=Paper=
{{Paper
|id=None
|storemode=property
|title=A Representation Language for Describing and Managing Elementary/Complex Events in a Non-Fictional Narrative Context
|pdfUrl=https://ceur-ws.org/Vol-624/paper2.pdf
|volume=Vol-624
}}
==A Representation Language for Describing and Managing Elementary/Complex Events in a Non-Fictional Narrative Context==
https://ceur-ws.org/Vol-624/paper2.pdf
A Representation Language for Describing and
Managing Elementary/Complex Events in a Non-
Fictional Narrative Context
Gian Piero Zarri1
1
University Paris-Est, Créteil, Val de Marne (UPEC), LiSSi Laboratory, 120-122 rue Paul
Armangot, 94400 Vitry-sur-Seine, France
gian-piero.zarri@u-pec.fr, zarri@noos.fr
Abstract. Making reference, mainly, to the “non-fictional narratives” domain,
we will suggest, firstly, an operational definition for highly ambiguous terms
like “elementary events” and “complex events”. We will then raise the problem
of how to represent these events/complex events in a computer-suitable form.
We will introduce therefore a language, NKRL (Narrative Knowledge
Representation Language), expressly specified and implemented for dealing
with narratives and temporal information. Afterwards, we will show briefly
how this language can be used for questioning and inferencing operations on
knowledge bases of “events” formalised according to the NKRL approach.
Keywords: Elementary events, complex events, knowledge representation,
querying and inferencing.
1 Introduction
“Event-based” tools and systems seem to be particularly popular today. They range
from highly formalised systems like event algebras [1] and event ontologies [2], to
practical applications like, e.g., narrative-based video annotation and editing [3], the
design of event-driven software architectures integrating actuator and sensor networks
[4], or the use of an event-centric approach for modelling policy decision-making in a
business process context [5]. For the great majority of these tools and systems,
however, the term “event” seems to denote simply a sort of ‘primitive’ or ‘intuitively
understood’ notion that does not ask for any sort of definition. When this last is given,
it is often limited to basic statements like “something that happens at a given place
and time”, “something that takes place”, “perduring entities that unfold over time” or
– in the glossary of terms of the Event Processing Technical society – “anything that
happens, or is contemplated as happening”.
In the framework of the NKRL project (NKRL = Narrative Knowledge
Representation Language), see [6, 7, 8, 9], we have now found satisfactory
definitions, even if largely pragmatic and operational, for basic essential notions like
those of “events” and “complex events”. These definitions have been derived from
work concerning the proper NKRL domain, i.e., the representation and management
of non-fictional narrative information (or non-fictional narratives) – even if, as we
will show below, extensions to other domains dealing with the notion of ‘event’ are
certainly possible.
In the following, we will first explain informally, Section 2, what is denoted by
terms like “elementary/complex events” in a narrative/NKRL context. We will then
show, Section 3, how these informal notions can be translated into precise formal
representation structures that constitute the gist of the NKRL language. Section 4 will
supply some details about NKRL and will include two sub-sections, the first devoted
to an outlook of the NKRL’s knowledge representation techniques and the second to
the querying/inferencing procedures. Section 5 will consists in a short “Conclusion”.
2 Narratives, elementary and complex events
‘Narrative’ information concerns the account of some real-life or fictional story (a
“narrative”) involving concrete or imaginary ‘characters’. In a NKRL context we are
mainly concerned with “non-fictional narratives”, like those typically embodied into
corporate memory documents (memos, policy statements, reports, minutes etc.), news
stories, normative and legal texts, medical records, many intelligence messages,
surveillance videos, actuality photos for newspapers and magazines, material (text,
image, video, sound…) for eLearning, Cultural Heritage material, etc. We can note
that this choice is only due to very practical constraints – to profit, e.g., from the
financial support of the European Commission – and, as it will appear clearly in the
following, nothing (at least in principle) could prevent us from dealing with the whole
“Gone with the wind” fictional-narrative novel according to an NKRL approach.
