=Paper=
{{Paper
|id=None
|storemode=property
|title=What do you Mean? Arguing for Meaning
|pdfUrl=https://ceur-ws.org/Vol-605/paper8.pdf
|volume=Vol-605
|authors=Tom van Engers, Adam Wyner
}}
==What do you Mean? Arguing for Meaning==
https://ceur-ws.org/Vol-605/paper8.pdf
What do you mean? Arguing for Meaning
Tom M. van Engers*, Adam Wyner °
*
Leibniz Center for Law, University of Amsterdam
º
University of Leeds
Abstract Building ontologies has been proven to be a complex issue in part
because a community must commit to the conceptualization that the ontology
represents. The community members must align their concepts and co-create.
Arguing about a useful conceptualization is therefore an essential part of the
process of designing an ontology. Logicians have developed formal argumentation
theories, but have not combined formal argumentation with conceptualization.
Rather, while conceptualization should play an important role in any
argumentation theoretical approach, argumentation theories focus on arguments
based on propositional logic and argument structures, which are not sufficient for
arguing about domain conceptualization, which requires a more fine-grained
logical analysis. In this paper we will explain why conceptualization plays an
important role within argumentation and why argumentation support tools,
especially if they use Natural Language Processing (NLP), can help in creating
domain ontologies.
Keywords: Argumentation, Ontologies, Knowledge Acquisition, Natural
Language Processing.
1. Introduction
Building ontologies has proven to be a complex issue in part
because a community must commit to the conceptualization that the
ontology represents. The community members must align their concepts
and co-create. Arguing about a useful conceptualization is therefore an
essential part of the process of designing an ontology. The creation of
ontologies is usually done in small teams as part of informal knowledge
engineering activities where participants discuss the conceptualization.
Except where a minority has discretionary power to define the concepts,
such a format is not suited for creating shared meaning between members
of a larger community. However, in practice, people can cope with the
task. For instance, where someone misunderstands, clarifying questions are
asked and explanations given. Thus, the shared conceptualisation emerges
from discussion; arguing about a useful conceptualization is an intrinsic
part of communication. While it is not always easy for human beings to
acknowledge and adjust to a different conceptualization, the problems of
detecting conceptual differences and creating reconceptualizations are
problems which are hard to solve in AI.
While one might expect that logicians working at formal theories
on argumentation would have addressed the problems of conceptualization,
thus far little attention has been paid to combining formal argumentation
with conceptualization. Instead, argumentation theories focus on arguments
LOAIT2010-Proceedings.tex; 26/06/2010; 13:46; p.87
88 T. van Engers, A. Wyner
based on propositional logic, which is not fine-grained enough to argue
about domain conceptualization.
Computational linguists have made significant progress in building
ontologies from sentences expressed in natural languages. In order to
address the hard AI problem of understanding natural language, researchers
in this domain usually work with controlled languages (CLS). A good
example of this approach is the Attempto Controlled English (ACE, see
http://attempto.ifi.uzh.ch/site/description/), which is used by a relatively
large number of computational linguists. Sentences expressed in ACE, i.e.
in a somewhat restricted subset of the English language, can be parsed into
first order logic (FOL) from which the ontology is derived.
One of the reasons to consider the interaction between natural
language, argumentation, and conceptualisation is that knowledge
engineers must translate from knowledge of a domain, often expressed in
natural language, into a representation that is argued about. However,
representing each sentence as a proposition hides crucial information that
would help to relate statements or the contents of statements, draw
inferences, filter redundancy, and identify contradictions.
In this paper we will illustrate why conceptualization plays an
important role within argumentation and why argumentation support tools
especially if they use Natural Language Processing (NLP) can help in
creating domain ontologies.
2. Using CNL for Policy-making Discussions
We work with a scenario in which we want to support stakeholders
to participate in policy-making discussions, using forum technology. For
this purpose the domain knowledge, i.e. knowledge about the issues being
discussed, must be made explicit, formal, and expressed in a language that
a machine can process. This machine-readable knowledge representation
we call the target form. Translating the knowledge that people have of a
domain, which is often implicit, informal, and expressed in natural
language, the source form, into the target form is a labour, time, and
knowledge intensive task (see also Van Engers 2005), creating a
“knowledge acquisition bottleneck” which has limited the adoption and use
of powerful AI technologies (see Forsythe and Buchanan 1993).
