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{\Large \bf An approach to building the hierarchical element of 
a lexical knowledge base from a machine readable dictionary.}\\
\smallskip
Ann Copestake\\
\smallskip
University of Cambridge Computer Laboratory\\
New Museums Site, Pembroke Street, Cambridge, CB2 3QG, UK\\
aac@cl.cam.ac.uk\\
\smallskip
June 1990\\
\bigskip
Paper presented at the First International Workshop on
Inheritance in NLP\\ 
Tilburg, August 1990.\\
\bigskip
ESPRIT BRA-3030 ACQUILEX WP No 008
\end{center}
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{\Large \bf An approach to building the hierarchical element of 
a lexical knowledge base from a machine readable dictionary\footnote{
This work was supported by Esprit BRA-3030.  Thanks to Ted
Briscoe, Nicoletta Calzolari and Piek Vossen for their comments on
various versions of this paper.}}\\
Ann Copestake\\
Computer Laboratory, University of Cambridge\\
June 1990
\end{center}

This abstract describes an approach to extracting taxonomies from
machine readable dictionaries and using them to structure a
lexical knowledge base which incorporates default inheritance.  
Taxonomy construction is based on an intuitive notion of the
organisation of the substantial quantities of data in machine readable
dictionaries which were developed for quite independent purposes.  Our
intention is to investigate how this affects, and is affected by, the
formal semantics of the knowledge representation for the lexical
knowledge base which we are attempting to create, especially with
respect to inheritance.

\section{Introduction}

The work described is part of the ACQUILEX project 
`The Acquisition of Lexical Knowledge for
Natural Language Processing Systems' the goal of which is 
to demonstrate the feasibility
of using the machine-readable versions of conventional dictionaries (MRDs) as
a source of lexical information for natural language processing (NLP) systems. 
We are investigating the extraction of lexical semantic information
from the definition fields of lexical entries.
In order to do this
we have both to define the type of lexical semantic information that NLP
systems require and also to determine what information we can
acquire (semi-)automatically. 
The information extracted has to be represented in a lexical knowledge
base in a form which
can be utilised by an NLP system. 
Our approach to lexical semantics will determine what
information we attempt to extract from dictionary entries, but the
content of the dictionaries themselves will in turn affect the 
theoretical approach, 
because we view dictionaries as linguistically useful corpora 
with determinable organisational principles which should be reflected
in the knowledge base.
However we are really only attempting to represent information that
may be derived from the definitions, not the definitions themselves.

The current work is a preliminary attempt to extract information from a
MRD and to partially define an appropriate knowledge representation.
We have initially attempted to extract taxonomies for three reasons.
Firstly taxonomic information appears to be a very basic form of
structure inherent in dictionaries.  Secondly there is a
need for inheritance of information in the approach to lexical
semantics which we are currently pursuing.  
The inheritance hierarchies required seem to have a structure
very similar to taxonomies.
Thirdly extraction of taxonomic information is known to be feasible;
there have been several
reports on extracting taxonomies from MRDs 
(eg Chodorow, Byrd and Heidorn(1985), 
Calzolari (1988)) and heuristics for
establishing the superordinate {\em genus term} in conventional dictionary
definitions are well known.  
A robust, pattern matching, parser for
LDOCE (Longmans Dictionary of
Contemporary English) definitions had already
been produced (Alshawi 1989).  However most
previous work on extracting taxonomies
has either required lengthy manual preprocessing 
to disambiguate word senses used  
in definitions or has relied on a user curtailing the process of
taxonomy building without giving detailed decision criteria for the
curtailment.  We have attempted to make sense-disambiguation
semi-automatic and to limit the decisions that must be made by the user.

The extracted taxonomies can be regarded purely as data structures; however
a hierarchical structure for a lexical knowledge
base, the LKB, can be derived from them.
Our proposal for a lexical knowledge representation 
shares much of the motivation behind DATR (Evans and Gazdar 1990), in
that it is compatible with unification-based grammar formalisms and
allows default inheritance.  However our current representation
language is considerably simpler than DATR.
The taxonomies are viewed as
defining a hierarchy down which inheritance can operate.  Nevertheless this 
gives a richer and more flexible system than
the sort hierarchies incorporated into the
Esprit ACORD system (Moens et al., 1989) and the SRI (Cambridge) CLE 
system (Alshawi et al. 1989), for example.
Our current proposal can be viewed as an augmentation of  
a standard graph-based unification formalism
(such as PATR-II) with minimal default inheritance (allowing only
`orthogonal' multiple inheritance (Touretsky 1986)).  
However since to some extent we
are representing real world knowledge it also seems desirable
to consider what the semantics of the hierarchical relationship
in terms of real world entities
might be. 