More precisely, we assume that (fictional or non-fictional) narratives correspond
to the basic layer, the “fabula (a Latin word: fable, story, tale, play) layer”, introduced
by Mieke Bal [10] in her crucial work on the structures of narrative phenomena.
Accordingly, an (NKRL) narrative consists then in a series of logically and
chronologically related events (a ‘stream of elementary events’) that describe the
activities or the experiences of given characters. From the above, we can immediately
deduce that a narrative coincides, in practice, with a “complex event” – see also [8].
From this definition and other work in a “narratology” context – an introduction to
this discipline can be found in [11] – we can infer some important characteristics of
“narratives/complex events”, see [9: 2-13] for a more detailed discussion –
independently, once again, by any ‘fictional/non fictional’ consideration:
• One of the features defining the connected character of the elementary events that
make up the stream concerns the fact that these are chronologically related, i.e.,
narratives/complex events extend over time. This diachronic aspect of
narratives/complex events (a narrative normally has a beginning, an end and some
form of development) represents indeed one of their most important characteristics.
• Space is also very important in the narrative/complex events domain, given that the
elementary events of the stream occur generally in well defined ‘locations’, real or
imaginary ones. The connected events that make up a narrative/complex event are
then both temporally and spatially bounded. Bakhtin [12] speaks in this context of
“chronotopes” when drawing attention on the fact that time and space in narratives
are strictly interrelated.
• A simple chronological successions of elementary events that take place in given
locations cannot, however, be defined as a ‘narrative’ (a complex event) without
some sort of ‘semantic coherence’ and ‘uniqueness of the theme’ that characterise
the different elementary events of the stream. If this logical coherence is lacking,
the elementary events pertain to different narratives: a narrative can also be
represented by a single ‘elementary event’.
• When the constitutive ‘elementary events’ of a narrative/complex event are
verbalized in NL terms, their ‘coherence’ is normally expressed through syntactic
constructions like causality, goal, indirect speech, co-ordination and subordination,
etc. In this paper, we will systematically make use of the term ‘connectivity
phenomena’ to denote this sort of clues, i.e., to denote what, in a stream of
elementary events, i) leads to a ‘global meaning’ that goes beyond the simple
addition of the ‘meanings’ conveyed by a single elementary event; ii) defines the
influence of the context in which a particular event is used on the meaning of this
individual event, or part of it.
• Eventually, narratives/complex events concern the behaviour or the condition of
some ‘actors’ (persons, characters, personages, figures etc.). They try to attain a
specific result, experience particular situations, manipulate some (concrete or
abstract) materials, send or receive messages, buy, sell, deliver, etc. In short, they
have a specific ‘role’ in the event (in the stream of events representing the global
narrative) – see, in a very peculiar ‘narratology’ context, the famous seven roles
(the hero, the villain, the princess etc.) described by Vladimir Propp in its
“Morphology of the Folktale” [13]. Note that these actors or characters are not
necessarily human beings; we can have narratives concerning, e.g., the vicissitudes
in the journey of a nuclear submarine (the ‘actor’, ‘character’ etc.) or the various
avatars in the life of a commercial product.
Defining a narrative/complex event as a ‘stream of elementary events’ would
correspond, once again, to some sort of ‘dull’ definition without being able to specify
what an “elementary event” can be. In an NKRL context, this point is also particularly
important from a practical point of view given that, as we see later, each elementary
event is separately encoded making use of the NKRL knowledge representation tools.
