Call for PapersLatent Semantics for the Web (LSW) Workshop co-held with ACM
WWW/TheWebConf 2018 <https://www2018.thewebconf.org/> in Lyon, France,
April 23-27, 2018
http://usc-isi-i2.github.io/WWW18workshop/
Important Dates (2018)
Papers due: Jan. 31 (Extended)
Notification: Feb. 26
Workshop: April 23
Understanding the semantics of Web content is at the core of many
applications, ranging from Web search, news aggregation and machine
translation to personal assistant services such as Amazon Echo, Cortana,
Siri, and Google Home. Latent Semantics utilizes a rich suite of
information retrieval and machine learning techniques that capture meaning
through powerful statistical neural network-based methods like word2vec and
node2vec. Recently, such emerging semantic models have achieved
state-of-the-art results in several predictive applications (e.g.
recommendation, node classification, knowledge graph completion) relevant
not just to the broader World Wide Web research community, but also allied
communities such as Semantic Web, data mining and natural language
processing. In the LSW workshop, we explore the convergence of latent
semantics (LS) models and the Web. We explore several aspects of LS models
that are particularly relevant to the Web, namely
• Novel methods, including embedding methods, that take into account the
specific properties of the Web (e.g., link structure, multimedia content…)
• Evaluation of LS methods, especially in a Web context
• Intersection of LS models with traditional ontological semantics
• Reasoning about such models in a rigorous way
• Extending the scope of these models with techniques such as zero-shot
learning and transfer learning.
Short and long papers are solicited for the following set of non-exhaustive
topics:
Theory, Algorithms and Methods:
• Novel Latent Semantic (LS) and embedding models, especially for diverse
data such as webpages, RDF graphs, and ontologies
• Theoretical foundations of LS models
• Novel algorithms for representing and embedding knowledge graphs
• Novel methods for Knowledge graph completion
• Novel synergies for combining LS models with graphical models like
Probabilistic Soft Logic (PSL) and Markov Logic Networks (MLNs)
• Innovative and efficient methods for querying LS models
• Theoretically grounded methods for evaluating LS models
Applications
• Web search
• Question answering
• Personalization
• Data Mining
• User interfaces and visualization
• Semantic recommendations
• Link prediction
• Node classification
• Instance matching/Entity resolution
Experiments, Systems and Data
• Novel datasets, especially datasets acquired through, or useful for
evaluating, Web-specific LS models
• Novel methodologies, concerning both evaluations and data
curation/collection
• Experimental results using existing methods, including negative results
of interest
• Descriptions of best practices, case studies, lessons learned, and
features
We will also accept a small number of vision, opinion and position papers
that provide discussions on challenges and roadmaps (for hybrid systems,
and emerging semantic models).
All papers should be formatted according to the latest ACM double-column
conference format also used for the main research track at WWW. All papers
will be peer reviewed, single-blinded. Authors whose papers are accepted to
the workshop will have the opportunity to participate in a poster session,
and some set may also be chosen for oral presentation. Long papers should
not exceed than 8 pages, and short papers should not exceed 4 pages,
including all references. The accepted papers will be published in the
WWW18 Satellite Proceedings and will not be considered archival.
We are using the EasyChair system
<https://easychair.org/conferences/?conf=www2018satellites> for
submissions. If you would like to make a submission, please enter as an
author and then select the name of the workshop (Latent Semantics for the
Web) to submit papers and be sure to select the correct name as other
workshops may also be listed, along with other satellite tracks!
Please email any enquiries to Mayank Kejriwal and cc Michael Cochez
Organizers
Michael Cochez is a postdoctoral researcher at the Fraunhofer Institute
for Applied Information Technology FIT.
Mayank Kejriwal is a Research Scientist at the Information Sciences
Institute, USC Viterbi School of Engineering.
Achim Rettinger is leading a research team on Adaptive Data Analytics at
Karlsruhe Institute of Technology.
Pedro Szekely is a Research Associate Professor and a Research Team Leader
at the Information Sciences Institute, USC Viterbi School of Engineering.