In the
next section 
I outline a method for semi-automatically extracting
sense-dis\-ambig\-uated taxonomies from LDOCE.  
LDOCE has two features which can be exploited to facilitate this
process; the use of a restricted core vocabulary in the definitions
makes parsing easier and restricts the possible non-leaf nodes in the 
taxonomies, and the semantic information encoded in box codes for
particular entries can be exploited for sense-disambiguation as
explained in Section~2.2.
In Section~3 I introduce a proposal for the
organisation of the LKB 
which describes how
it can be structured around an inheritance hierarchy derived from the 
taxonomies.  
Two potential applications
of the LKB are mentioned; in disambiguation and in the treatment of
``logical metonymy'' (Pustejovsky 1989).
I conclude with some preliminary remarks 
on the semantics of the hierarchical link.

\section{Semi-automatic creation of taxonomies.}

\subsection{The taxonomy creation program}

The notion of taxonomy that has been used in work on MRDs is
essentially an informal and intuitive one: a taxonomy is the network
which results from connecting headwords with the genus terms in their
definitions but the concept of genus term is not formally defined;
however for noun definitions, which are all I will consider here, it
is in general taken to be the syntactic head of the defining noun
phrase. 
The program described, in effect, implements this definition with the
further (incorrect)
assumption currently being made that a genus term is a single
lexeme (coordinated genus terms are discussed in Section~3.2).  

The taxonomy creation program operates in a top-down fashion; starting
from a given,
disambiguated word sense all the entries
in which this is used as the genus term are found, and the program
then recurses on the word sense corresponding to each of these
entries, terminating on word senses which are never used as genus terms.
So starting from {\bf liquid 2 (1)}, {\bf oil 1 (1)} is one of the
(large number of) entries found;
this in turn is the genus
term in entries such as {\bf paraffin 1 (1)}.  {\bf Paraffin 1 (1)} 
is not used as a
genus term in any entries, so the process bottoms out at this point,
and {\bf paraffin 1 (1)} will be a leaf node in the taxonomy.

The parser retrieves the {\em class} from definitions of individual
word senses; this could be lexically
ambiguous with respect to both homonyms and subsenses of the genus term.
Most of our experiments have been done on definitions of
nouns --- for these the class will normally be the syntactic head of
the noun phrase, the exceptions being definitions which include
phrases such as ``a kind of X'' where the class assigned will be X
rather than ``kind''.  For details of the parser see Alshawi (1989).
Finding the entries with a given genus term is thus a two stage process; 
first all the entries with the appropriate class will be found 
and then these
are filtered by disambiguating the class.
For example, in order to find the entries
where {\bf liquid 2 (1)} is used as the genus term, the entries which have
``liquid'' as a class are first retrieved and these are then examined
to attempt to determine whether {\bf liquid 2 (1)} is the sense being used,
as opposed to one of the adjectival senses under {\bf liquid 1}, or the
technical sense {\bf liquid 2 (2)} ``either of the consonants r or
l''.  A fragment of the taxonomy derived starting from {\bf liquid 2
(1)} is shown below.

\small
\begin{tabbing}
liq\=liq\=liq\=\hspace{35mm}\=liq\=liq\=liq\=\hspace{35mm}\=liq\=liq\=liq\=\kill
liquid 2 (1)\>\>\>\>\>\> beer 1 (1)\>\>\>\>\> mild 2 (1)\\
\> drink 2 (1 2)\>\>\>\>\>\> ale 1 (1) \>\>\>\> pale ale 1 (1)\\
\>\> absinth 1 (1)\>\>\>\>\> barley wine 1 (1)\>\>\>\> porter 3 (1)\\
\>\> anis 1 (1)\>\>\>\>\> bitter 3 (1)\>\>\>\> stingo 1 (1)\\
\>\> aperitif 1 (1)\>\>\>\>\> bock 1 (1)\>\>\>\> stout 2 (1)\\
\>\> applejack 1 (1)\>\>\>\>\> home brew 1 (1)\>\>\>\> wallop 1 (2)\\
\>\> arrack 1 (1)\>\>\>\>\> lager 1 (1)\>\>\>beer 1 (2)\\
\>\> barley water 1 (1)\>\>\>\>\> light ale 1 (1)\>\>\> beer 1 (3)
\end{tabbing}
\normalsize