According then to a well-known Jaegwon Kim’s definition, see [14, 15], a “monadic”
event – which can be considered as equivalent to an “elementary event” – is identified
by a triple [x, P, t] where x is an object that exemplifies the n-ary property or relation
P at time t (where t can also be an interval of time); “monadic” means then that the n-
ary property P is exemplified by a single object x at a time. To make reference to one
of the recurrent examples in the theoretical discussions about events, “Brutus stabs
Caesar”, the Kimian interpretation of this event corresponds then to the representation
of an individual x, Brutus who, at time t, is characterised by the property P
exemplified by his stabbing of Caesar. Without entering now in the theoretical
controversies raised by this sort of definition, see [9: 8-13] for some information in
this context, we can note that, from an NKRL point of view, a more ‘practically
useful’ – more complete and structured – definition of elementary event is that
introduced by Donald Davidson [16, 17], particularly popular in the linguistic
domain. This last focuses the representation of an elementary event on the “action
verb” characterising the global conceptual category of the event more than – as in the
Kimian approach – on the “generalised properties” of this event. In this way, the
Davidsonian representation of “Brutus stabs Caesar” becomes: ∃e.stab(e, b, c), where
e is an event variable. The global meaning of this formalism corresponds to: “There is
an event e such that e was a stabbing of Caesar (c) by Brutus (b)”. Moreover, as
emphasised above when we have listed some important characteristics of
narratives/complex events, “roles” have a particular importance in a narrative. Our
preferred formalism for the representation of elementary events is then the so-called
“neo-Davidsonian” approach: the neo-Davidsonians, see [18, 19, 20], assume in fact
that the event argument e above must be the only argument of the (verbal) predicate:
this implies then, necessarily, the introduction of thematic (functional) “roles” for
expressing the relations between events and their participants. The formalization of
“Brutus stabs Caesar” becomes then now:
∃e[stab(e) & agent(e) = b & object(e) = c] . (1 )
Apart from the theoretical implications, what expounded above is particularly
important because it supplies us with a ‘pragmatically useful’ and ‘operational’
criterion for recognizing and isolating – in some way, for ‘defining’ – “elementary
events”. The criterion consists then in the identification, within the description in
natural language (NL) terms of the global stream representing a narrative/complex
event, of a specific ‘generalized natural language predicate’: this represents then the
‘core’ of a new elementary event. The predicate corresponds usually to a verb – to
stick to the previous example, recognizing “stabs” as a verb in the NL chain “Brutus
stabs Caesar” should be sufficient for signalling the presence of an elementary event –
but, according to the neo-Davidsonian approach, this predicate can also, in case,
correspond to a noun (…Jane’s amble along the park…) or an adjective (“… worth
several dollars…”) when these last have a predicative function. Of course, a drawback
of this criterion concerns the fact that its utility is limited to the recognition of the
elementary components of narratives/complex events expressed in NL terms, while
narratives are “multimedia” in essence – a photo representing President Obama
addressing the Congress, or a short video showing three nice girls on a beach are
surely narrative documents (the first including only an elementary event) but they are
not, of course, NL documents. A classical way of getting around this problem consists
in annotating multimedia narratives in natural language see, e.g., [21]. In [3],
Lombardo and Damiano propose a method for annotating and editing video fragments
(i.e., video narratives) with respect to their semantic content where the basic unit of
description – corresponding then to our ‘elementary events’ – are “beats” defining
“… the minimal units for story advancement that will exposed to the audience” [3:
707]. The criteria used to identify the “beats” in an unambiguous and repeatable way
are not, however, completely defined.
3 Representing Elementary and Complex Events
Eq. 1 above – an n-ary form of representation – shows clearly that the now so popular
W3C proposals like RDF(S), OWL or OWL 2 – see [22, 23, 24] – are, at least in their
standard format, unable to supply a basis for representing elementary events on a
computer. All the W3C representations are, in fact, of the binary type, based on the
classical ‘attribute – value’ model, where a property/attribute can only be a binary
relationship linking two individuals or an individual and a value. The inadequateness
of this approach to take into account complex representational problems like those
linked with narratives, spatio-temporal information, and any sort of events and
complex events is now widely recognized see, e.g., [25, 26, 27, 28, 29].
