Dealing with context is one of the most
interesting and most important
problems faced in Artificial
Intelligence (AI). Traditional AI
applications
often require to model, store, retrieve
and reason about knowledge that
holds within certain circumstances - the
context. Without considering this
contextual information, reasoning can
easily run to problems such as:
inconsistency, when considering
knowledge in the
wrong context; inefficiency, by
considering knowledge irrelevant for a
certain context; incompleteness, since
an inference may depend on knowledge
assumed in the context and not
explicitly stated. Contextual information is
also relevant in many tasks in knowledge
representation and reasoning such
as common-sense reasoning, dealing with
inconsistency, ambiguity, and
uncertainty, evolution, etc.
In recent years, research in contextual
knowledge representation and
reasoning became more relevant in the
areas of Semantic Web, Linked Open
Data, and Ambient Intelligence, where
knowledge is not considered a
monolithic and static asset, but it is
distributed in a network of
interconnected heterogeneous and
evolving knowledge resources. The ARCOE
workshop aims to provide a dedicated
forum for researchers interested in
these topics to discuss recent
developments, important open issues, and
future directions.
-- Topics --
ARCOE-12 welcomes submissions on the
topics below as well as on their
intersection and other topics related to
acquisition, representation,
reasoning with context and its applications.
Philosophical and theoretical
foundations of context:
1. What is context and how should it be
represented.
2. Relevant types of contextual
information and their properties.
3. Combining contextual information with
object information for reasoning.
4. Context and common-sense reasoning.
5. Exploiting context in inconsistency
and uncertainty handling,
defeasible reasoning and argumentation.
6. Contextual logic programming.
7. Updating contextual knowledge and
context-aware belief revision.
8. Frameworks for formalizing context
and context-aware knowledge
representation.
Context modeling and contextual
knowledge engineering:
1. Modeling of user's/agent's context.
2. Context driven organization of
knowledge and modeling.
3. Ontologies for context modeling.
4. Context-aware modeling tools and
methodology.
5. Comparisons to context-unaware
modeling techniques.
Effective reasoning with context:
1. Effective context-aware reasoning
algorithms.
2. Distributed reasoning with context.
3. Context-driven heuristics in
classical reasoning systems.
4. Reasoning under uncertainty and
inconsitency.
5. Defeasible reasoning.
4. Hybrid formalisms for reasoning with
context, including sub-symbolic
contexts
Applications of context in areas such as:
1. Agent communication and coordination.
2. Semantic Web and Linked Open Data.
3. Knowledge modularization.
4. Ontology matching.
5. Ontology fault diagnosis and repair.
6. Ontology evolution and versioning.
7. Information integration.
8. Ambient intelligence and pervasive
computing.
9. Exploiting context in Web 2.0
applications, e-commerce, and e-learning.