Disambiguation is essential in order to produce a coherent taxonomy
and to allow termination of the taxonomy building process before it
potentially includes all the entries in the dictionary.
For example, one of the entries which has {\bf liquid} 
as a class is {\bf spirit 1 (13)}.  
If the program recursed on {\bf spirit} 
and did not do the
sense discrimination (or did not do it correctly)
entries such as {\bf devil} and {\bf ghost} would be retrieved.  
These in turn would give rise
to further problems of the same kind.
 
In the case of {\bf liquid},
disambiguation will be quite straightforward; the
adjectival homonym is excluded since the taxonomy is restricted to
nouns because of the convention that a
sense is defined by a phrase of the same part of speech (so 
the genus
terms in definitions of nouns will always be nouns, for example).  
A set of (weighted) heuristics is used to find the ``best match'' among the
possibilities with the same syntactic category as the potential genus
term --- in the example {\bf liquid 2 (1)} and {\bf liquid 2 (2)} are
thus the only candidates.

The current set of heuristics rely partially on a feature particular
to LDOCE --- most entries have a {\em box code} in the MRD which encodes 
a certain amount of semantic information.  In particular one part of
the box code contains codes such as L for
liquid, H for human, T for abstract.  One heuristic is based on
whether the value of this code for the potential genus sense is
compatible with the code for the entry.  For example, 
the codes for {\bf oil 1 (1)} and {\bf liquid 2 (1)} are both L,
but that for {\bf liquid 2 (2)} is T, which is not compatible with L.
Another part of the box code  
specifies whether a sense is slang, informal,
technical and so on.  Slang and informal senses in particular are very
unlikely to be used as genus terms.
Another heuristic is simply that lower numbered senses are more likely
to be used as genus terms, because the ordering of senses tends to reflect
their relative frequency/typicality (although there are many
exceptions).
A fourth heuristic attempts to determine the degree of overlap between 
the uninflected forms of the words in the definitions, 
to provide some measure of semantic ``closeness''.

Such sense disambiguation is, of course, not completely reliable. So
taxonomy derivation is semi-automatic --- the user is asked
confirm or override the important choices, those concerning the 
entries which might be non-leaf nodes in the taxonomy.
(In our tests so far this has resulted in the user being consulted 
on about 5\% of the entries.)
This is enough to prevent spurious paths being followed, furthermore
an incorrect decision about a leaf node cannot affect the final
taxonomy too greatly.
The user also has the option to specify that the parser has assigned the
class term incorrectly.  Such yes/no decisions are normally fairly
quick and easy to make.  The only other decision that the user has to
make is whether to merge subsenses which might 
be too difficult to disambiguate.
For example 
{\bf animal 1 (1)} and {\bf animal 1 (2)} might be amalgamated because 
{\bf animal 1 (2)} is
defined as the group of {\bf animal 1 (1)} excluding humans.  (The new
sense is specified
as {\bf animal 1 (1 2)}.)
\footnote{Merging of senses has been avoided wherever possible in our
experiments so far but will be necessary, so
we need to define more stringently when and where it is justified.}

\subsection{Test results}
The program has mainly been tested by building two substantial 
taxonomies, one starting with
{\bf animal 1 (1 2)} and one with {\bf substance 1 (1)}.  
The animal taxonomy includes about 700
words senses, of which only about 30 are non-leaf nodes and
has a maximum depth of 6;
the substance taxonomy has about 1000 senses with 50 non-leaf nodes
and a maximum depth of 5.  Taxonomies derived from LDOCE would be
expected to have a greater
percentage of leaf nodes than those from other dictionaries because of
the restricted core vocabulary.  