Note that the argument often raised in a W3C context and stating that any
representation making use of n-ary relations can be always converted to one making
only use of binary relations without any loss of expressiveness is incorrect with
respect to the last part of the sentence. It is true in fact that, from a pure formal point
of view, any n-ary relationship with n > 2 can always be reduced, in a very simple
way, to a set of binary relationships. This possibility is well illustrated, among other
things, by the successful representation of the NKRL’s core in the terms of a (W3C)
binary language like RDF, see [30]. However, this fact does not change at all the
intrinsic, ‘semantic’ n-ary nature of a simple statement like “Bill gives a book to
Mary” that, to be understood, requires to be taken in its entirety. This means – see
also Eq. 1 above – to make use of a semantic predicate of the GIVE type that
introduces its three arguments, “Bill”, “Mary” and “book” through three functional
relationships (roles) like SUBJECT (or AGENT), BENEFICIARY and OBJECT, the
whole n-ary construction being – this is the central point – reified and necessarily
managed as a coherent block at the same time. Only in this way it will be possible to
infer, in a querying/inferencing context that, e.g., the above elementary event is
linked, in the framework of a wider narrative, to another elementary event relating
Mary’s birthday; for the formal details see, e.g., [25].
Efforts done, in a strict W3C context, to extend their binary languages using some
n-ary features have not been very successful until now, see, for more details, [9: 17-
21]. Another way of trying to adapt/extend the traditional ‘binary’ tools to take into
account complex, dynamic situations consists in reducing the notion of “role” from
its normal status of ‘functional relationship’ to that of ‘static’ concept or class, see [9:
138-149] for a discussion about this topic. These role-concepts can then be used
within all sorts of complex binary schemata or patterns to represent causality,
mereology, participation etc. A well-known example of this approach is the
“Descriptions and Situations (DS)” model, see [31], implemented as a plug-in
extension to the DOLCE system [32], an (OWL-based) ‘upper ontology’. A recent
variation on this approach is represented by Event-Model-F, see [33], based in turn on
a reduced version of DOLCE, DOLCE+DnS Ultralite (DUL), see http://www.loa-
cnr.it/ontologies/DUL.owl.
Several actual n-ary models that, among other things, can be used to represent in
computer-usable ways elementary events have been described in the literature, see [9:
22-33] for a review. The n-ary model used in NKRL can be denoted as:
(Li (Pj (R1 a1) (R2 a2) … (Rn an))) , (2)
where Li is the symbolic label identifying (‘reifying’) the particular n-ary structure
(e.g., the global structure corresponding to the representation of previous “John gives
a book to Mary” example), Pj is a conceptual predicate, Rk is a generic “functional
role” and ak the corresponding predicate argument (e.g., the individuals JOHN_,
MARY_ etc.). Note that each of the (Ri ai) cells of Eq. 2, taken individually, represents
a binary relationship in the W3C (OWL, RDF…) languages style. The main point is
here that, as already stated, the whole conceptual structure represented by (2) must be
considered globally.
Similarities between neo-Davidsonian expressions for elementary events like that
of Eq. 1 and the formal structure of Eq. 2 are evident. However, some important
differences exist. To avoid both the typical ambiguities of natural language and
possible ‘combinatorial explosion’ problems – see the discussion in [9: 56-61] – both
the (unique) conceptual predicate of Eq. 2 and the associated functional roles are
‘primitives’. Predicates Pj pertain in fact to the set {BEHAVE, EXIST, EXPERIENCE,
MOVE, OWN, PRODUCE, RECEIVE}, and the functional roles Rk to the set
{SUBJ(ect), OBJ(ect), SOURCE, BEN(e)F(iciary), MODAL(ity), TOPIC,
CONTEXT}. Two special operators, date-1 and date-2 – that can be assimilated to
functional roles – are used to introduce the temporal information associated with an
elementary event: see, e.g., [9: 76-86, 194-201] for a detailed description of the
formal system used in NKRL for the representation and management of temporal
information. The NKRL representation of specific elementary events – that
corresponds to the concrete instantiations (called “predicative occurrences”) of
general structures in the style of Eq. 2 – is then a sort of canonical representation.
Several predicative occurrences – denoted by their symbolic labels Li and
representing formally a (possibly structured) set of elementary events – can be
associated within the scope of second order structures called “binding occurrences”.
