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Referring Expressions in Artificial Intelligence and Knowledge Representation Systems

The tutorial introduces the audience to the concept of referring expressions, formulae that can be used to communicate identities of otherwise abstract objects. The formalism provides foundations for a successful and unambiguous exchange of information about individuals between agents sharing common knowledge about such individuals, a task that is indispensable in most modern applications of knowledge representation and semantic technologies.

Location and Time:ECAI 2024 in Santiago de Compostela (time and location TBA).
Tutorial Slides: refexp-tutorial.pdf (to be updated)

Tutorial Synopsis

A referring expression in linguistics is any noun phrase identifying an object in a way that will be useful to interlocutors. In the context of knowledge representation and information systems, constant symbols occurring in an underlying knowledge base are the artifacts usually used to identify a subset of the objects for which the knowledge base captures knowledge.

This tutorial explores how objects that can be usefully identified can be extended by allowing more general expressions in the underlying language of the knowledge base, called singular referring expressions, to replace constants as syntactic identifiers of such objects.

Expanding the possibilities of identifying (possibly implicitly defined) objects serves numerous purposes, ranging from allowing query answers to contain additional tuples (which are typically eliminated due to lack of constant symbols denoting components of such tuples), to answers that are more informative, to decisions on how to communicate references to objects among various cooperating agents, and to identification issues related to physical data representation in computer storage (such as relying on addresses in main-memory databases).

The idea of referring expressions itself circumvents the need for artificially-invented identifiers that are commonly opaque to the user that interacts with the knowledge base.

Goals of the Tutorial

  1. to review the history of approaches to identifying individuals/objects, in particular in the area at the intersection between logic-based approaches to knowledge representation and the areas of natural languages and philosophy, approaches that can be traced back to Russell, Frege, and Strawson, and
  2. to present a modern unifying approach to referring expressions based on logical methods in AI and Knowledge Representation with clear and direct applications to many practical areas of AI and Computer Science in general.


Detailed Outline

Audience and Background

The topics covered in the tutorial are of interest to wide range of AI researchers and to members of the general public with an interest in knowledge representation. In particular, the tutorial targets the following groups:

The tutorial assumes the audience is familiar with the basics of first order logic and of conceptual modelling formalisms (such as ER or UML) at the introductory university course level. No knowledge of particular ontology/KR languages such as description logics and other formalisms is assumed.

Relevance to ECAI 2024

The tutorial focuses on foundational issues relating to object identification in knowledge bases, ontologies, and information systems based on ontologies, and on how such issues can be comprehensively addressed. Since every design of an information system faces decisions relating to how external entities will be identified within such a system (in addition to representing various properties of such entities), a general approach to this problem is of interest to ontology developers/engineers and data scientists. Interestingly, the approach to object identification discussed in the tutorial naturally and seamlessly complements standard approaches in conceptual and ontology design methodologies. The tutorial is thus of interest both to researchers in knowledge representation and to practitioners in the wide area of information management.


About the Authors

Dr. David Toman and Dr. Grant Weddell are professors of Computer Science at the University of Waterloo, Canada. They have published and presented results in the area of knowledge representation over the last 20 years at premier AI conferences (including a Reiter Prize at KR 2010 and Best Paper Prize at ISWC 2013); Dr. Toman has also given tutorials in the area of temporal representation and reasoning and temporal databases and information systems that has led to an invited chapter in the Handbook of Temporal Reasoning in Artificial Intelligence.

Presenters' Background in the Area of the Tutorial

The authors, together with Alexander Borgida (Rutgers), have introduced referring expressions in the area of Knowledge Representation and Ontology-based Data Access and were awarded the Ray Reiter Best Paper prize at KR 2016 for this work. They have extended this work to the area of conceptual modelling and other areas connected with ontological reasoning and knowledge representation. Subsequently, with their coauthors, they were awarded the 2018 Bob Wielinga Best Paper Award for the paper furthering the use of referring expressions in conceptual modelling.

The authors have recently presented tutorials on the topic of referring expressions in knowledge representation and information systems:

Moreover, as Professors of Computer Science in one of the top-ranked CS programs, the authors have been lecturing on both the graduate and undergraduate level for more than twenty years each on topics ranging from introductory lectures on Logic, to specialized graduate lectures on Description Logic and Knowledge Representation, and to senior-year lectures on Programming Languages and on Database Systems Implementation.


Name: David Toman and Grant Weddell
Affiliation: Cheriton School of Computer Science, University of Waterloo
Address: 200 University Ave W., Waterloo, ON N2L3G1, Canada
E-mail: {david,gweddell}@uwaterloo.ca
WWW: cs.uwaterloo.ca/~{david,gweddell}


Resources and Bibliography