Together these taxonomies cover about 7\% of the noun
entries in LDOCE yet it took less than 
two hours of interaction to derive them both.
Assuming that these examples are fairly representative it seems 
that it will be possible
to construct taxonomies containing the majority of noun senses
relatively quickly.  An important side effect of the process of
taxonomy building is the disambiguation of the genus terms in the
definitions; we expect to be able to exploit this information when
parsing entries to extract other information.

No distinction is made in these taxonomies between mass nouns, group
nouns and singular count nouns or between senses which seem to correspond to
natural kinds and those which do not.
Although it is clear that some of this information can be extracted
from the entries we currently ignore it in producing the taxonomies.
I do not think that there should be separate taxonomies for
count, mass and group nouns.  For example, in definitions of the form
``a group of X'' we take X to be the genus term rather than {\bf group}. 
Although the information that something is a group term, for example, should be
represented in the LKB, it seems inappropriate to represent it in the
taxonomy.
To do this extra link types would have to be invented, but this seems
likely to result in the
imposition of a structure on the entries which is not really there
because it complicates the 
concept of genus term as accepted by lexicographers.
Representation can really only be considered with respect to a
particular linguistic treatment. 
I developed an approach to the semantics of mass terms and plurals 
in Copestake (1990a) (after Krifka 1987)
where singular, group and mass entities have the same 
model-theoretic status and the distinctions between them are expressed
using a classifier-like construction, which seems to justify the use of
a single taxonomy (this is considered in more detail
in Copestake (1990b)). 

Although there were some erroneous inclusions in the taxonomies due to the
sense discrimination heuristics failing, most of these were relatively
unimportant, because they were failures to discriminate between senses
which were fairly similar.  An exception was the erroneous inclusion of 
6 senses such as {\bf crew 1 (3)} 
under {\bf team 1 (1)} in the
animal hierarchy.  {\bf Team 1 (1)} is defined as ``2 or more animals
drawing the same vehicle'' whereas the group of people sense is {\bf team 1
(2)}. 
However this sort of error is very easy to spot, because there are a
group of incorrect words --- manual checking/editing of the complete
taxonomy to correct this sort of error would be relatively easy.

The problems caused by Alshawi's parser and grammar failing to
identify the class term in the definition correctly were much more serious.
Up to 20\% of the senses which were assigned class ANIMAL should have
been assigned some other class and over 150 word senses were omitted
because the class assignment was missing or 
wrong\footnote{We checked for omissions by retrieving all 
the entries with the
appropriate box codes for animals, but this will not be
possible in general.}
Many of these cases were in definitions where there was a conjunction
eg ``a person or animal who \ldots'' which the grammar does not cover.
Such definitions raise questions about the structure
of the taxonomy which are discussed in section~3.2.  In other cases
there was structural ambiguity and the (single) result returned was incorrect:
\begin{description}
\item[armadillo] ``a small animal native to the warm parts of the
Americas'' --- was assigned class NATIVE.
\end{description}
We are currently attempting to build a more sophisticated parser for
LDOCE definitions, which should solve some of these problems.

There are problems which are inherent in the dictionary entries.
There are cycles in the definitions.  For example
{\bf animal 1 (1)} is defined as ``a living creature
\ldots'' and {\bf creature 1 (1)} is defined as ``an animal of any
kind''.
Because the taxonomy was produced starting from {\bf animal} this
does not cause any serious problems;
{\bf creature 1 (1)} is included in the hierarchy under 
{\bf animal 1 (1 2)}, recursion on
{\bf creature 1 (1)} 
happens as normal except that the cycle is detected and noted.
A more
serious problem is when the cycles result in words being excluded
altogether --- for example {\bf cow} is defined in terms of {\bf
cattle} and {\bf cattle}
in terms of {\bf cow} --- about 17 word senses were excluded 
from the animal taxonomy because of this.
In general this could only be detected by creating taxonomies which
covered the majority of the entries 
and then determining which class words had not been incorporated.
 