These are, in practice, labelled lists formed of a “binding operator Bn” with its
arguments. The operators are those used in NKRL to represent the “connectivity
phenomena” that guarantee the global coherence of narrative/complex events, see
Section 2 above. They are: ALTERN(ative), COORD(ination), ENUM(eration),
CAUSE, REFER(ence) – the ‘weak causality operator’, introducing two arguments
where the second is necessary but not sufficient to explain the first – GOAL,
MOTIV(ation) – the ‘weak intentionality operator’, where the first argument is not
necessary to realise the second, which is however sufficient to explain the first –
COND(ition), see [9: 91-98]. The general expression of a binding occurrence is then:
(Bnk arg1 arg2 … argn) , (3 )
Eq. 3 is particularly important in an NKRL context because it supplies also the
formal expression – once again, a ‘pragmatic/operational’ form of definition – of the
notion of “narrative/complex event”. The arguments argi of Eq. 3 can, in fact, i)
correspond directly to Li labels – i.e., they can denote simply the presence of
particular elementary events represented formally as predicative occurrences – or ii)
correspond recursively to new labelled lists in Eq. 3 format. In the first case, the
global narrative/complex event represents merely a chronological stream of
elementary events, temporally characterized, where all these events have the same
logical/semantic weight and the operator Bn corresponds to COORD (or
ENUM/ALTERN). In the second case, we can suppose, e.g., that a given sequence of
events – an Eq. 3 list of the COORD... type – represents the CAUSE of another
sequence of events. The global representation of this narrative/complex event will
then correspond to an Eq. 3 list labelled as CAUSE, having as arguments arg1 the
COORD... list including the elementary events at the origin of the complex event and
as arg2 the COORD... list including the elementary events that represent together the
consequence, see also the simple example at the end of Section 4.1 below. What
expounded above is in agreement with the remarks expressed by several authors – see
[34] for example – about the possibility of visualizing under tree form the global,
formal expression of a narrative/complex event made up of several elementary events.
4 A Short Description of the NKRL system
After having introduced, in the previous Sections, the general theoretical framework
underpinning the NKRL approach to the narrative/complex events problem, we will
now illustrate briefly some points concerning its concrete implementation – see [9]
for a complete description.
3.1 The Knowledge Representation Aspects
NKRL innovates with respect to the current ontological paradigms by adding to the
usual ‘ontologies of concepts’ an ‘ontology of (elementary) events’, i.e., a new sort of
hierarchical organization where the nodes correspond to n-ary structures in the style
of Eq. 2 above. In the NKRL’s jargon, these n-ary structures are called “templates”
and the corresponding hierarchy – i.e., the ontology of elementary events – is called
HTemp (hierarchy of templates). Templates can be conceived as the canonical, formal
representation of generic classes of elementary events like “move a physical object”,
“be present in a place”, “produce a service”, “send/receive a message”, etc.
Note that, in the NKRL environment, an ‘ontology of concepts’ (according to the
traditional meaning of these terms) not only exists, but it represents an essential
component of this environment. The ‘standard’ ontology is called HClass (hierarchy
of classes): structurally and functionally, HClass is not fundamentally different from
one of the ontologies that can be built up by using tools in a ‘traditional’ Protégé
style, see [35]. An (extremely reduced) representation of HClass is given in Figure 1 –
HClass includes presently (April 2010) more than 7,000 concepts.
When a specific elementary event pertaining to one of the ‘general classes’
represented by templates must be encoded, the corresponding template is instantiated
giving rise to a “predicative occurrence”. To represent then a simple elementary event
(corresponding to the identification of the surface predicate “offer”) like: “British
Telecom will offer its customers a pay-as-you-go (payg) Internet service in autumn
1998”, we must select firstly in the HTemp hierarchy the template corresponding to
“supply a service to someone”, represented in the upper part of Table 1. This template
is a specialization of the particular MOVE template corresponding to ‘transfer of
resources to someone’ – Figure 2 below reproduces a fragment of the ‘external’
organization of HTemp. In a template, the arguments of the predicate (the ak terms in
Eq. 2) are concretely represented by variables with associated constraints: these are
expressed as HClass concepts or combinations of concepts, i.e., the two ontologies,
HTemp and HClass, are then strictly intermingled.
Fig. 1. Partial representation of HClass, the ‘traditional’ ontology of concepts.
