(Apologies for cross posting)

Special Issue of the Journal of Applied Ontology “Meaning in Context:
ontologically and linguistically motivated representations of objects and
events.”

https://submissions.iospress.com/applied-ontology/CIM


Overview

Dealing with context is a key factor in the conceptualization of human
experience, and a major issue for understanding natural language. It is
well known that some properties of objects and events may have different
cognitive salience according to their context of occurrence, thus
determining access to partial relevant information rather than to all
information. One typical example is that of an orange being passed between
two children, or the same orange peeled on a table: in the former case the
roundness prevails over other traits, and the orange is being used to play;
in the latter one, the edible features are those mostly conveyed by the
scene. Interpreting events poses contextual challenges as well: (in how
far) does a given event allow for different interpretations, like it might
happen for revenge/self defense? Similar selectional mechanisms underlie
figurative uses of word meanings, such as metonymy and metaphors among
others, that intrinsically characterize the interface between knowledge and
language.
Contextual access to objects and events needs to be further investigated,
shared conceptualizations and terminologies are needed, as well as more
robust approaches, including connections to domain and formal ontologies.
The design of ontological and linguistic resources that account for the
semantic phenomena involved in the contextual interpretation of objects and
events requires collecting information and devising context-aware
procedures.

In an era where most research is committed to statistical approaches, e.g.
vector representations of the linguistic context and neural architectures,
pairing the natural language semantic interpretation process and formal
ontology may improve the inferential capacities of artificial agents with
the explanatory power that is less relevant in those mainstream approaches.
Methods traditionally adopted to elaborate text documents exhibit
limitations in representing and processing objects and events. Many efforts
are being put in grasping text documents’ semantics based on semantically
shallow approaches, whilst natural language inference demands for deep
interpretation models, allowing to handle properties, functions, and roles,
among others, to deal with commonsense and to produce explanations.
A different approach relies on lexical information: several large-scale
lexical resources, such as WordNet (https://wordnet.princeton.edu),
BabelNet (http://babelnet.org), FrameNet (
https://framenet.icsi.berkeley.edu/fndrupal/), and ImagAct (
http://imagact.lablita.it/index.php?lang=en), among others, have been
proposed in the last few years and have been successfully employed to
bridge the gap between knowledge representations and natural language.
However, to cope with contextual access to objects and events involves many
additional features still lacking in such resources. Neither shallow
representations of NL semantics nor lexical resources alone provide
sufficient ground to account for contextual phenomena.

Relevant areas include, but are not limited to: events representation and
retrieval, event sequences, contextual features representation, trend
detection, knowledge discovery, word sense disambiguation, ontology
alignment, opinion mining and sentiment analysis, and conceptual
similarity, among others. All proposed approaches must address the issue of
representation of context, and suitable procedures to use context and
context aware meaning representations of objects and events. The ideal
submission should provide evidence that context improves the performance of
systems on real-world applications and/or provides useful insights and
explanations on systems’ output.


Topics of Interest

Research works submitted to the special issue should foster scientific
advances whether and to what extent objects and events representation and
processing can be linked to the context where they occur. The following is
a tentative list of relevant topics:

- theoretical foundations for the use of AI techniques to deal with context
and with changing/evolving objects and events;
- KR frameworks to represent mutable/evolving objects and events, including
formal ontologies, conceptual spaces and distributed representations;
- formal methods for reasoning in evolving scenarios;
- theoretical, methodological, experimental, and application-oriented
aspects of knowledge engineering and knowledge management centered on
events and evolving objects;
- use cases and application scenarios (e.g., in law, medicine) where
contextual information impacts on objects/events representation and
processing;
- linguistic approaches to context analysis;
- context-aware lexical resources to describe objects and events;
- context-aware topic and event detection and tracking, knowledge discovery;
- context-aware frame semantics;
- entity linking and  word sense disambiguation;
- representation of context in the Semantic Web;
- surveys on the adoption of contextual information in Cognitive Science,
NLP and Ontological Modeling;
- context-based explainable Artificial Intelligence.

Timeline

- Manuscript Submission Deadline: July 23rd 2018;
- Acceptance Notification: November 26th 2018;
- Final Manuscript Due: February 26th 2019.

Submission Guidelines

Submission guidelines can be found on the Journal Site,
https://www.iospress.nl/journal/applied-ontology/?tab=submission-of-manuscripts
This special issue welcomes original high-quality contributions that have
been neither published in nor submitted to any journals or refereed
conferences. Extended versions of (properly referenced) conference papers
should include at least 30% of new material. Please, clearly specify in the
cover letter that the paper is to be considered for the special issue on
"Meaning in Context: ontologically and linguistically motivated
representations of objects and events."

Guest Editors

Valerio Basile, Sapienza University of Rome, Italy, bas...@di.uniroma1.it
Tommaso Caselli, Rijksuniversiteit Groningen, The Netherlands,
t.case...@rug.nl
Daniele P. Radicioni, University of Turin, Italy, radic...@di.unito.it
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