domain-oriented applications of distributional semantics.
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Topics of Interest
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The topics of interest include, but are not limited to the following categories and their sub-domains.
* Foundational Issues:
– Novel distributional models of meaning
– Distributional semantics and compositionality
– Distributional semantics and knowledge representation
– Geometrical and formal aspects of distributional semantics
– Comparative analysis between distributional and non-distributional (e.g., model-theoretic) models of meaning
* Semantic Web & Databases:
– Distributional semantics for structured & semi-structured data
– Distributional query models
– Distributional reasoning models
– Web-scale distributional models
– Distributional semantics and data integration
– Distributional semantics and ontology alignment
– Knowledge consolidation
– Data-based ontology debugging
* Information Retrieval:
– The relationship between IR, vector-space models and distributional semantics
– Distributional semantics and semantic relatedness
– Distributional ranking functions
– Distributional semantic search models and architectures
– Quantum-IR and distributional semantics
– Dimensionality reduction
* Knowledge Acquisition:
– Concept formation
– Taxonomy learning
– Relation learning
– Ontology and axiom learning
– Distributional semantics and data mining
* Experimental Analysis:
– Evaluation methodologies for distributional semantics
– Evaluation data sets and resources
– Comparative evaluation of distributional models
– Temporal aspects of distributional models
– Distributional semantics and high-performance/parallel computing
* Applications:
– Applications using distributional semantics
– Domain-specific distributional models
– Use cases for distributional semantics
We especially encourage submissions that cover more aspects across multiple above-mentioned categories.
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Important Dates
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- Deadline for abstracts: July 2, 2012.
- Deadline for submissions: July 7, 2012.
- Notification of acceptance: August 7, 2012.
- Camera-ready versions: August 21, 2012.
- Workshop date: September 21, 2012.
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Submissions Instructions
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Papers should be prepared following the IEEE format and their length should not exceed 8 pages. Authors are invited to submit regular papers (8 pages), short papers (4 pages), demonstration and position papers (2 pages). Additional details on the submission instructions are available at http://didas.org/submission.html
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Organization Committee
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Workshop Co-Chairs
Andre Freitas, DERI, National University of Ireland, Galway, Ireland
Eduard H. Hovy, Information Sciences Institute, University of Southern California
Vit Novacek DERI, National University of Ireland, Galway, Ireland
Program Committee
Enrique Alfonseca, Google Inc., Zürich, Switzerland
Pierpaolo Basile, Department of Computer Science, University of Bari "Aldo Moro", Italy
Rafaela Bernardi, Department of Information Engineering and Computer Science, University of Trento, Italy
Chris Biemann, Ubiquitous Knowledge Processing Lab, TU Darmstadt, Germany
Bob Coecke, Oxford University Computing Laboratory, UK
Trevor Cohen, University of Texas Health Science Center at Houston, USA
Andre Freitas, DERI, National University of Ireland, Galway, Ireland
Eugenie Giesbrecht, FZI Research Center, University of Karlsruhe, Germany
Aurelie Herbelot, Institut für Linguistik, Universität Potsdam, Germany
Fabio Massimo Zanzotto, DISP, University of Rome “Tor Vergata”, Italy
Diana McCarthy, Lexical Computing Ltd., UK
Vit Novacek, DERI, National University of Ireland, Galway, Ireland
Magnus Sahlgren, Gavagai, Sweden
Hinrich Schütze, Statistical NLP Group,University of Stuttgart, Germany
György Szarvas, Ubiquitous Knowledge Processing Lab, TU Darmstadt, Germany
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