In Wyner et al. (2010) we propose and outline a framework which
extends multi-threaded discussion forums, integrating NLP, ontologies, and
argumentation. The proposed framework goes beyond existing debate and
argumentation support systems, by making the semantic content of the
stakeholders in the policy-making debate formal and explicit. In this paper
we will address the formalization rather than the construction of dialectical
arguments.
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What do you mean? Arguing for meaning 89
While there are tools which support multi-user ontology
development (see WebProtege http://webprotege.stanford.edu/) and there
are ontology development tools which use natural language for input (see
the AceWiki plug in for Protege http://attempto.ifi.uzh.ch/acewiki/), there
is no support for arguing in natural language about an ontology. Rather,
current ontology online multi-user systems such as WebProtege rely on the
users to converge on an ontology or note the differences. Our proposal
motivates the development of systems which not only captures the
differences, but represents them as distinct ontologies for reasoning.
Broadly speaking, among the issues that need to be addressed are
the following. Even if users enter in well-formed natural language
sentences, how can we be assured that they enter in well-formed,
meaningful rules for the formulation of arguments? Where we rely on
input from public participants, who are not logicians or knowledge
engineers with training in building well-formed rules, ill-formed arguments
could be entered. This raises a general issue of what prompts can be
introduced to make KB construction systematic and meaningful? For
instance, at the level of propositions there is nothing incoherent about a rule
such as If P and Q, then R. However, we see the rule is incoherent where P
is Bill is happy and Q is The Great Wall of China is long and R is Swallows
fly south in spring. Indeed, there is nothing preventing users from entering
ungrammatical sentences, or sentences that are out of topic of the context
of discussion. In the following we develop these issues.
One of the results and in some cases even one of the purposes of
argumentation is to clarify issues by finding a shared conceptualization
between the participants. Boer (in Boer 2009) citing Schlag (see Schlag
1996) stresses the importance of posing questions in (legal) arguments. He
uses the following rhetorical hierarchy guiding those questions:
1. Ontological questions question the truth of terminological axioms
and the ontological inferences based upon them.
2. Epistemic questions question the non-terminological inferences
made from certain premises to certain thesis.
3. Normative questions address whether something is allowed or
disallowed, good or bad etc.
4. Technical questions question the propositions of a case and are
about the truth of the facts of a case.
This strength of attacking arguments depends on the rhetorical
level, level 4 being the weakest and level 1 being the strongest attack.
In the following section we will explain some conceptualization
issues that are relevant to argumentation.
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90 T. van Engers, A. Wyner
3. Conceptualization issues in arguments
Participants involved in an argumentation process use natural
language as the most import means of expressing themselves. In order to
understand those expressions, the terms and syntactical information glueing
them together has to be transferred into a conceptual model. Where the
participants gradually come to understand one another, we have a process
of shared conceptualization (Van Engers 2001). The shared conceptual
model (ontology) only partly overlaps with the internal mental models of
the stakeholders, and making an explicit conceptualization is usually a
labour intensive task which requires lots of discussion because the
(intended) meaning of concepts depend on the role those concepts play in
the cognitive system of the individuals. Shared meaning has to be
construed, requiring a ‘rewiring’ in the minds of these individuals.
Mapping terms to a shared conceptualization can result in two typical
inferential problems. The first one is class-referential mismatch, and the
second is instance-referential mismatch.
An example of a class-referential mismatch is given in the
following example where we have the following arguments:
Argument 1 consists of three statements in natural language;
Statement 1. People need a healthy living environment.
Statement 2. Plants are responsible for considerable air pollution.
Statement 3. Therefore plants should be prohibited in living
environments.
Argument 2 also consists of three statements in natural language:
Statement 1. People need a healthy living environment.
Statement 2. Plants are responsible for regeneration of air.
Statement 3. Therefore we should have as many plants as possible
in living environments.
Obviously the interpretation of these arguments would be quite
different depending on what the concept would be that we want the term
‘plant’ to refer to.
An example of a instance-referential mismatch is the following.
Suppose we have the following two arguments:
Argument 1:
Sentence 1: John is rich therefore John is happy.
and a rebuttal
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What do you mean? Arguing for meaning 91
Argument 2:
Sentence 2: John has severe health problems therefore John is not
happy.