Table 1. Deriving a predicative occurrence from a template.
name: Move:TransferOfServiceToSomeone
father: Move:TransferToSomeone
position: 4.11
natural language description: “Transfer or Supply a Service to Someone”
MOVE SUBJ var1: [var2]
OBJ var3
[SOURCE var4: [var5]]
BENF var6: [var7]
[MODAL var8]
[TOPIC var9]
[CONTEXT var10]
{[modulators]}
var1 = human_being_or_social_body
var3 = service_
var4 = human_being_or_social_body
var6 = human_being_or_social_body
var8 = process_, sector_specific_activity
var9 = sortal_concept
var10 = situation_
var2, var5, var7 = geographical_location
c1) MOVE SUBJ BRITISH_TELECOM
OBJ payg_internet_service
BENF (SPECIF customer_ BRITISH_TELECOM)
date-1: after-1-september-1998
date-2:
When creating a predicative occurrence (an instance of a template) like c1 in the
lower part of Table 1, the role fillers in this occurrence must conform to the
constraints of the father-template. For example, in occurrence c1,
BRITISH_TELECOM is an individual, instance of the HClass concept company_: this
last is, in turn, a specialization of human_being_or_social_body.
payg_internet_service is a specialization of service_, a specific term of social_activity,
etc. The meaning of the expression “BENF (SPECIF customer_
BRITISH_TELECOM)” in c1 is self-evident: the beneficiaries (role BENF) of the
service are the customers of – SPECIF(ication) – British Telecom. The ‘attributive
operator’, SPECIF(ication), is one of the four operators used for the set up of the
structured arguments (expansions) of conceptual predicates like MOVE, see [9: 68-
70]. In the occurrences, the two operators date-1 and date-2 materialize the temporal
interval normally associated with an elementary event, see again [9: 76-86, 194-201].
Fig. 2. ‘MOVE’ etc. branch of the HTemp hierarchy.
More than 150 templates are permanently inserted into HTemp; HTemp, the
NKRL ontology of events, corresponds then to a sort of ‘catalogue’ of narrative
formal structures, which are very easy to extend and customize.
To supply now an at least intuitive idea of how a complete narrative/complex
event is represented in NKRL, and returning to the Table 1 example, let us suppose
we would now state that: “We can note that, on March 2008, British Telecom plans to
offer to its customers, in autumn 1998, a pay-as-you-go (payg) Internet service…”,
where the specific elementary event corresponding to the offer is still represented by
occurrence c1 in Table 1.
To encode correctly the new information, we must introduce first an additional
predicative occurrence labelled as c2, see Table 2, meaning that: “at the specific date
associated with c2 (March 1998), it can be noticed, modulator obs(erve), that British
Telecom is planning to act in some way” – the presence of a second surface predicate
in the NL expression of the complex event denotes the presence of a second
elementary event. obs(erve) is a ‘temporal modulator’, see [9: 71-72], used to identify
a particular timestamp within the temporal interval of validity of an elementary event.
We will then add a binding occurrence c3 labelled with a GOAL Bn operator, see the
previous Section, to link together the conceptual labels c2 (the planning activity) and
c1 (the intended result). The global meaning of c3 can be verbalized as: “The activity
described in c2 is focalised towards (GOAL) the realization of c1”. In agreement with
the remarks at the end of the last Section, c3 – the representation of the global
narrative/complex event – can also represented under tree form, having GOAL as top
node, and two branches where the leaves are L1 = c2 and L2 = c1.
Table 2. Binding and predicative occurrences.
c 2) BEHAVE SUBJ B R IT IS H _T E LE C O M
MO D A L pl anni ng_
{ obs }
dat e1: m arc h-1998
dat e2:
B ehav e: A c t E xpl i c i t l y (1. 12)
* c 1) MO V E SUBJ B R IT IS H _T E LE C O M
OBJ payg_i nt ernet _s erv i c e
BENF (S P E C IF c us t om er_ B R IT IS H _T E LE C O M)
dat e-1: af t er-1-s ept em ber-1998
dat e-2:
Mov e: T rans f erO f S erv i c eT oS om eone (4. 11)
c 3) (G O A L c 2 c 1)
3.2 The Querying/Inferencing Aspects
Reasoning in NKRL ranges from the direct questioning of a knowledge base of
narratives represented in NKRL format – by means of search patterns pi (formal
queries) that unify information in the base thanks to the use of a Filtering Unification
Module (Fum), see [9: 183-201] – to high-level inference procedures. These last make
use of the richness of the representation to establish ‘interesting’ relationships among
the narrative items stored within the base; a detailed paper on this topic is [6].