\section{Structuring the lexical knowledge base with taxonomic information}
\subsection{The knowledge representation}

One view of the LKB is as a lexicon with links
between partially specified entries.   Inheritance of information
about particular attributes
along these links results in complete entries which could then be regarded as
independent entities equivalent to entries in a normal lexicon.
A conventional lexicon produced by expanding all the entries 
at once with the inherited information would probably be
unmanageably large --- the point is that the links are only seen as 
providing for inheritance.
We currently represent the attribute structure for
individual entries as labelled DAGs, giving expanded entries similar to those
used in PATR-II.
I will initially concentrate on this view of the LKB; the definition
of inheritance
in such a system is analogous to that in an object
oriented programming language --- it specifies what attributes an LKB
entry will have but does not constrain its interpretation.
However it is also
possible to see at least some of the contents of the LKB as real world
knowledge which should be given a semantics in terms of real world
entities; some preliminary remarks about the semantics of the
links in these terms are made in section~3.3.

The LKB, for noun senses at least,
will contain links derived from the taxonomies, that organise the
entries as a tree-structured hierarchy.  I will refer to this as an
IS-A hierarchy, to distinguish it from other possibilities such as a
PART-OF hierarchy, but I am using IS-A in a somewhat broader way than
usual because mass and group terms will be included in the hierarchy.
The earlier assumption of a unique genus sense means that our
taxonomies in general will be
tree-structured (exceptions are discussed in 3.2).  
This is a very useful property 
because it gives a unique ordering for a
default mechanism; we have therefore assumed that the IS-A hierarchy can be
tree-structured.
The problems of allowing defaults in systems which incorporate
multiple inheritance are well known (the
so-called ``Nixon diamond'' is an example).  Since it greatly
simplifies our task we start from the assumption that only
orthogonal multiple
inheritance will be allowed (see Touretzky 1986).
Thus if we do add other relationships such as PART-OF, any information
inherited along PART-OF links would not be allowed to conflict with
that inherited
along IS-A links.  Under these conditions 
inheritance has been formalised in a
variety of ways (Touretzky 1986, see also Evans and Gazdar 1990); 
since the appropriate behaviour seems
uncontroversial I will not discuss it further here.

One straightforward use of the LKB is to allow features such as
ANIMATE to be represented for use by a parser which incorporated
selectional restrictions, for example.  Such features
could be associated either manually or semi-automatically
with the LKB entries corresponding to the nodes
towards the top of the taxonomy; the more specific word senses would
then inherit them.  
However
the approach to lexical semantics which we are currently pursuing
requires the inheritance of a much richer range of
attributes.
For example the treatment of logical metonymy adopted in 
Briscoe, Copestake and Boguraev (1990) 
allows the coercion of sentences such as: 
\begin{quote}
John enjoyed the autobiography.
\end{quote}
into the equivalent of:
\begin{quote}
John enjoyed reading the autobiography.
\end{quote}
(in an informationally impoverished context).  
Assuming that the information that the
default predicate associated with {\bf book 1 (1)} is {\bf read 1
(1)} has been acquired in some manner (currently manually, 
possibly eventually through analysis of
dictionary definitions or corpora) this information would
then be inherited by the entry for {\bf autobiography}.
Entries lower in the
taxonomy may override the inherited information; thus  
although {\bf telephone directory}
is under {\bf book 1 (1)} in the hierarchy the reading event should
be cancelled by more specific information about the purpose of
telephone directories.

We might represent this sort of information in the LKB as follows: 
\begin{tabbing}
book 1 (1) \hspace{20mm} \= [ \=default: purpose : read 1 (1) \\
\>\> animate: false ] \\
autobiography \> [ IS-A: book 1 (1) ] \\
telephone directory \> [ IS-A: book 1 (1)  \\
\>\>default: purpose : refer to 1 (2)] 
\end{tabbing}
The IS-A links are to be interpreted as allowing
inheritance, once this has occurred the expanded entry can be regarded
as a normal entry in a lexicon.  
{\bf autobiography} would thus have the same values as 
{\bf book 1 (1)} for both attributes.
{\bf telephone directory} will inherit the value false for the 
attribute animate but the value of its purpose attribute
overrides that inherited from {\bf book 1 (1)} --- the
assumption is that failure of ordinary unification 
will always lead to the information associated with the node lowest in
the hierarchy being retained, regardless of any possible relationship
between the values.

