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[Gfo-users] Third and Final Call for Paper: Special Issue of the Journal

From: daniele radicioni
Subject: [Gfo-users] Third and Final Call for Paper: Special Issue of the Journal of Applied Ontology "Meaning in Context: ontologically and linguistically motivated representations of objects and events."
Date: Mon, 11 Jun 2018 11:40:24 +0200

(Apologies for cross posting)

Special Issue of the Journal of Applied Ontology "Meaning in Context: 
ontologically and linguistically motivated representations of objects and 


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 (, BabelNet 
(, FrameNet (, 
and ImagAct (, 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’ 

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.


- 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,
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 

Guest Editors

Valerio Basile, Sapienza University of Rome, Italy, address@hidden
Tommaso Caselli, Rijksuniversiteit Groningen, The Netherlands, address@hidden
Daniele P. Radicioni, University of Turin, Italy, address@hidden

: : : : : : : : : : : : : : : : : : :

Daniele Radicioni, PhD
Department of Computer Science
University of Turin
Corso Svizzera, 185
10149 - Torino
phone: +39 011 6706802
fax:   +39 011 751603

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