The NKRL rules are characterised by the following general properties:
• All the NKRL high-level inference rules can be conceived as implications of the
type:
X iff Y1 and Y2 … and Yn . (4)
• In Eq. 4, X corresponds either to a predicative occurrence cj or to a search pattern
pi and Y1 … Yn – the NKRL translation of the ‘reasoning steps’ that make up the
rule – correspond to partially instantiated templates. They include then, see the
upper part of Table 1 above, explicit variables of the form vari.
• According to the usual conventions of logic/rule programming, see [33: 105-170] –
InferenceEngine understands each implication as a procedure. This reduces
‘problems’ of the form X to a succession of ‘sub-problems’ Y1 and … Yn.
• Each Yi is interpreted in turn as a procedure call that tries to convert – using, in
case, backtracking procedures – Yi into (at least) a successful search pattern pi.
These last must then be able to unify (using the standard Fum module) one or
several of the occurrences cj of the NKRL knowledge base.
• The success of the unification operations of the pattern pi derived from Yi means
that the ‘reasoning step’ represented by Yi has been validated. InferenceEngine
continues trying then to validate the reasoning step corresponding to Yi+1.
• In line with the presence of the operator ‘and’ in Eq. 4, the implication represented
by Eq. 4 is fully validated iff all the reasoning steps Y1, Y2 … Yn are validated.
All the unification operations pi/cj required from the inference procedures make use
only of the unification functions supplied by the Filtering Unification Module (Fum)
introduced above. Apart from being used for the direct questioning operations, Fum
constitutes as well, therefore, the ‘inner core’ of the InferenceEngine modules.
From a practical point of view, the NKRL high-level inference procedures concern
mainly two classes of rules, ‘transformations’ and ‘hypotheses’ see, e.g., [6].
Let us consider, e.g., the ‘transformations’. These rules try to ‘adapt’, from a
semantic point of view, a search pattern pi that ‘failed’ (that was unable to find a
unification within the knowledge base) to the real contents of this base making use of
a sort of ‘analogical reasoning’. They attempt then to automatically ‘transform’ pi
into one or more different p1, p2 … pn that are not strictly ‘equivalent’ but only
‘semantically close’ (analogical reasoning) to the original one. In a transformation
context, the ‘head’ X of Eq. 4 is then represented by a search pattern, pi.
Operationally, a transformation rule can be conceived as made up of a left-hand
side, the ‘antecedent’ – i.e. the formulation, in search pattern format, of the ‘query’ to
be transformed – and of one or more right-hand sides, the ‘consequent(s)’ – the
NKRL representation(s) of one or more queries (search patterns) to be substituted for
the given one. Denoting with A the antecedent and with Cs all the possible
consequents, the transformation rules can then be expressed as:
A(vari) ⇒ Cs(varj), vari ⊆ varj (5)
With respect to Eq. 4 above, X coincides now with A – a search pattern – while the
reasoning steps Y1, Y2 … Yn are used to produce the search pattern(s) Cs to be used in
place of A. The restriction vari ⊆ varj – all the variables declared in the antecedent A
must also appear in Cs – assures the logical congruence of the rules. More formal
details are given, e.g., in [9: 212-216].
Let us consider a concrete example, which concerns a recent NKRL application
about the ‘intelligent’ management of ‘storyboards’ in the oil/gas industry, see also
[37]. We want then ask whether, in a knowledge base where are stored all the possible
elementary and complex events related to the activation of a gas turbine, we can
retrieve the information that a given oil extractor is running. In the absence of a direct
answer we can reply by supplying, thanks to a transformation rule like that (t11) of
Table 3, other related events stored in the knowledge base, e.g., an information stating
that the site leader has heard the working noise of the oil extractor, see Figure 3.