These arguments can be represented in the following AIF-graph:
Figure 1. An AIF graph representing two conflicting arguments with a
potential instance-referential mismatch. In this AIF-graph we’ll find four I-
nodes corresponding to
1. John is rich
2. John is happy
3. John has severe health problems
4. John is not happy
Obviously we expect that the John in all of these sentences refers to
the same instance (assuming that this is what most readers will infer). But
suppose that this is not the case and John in the first two I-nodes is
referring to a different instance. In that case the two S-nodes representing
the conflict between the second and fourth wouldn’t make sense. In order
to connect the I-nodes to the conceptualization we could use a mapping
function. This mapping function would map the I-nodes 1 and 2 in our
example to instance ‘John12’ and I-nodes 3 and 4 to John’34’. More
precisely we would have two situations -- a situation before it was clarified
that there are two Johns instead of one and the situation after this was
clarified.
In the first AIF-graph the nodes would be functionally mapped to
the same instance (John’12’). While in the second AIF-graph the I-nodes 1
and 2 in would be mapped to instance ‘John12’ and I-nodes 3 and 4 to
John’34’ and the S-nodes representing the conflict would be ‘undercut’
with a functional mapping to the ‘exclusion’ relation between John12 and
LOAIT2010-Proceedings.tex; 26/06/2010; 13:46; p.91
92 T. van Engers, A. Wyner
John34 in the conceptual model represented by the two sentences in our
example.
Another conceptualization mismatch is the caused by the properties
that individuals believe to belong to a concept. This problem could be
solved to either split the concept in two or more concepts. This can be
illustrated by the following example where we reuse the first argument of
our previous example,
Argument 1 consists of three statements in natural language:
Statement 1. People need a healthy living environment.
Statement 2. Plants are responsible for considerable air pollution.
Statement 3. Therefore plants should be prohibited in living
environments.
Argument 2 also consists of three statements in natural language:
Statement 1. Only some plants cause considerable air pollution.
Statement 2. Plants in living environments can help to reduce t
travelling distance to work.
Statement 3. Therefore non-polluting plants should be allowed in
living environments.
The second argument introduces a new concept (explicit in
Statement 3) that of the non-polluting plant, which will require the splitting
of the original concept plant into two concepts, one polluting plants, and
another that of non-polluting plants. The reader must have detected the
implicit argument in Statement 2 of the second argument that hides the
conceptual relationship between travelling to work and air-pollution.
Making this relationship explicit would require prompting in order to reveal
all deductive steps implicitly made by the individual that made the
statement.
The expressivity of AIF-graphs is intentionally limited to represent
argument structures and not the content of the constituents of the ‘I-nodes’.
But this is unfortunately also the case in most other argumentation
formalisation formalisms. Understanding the meaning of the arguments
however does require a mechanism that allows for connecting the I-nodes
to the corresponding conceptualization of the content of these I-nodes.
4. Conclusions and future work
In the IMPACT project we address argumentation in the context of
policy modelling, which is a challenge. Firstly the participants in policy-
making debates use natural language and understanding natural language is
a hard AI problem. Secondly the dialectical form of the argumentation
process may shift between different dialogue types (see e.g. Walton 1992).
LOAIT2010-Proceedings.tex; 26/06/2010; 13:46; p.92
What do you mean? Arguing for meaning 93
Persuasion dialogue, information-seeking dialogue, negotiation dialogue,
inquiry dialogue and sometimes even eristic dialogue can be mixed in such
dialogues. We therefore have to limit the dialogue form and the language
used, using a controlled language and a specific dialogue protocol in the
forum.
On the argumentation formalization side we have little support yet
either. The Dung framework (see also Laera et al. 2006) which we see as a
basis for many argumentation theories is not typically useful in the context
of policy making. In order to support the users in understanding the
arguments, or policies, we need to be able to grasp the meaning of their
expressions and give feedback about the consequences of their positions
and choices. For this kind of feedback we have to go beyond the fourth
level in the rhetorical hierarchy introduced in the section 3, i.e. the
technical questions. We claim that in order to really support policy-making
we need to be able to also cover the other rhetorical layers, up to
understanding the meaning of the propositions, which implies that we have
to formalise the participants’ expressions using at least in FOL. We intend
to further improve the NLP components as well as a component that can
prompt participants posing rhetorical questions, as well as critical questions
relevant to the argument (a plethora of papers on critical questions in
argumentative settings can be found on Doug Walton’s website see
http://www.dougwalton.ca/papers.htm).
In our approach we hope to bridge between ontology building and
argumentation theories which we believe is essential to both fields. As no
knowledge will grow without arguments, we hope that our research
contributes to more knowledgeable policy-makers and consequently to
better policy.
Acknowledgements
The authors wish to thank the European Commission for
sponsoring the IMPACT project.
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