\subsection{Constructing an IS-A hierarchy from taxonomies}

In order to build a single hierarchy, the 
taxonomies derived by the methods described in the previous section
would have to be connected at the top.
This would have to be
done by hand, since dictionary definitions for very general terms such
as {\bf substance} and {\bf animal} are not very helpful; to some
extent at least the top nodes will be theory-dependent 
and will not correspond directly to word senses. 
However the dictionary generated taxonomies can be expected to
constrain this top level structure.
The cycles that exist in the definitions should be
removed in some way.  This could be done by making a (possibly
arbitrary) decision to break one link or by specifying that the two
senses are synonyms (and therefore occupy the same position
in the taxonomic ordering and have compatible values for the relevant 
attributes). 

Other researchers have assumed that a tangled hierarchy is needed to
represent the taxonomy (see  
Chodorow, Byrd and Heidorn 1985). 
In most cases this is because senses have not
been disambiguated.  
The other cases are entries with coordinated genus terms.
As mentioned earlier the current parser does not handle these but our
intention is to represent them in the taxonomy as structured rather
than atomic nodes.  Representation in the LKB depends on the conjunction.

For example, coordination with ``or'' such as in:
\begin{description}
\item[hunter 1 (1)] a person or animal who hunts \ldots
\end{description}
is very common in LDOCE.
In some definitions it seems as though two word senses are being
combined; in order to represent {\bf hunter 1 (1)} in a
tree-structured hierarchy
the sense could be split:
\begin{description}
\item[hunter 1 (1a)] a person who hunts \ldots
\item[hunter 1 (1b)] an animal which hunts \ldots
\end{description}
but this does not really seem justified.
Clearly having a tangled hierarchy does not solve the problem;
{\bf hunter 1 (1)} cannot have 
both {\bf person} and {\bf animal} as parents in any inheritance
hierarchy
because it would then be given
the union of their attributes --- it would be a chimera
of person and animal. 
Instead {\bf hunter 1 (1)} should inherit the intersection of the
attributes of {\bf person} and {\bf animal} --- this might best be
implemented by specifying an LKB entry
{\bf person or animal}\footnote{In this
case {\bf person or animal} actually corresponds to the word sense {\bf
animal 1 (1)} but in general we would need a small number of nodes in the
IS-A hierarchy that did not correspond to particular word senses.}
since this would apply to over 70 definitions.

In contrast, in order to correctly represent symmetric
coordination by ``and'' in
the genus term, multiple inheritance would be required.
However there are relatively few examples of genus terms 
containing ``and'' in the noun definitions 
and all 
those which I am aware of seem to
involve a temporal use --- for example:
\begin{description}
\item [montage] the choosing, cutting and combining together of
separate photographic material~\ldots
\end{description}
We do not currently have any way of adequately representing this in the LKB
because we have only been considering the representation of static information,
but simple multiple inheritance from the conjuncts
is clearly not what is required.

\subsection{Inheritance and the semantics of the IS-A link}

Although it is appropriate to describe the semantics of a lexicon
in terms of how attributes are
inherited, without constraining the interpretation of the entries,
this seems inadequate for the LKB.  The taxonomic information that we are
attempting to represent involves real world knowledge; furthermore,
since we are extracting it automatically from MRDs, we ought to attempt
to justify its use and the inheritance behaviour.  The following
is discussed in more detail in Copestake(1990b).

The simplest possibility is that the IS-A link in the LKB should be
interpreted as specifying that a subset relationship exists between
the extensions of the words senses, for example:
\[ {\rm autobiography}\ {\rm ISA}\ {\rm book 1 (1)} \equiv
\lbrack\!\lbrack {\rm autobiography} \rbrack\!\rbrack \subseteq 
\lbrack\!\lbrack {\rm book 1 (1)} \rbrack\!\rbrack \]
In good lexicographic practice the genus term should
``pinpoint that property of a thing that
is viewed by most speakers as being essential to it'' (Landau 1984).
Thus we might expect that the subset relationship is (usually) entailed
by the taxonomic relationship (whatever its actual semantics are).
Our
results so far are mostly consistent with such an interpretation.