Table 3. An example of ‘transformation’ rule.
t11: “working noise/condition” transformation
antecedent:
OWN SUBJ var1
OBJ property_
TOPIC running_
var1 = consumer_electronics, hardware_, diagnostic_tool/system, surgical_tool,
technical/industrial_tool, small_portable_equipment
first consequent schema (conseq1):
EXPERIENCE SUBJ var2
OBJ evidence_
TOPIC (SPECIF var3 var1)
var2 = individual_person
var3 = working_noise, working_condition
second consequent schema (conseq2):
BEHAVE SUBJ var2
MODAL industrial_site_operator
Being unable to demonstrate directly that an industrial apparatus is running, the fact that an operator
can hear its working noise or note its operational aspect can represent a proof of its running status.
Expressed in natural language, this result can be paraphrased as: “The system
cannot assert that the oil extractor is running, but it can certify that the site leader has
heard the working noise of this extractor”.
With respect now to the hypothesis rules, these allow us to build up automatically a
sort of ‘causal explanation’ for an elementary event (a predicative occurrence cj)
retrieved within a NKRL knowledge base. In a hypothesis context, the ‘head’ X of Eq.
4 then represented by a predicative occurrence, cj. Accordingly, the ‘reasoning steps’
Yi of Eq. 4 – called ‘condition schemata’ in a hypothesis context – must all be satisfied
(for each of them, at least one of the corresponding search patterns pi must find a
successful unification with the predicative occurrences of the base) in order that the
set of c1, c2 … cn predicative occurrences retrieved in this way can be interpreted as a
context/causal explanation of the original occurrence cj.
For example, to mention a ‘classic’ NKRL example, see [6], let us suppose we
have directly retrieved, in a querying-answering mode, information like:
“Pharmacopeia, an USA biotechnology company, has received 64,000,000 dollars
from the German company Schering in connection with an R&D activity” that
corresponds then to cj. We can then be able to automatically construct, using a
‘hypothesis’ rule, a sort of ‘causal explanation’ of this event by retrieving in the
knowledge base information like: i) “Pharmacopeia and Schering have signed an
agreement concerning the production by Pharmacopeia of a new compound” (c1) and
ii) “in the framework of the agreement previously mentioned, Pharmacopeia has
actually produced the new compound” (c2).
Fig. 3. Using the NKRL InferenceEngine in a ‘transformation’ context.
A recent development of NKRL concerns the possibility of using the two above
modalities of inference in an ‘integrated’ way, see [9: 216-234]. More exactly, it is
possible to make use of ‘transformations’ when InferenceEngine is working in the
‘hypothesis’ environment. This means that, whenever a search pattern pi is derived
from the ‘condition schema’ of a hypothesis to implement a step of the reasoning
process, we can use it ‘as it is’ – i.e., in conformity with its ‘father’ condition schema
– but also in a ‘transformed’ form if the appropriate transformation rules exist. The
advantages are essentially that i) a hypothesis deemed to fail can now continue if a
transformed pi is able to find an unification within the knowledge base, getting then
new values for the hypothesis variables; ii) this strategy allows us to explore in a
systematic ways all the possible implicit relationships among the data in the base.
4 Conclusion
NKRL deals with the representation and management of ‘elementary’ and ‘complex’
events by making use of n-ary and second order knowledge representation structures.
One of its main characteristics concerns the addition of an ontology of events to the
usual ontology of concepts. Its inference solutions employ advanced causal- and
analogical-based reasoning techniques to deal with the events and their relationships.
NKRL is also a fully operational environment, implemented in two versions (file-
oriented and Oracle-based) and developed thanks to several European projects. Many
successful applications in many different domains (from ‘terrorism’ to the ‘corporate’,
‘cultural heritage’ and ‘legal’ domains, to the management of ‘storyboards/historians’
for the gas/oil industry…) have proved the practical utility of this tool.
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