If this interpretation of the IS-A link in the LKB is adopted then
inheritance of necessary properties will not be overridable.  If it is
necessarily the case that animals are animate then everything below
animal in the hierarchy must inherit the property animate.  Default
inheritance is needed to allow inheritance of default properties.
A possible way to formalise this involves making use of
Delgrande's (1988) default logic.  This allows the representation of
statements such as:
\begin{quote}
$ \Box (x) [ {\rm book(1) 1}(x) \supset \neg {\rm animate}(x) ]$\\
$ \Box (x)({\rm autobiography}(x) \supset {\rm book(1)1}(x))$\\
$ (x)({\rm book(1)1}(x) \implies {\rm purpose}(x, {\rm read 1 (1)}) )$
\end{quote}
(Autobiographies are necessarily books,
books are necessarily not animate,
normally the purpose of books is to be
read.)
From this it follows that:
\begin{quote}
$ \Box (x) [ {\rm autobiography}(x) \supset \neg {\rm animate}(x) ]$
\end{quote}
but more interestingly one of Delgrande's approaches to default
reasoning would involve concluding by default:
\begin{quote}
$ (x)({\rm autobiography}(x) \implies {\rm purpose}(x, {\rm read 1 (1)}) )$
\end{quote}
This seems to give a justification for the LKB's proposed
default inheritance mechanism which would link in with a mechanism for
reasoning about particular individuals by default.

There are definitions which do not straightforwardly fit into this
approach.
For example:
\begin{description}
\item[water 1 (1)] the most common liquid \ldots 
\item[ice 1 (1)] water which has frozen to a solid \ldots
\end{description}
We could represent this in the LKB as:
\begin{tabbing}
liquid 2 (1) \hspace{20mm} \= [ \=state: liquid 1 (1) ] \\
water 1 (1) \> [ composition: water 1 (1)\\ 
\>\>   IS-A: liquid 2 (1)]\\ 
ice 1 (1) \> [ state: solid 1 (1)\\
\>\>  IS-A: water 1 (1) ]
\end{tabbing}
and allow the attribute state to be overridden by more specific
information.  
But this implies that {\bf liquid 2 (1)} is to be
interpreted as something that is by default liquid.  I don't actually find this
unreasonable but we then have to explain why:
\begin{quote}
Ice is a liquid.
\end{quote}
seems false.  Giving a real world semantics to the IS-A link
makes such problems apparent, and may give us some
insight into the nature of the taxonomic relationship; these examples
tend to suggest that our
implicit assumption of a static taxonomy is problematic, for example.

However we believe that the correct approach is not to speculate about possible
solutions or to suggest a more complex approach at this stage 
but to derive further taxonomies and to attempt to classify the
problems before refining our approach.  
Since we are primarily attempting to
specify the semantics of the LKB and not that of the taxonomic
relationship, if we can identify the definitions that cause particular
problems it may be possible to circumvent them in the LKB by, for
example, introducing entries that do not directly correspond to
particular word senses, although this is obviously something to avoid
if possible.  The frequency and nature of the exceptions would
determine whether we do this or modify the semantics.

\section{Conclusion}
The work described is at a very preliminary stage.
We intend to extract more taxonomies and to build an LKB which is
adequate for representing large amounts of information. 
Exactly what attributes 
we attempt to extract and represent will depend on the
problems that we tackle as part of the ACQUILEX project.

Besides the issues mentioned here concerning the 
representation of mass and group terms, the
semantics of the
hierarchical relationship in the LKB, and of the taxonomic relationship itself 
there are a variety of other questions
connected with the extraction of taxonomic information which will have
to be addressed eventually;
for example the way in which
sense distinctions are and should be made (see Vossen 1990), 
and the status of natural kinds in the taxonomies.
The use of a large knowledge base representing information extracted from MRDs 
will be a fundamental part of our approach to these issues.    

\section*{References}
\newcommand{\book}[4]{\item #1(#4) {\it #2,} #3}
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\newcommand{\boarticle}[5]{\item #1(#5) `#2' in {\it #3,} #4}
\newcommand{\farticle}[6]{\item #1(#6, forthcoming) `#2' in #4 (eds.), {\it #3,} #5}
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{\it #3}}
\newcommand{\jarticle}[6]{\item #1(#6) `#2', {\it #3,} {\it vol.#4,} #5}
\newcommand{\particle}[6]{\item #1(#6) `#2', {\it Proceedings of the
#3,} #4, pp.#5}

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    \itemindent -\leftmargin
    \itemsep 0pt plus 1pt
    \parsep 0pt plus 1pt}
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\end{document}






