=Paper=
{{Paper
|id=Vol-1363/paper_6
|storemode=property
|title=A Contextual Framework for Reasoning on Events
|pdfUrl=https://ceur-ws.org/Vol-1363/paper_6.pdf
|volume=Vol-1363
|dblpUrl=https://dblp.org/rec/conf/esws/BozzatoBARS15
}}
==A Contextual Framework for Reasoning on Events==
https://ceur-ws.org/Vol-1363/paper_6.pdf
A Contextual Framework for Reasoning on Events
Loris Bozzato1 , Stefano Borgo2 , Alessio Palmero Aprosio1 , Marco Rospocher1 , and
Luciano Serafini1
1
Fondazione Bruno Kessler,
Via Sommarive 18, 38123 Trento, Italy
2
Laboratory for Applied Ontology, ISTC CNR,
Via alla Cascata 56 C, 38123 Trento, Italy
{bozzato,aprosio,rospocher,serafini}@fbk.eu, stefano.borgo@cnr.it
Abstract. In this paper we investigate the contextualized representation of events.
In particular, we re-interpret the formalization of events and situations adopted in
the NewsReader Event and Situation Ontology (ESO) according to the notions
of context and module proposed in the Contextualized Knowledge Repositories
(CKR) framework. This contextualized formalization sets the basis for exploiting
logical reasoning on events and situations, enabling to automatize tasks such as:
recognizing incompatible event descriptions or inconsistent situations, inferring
missing or implicit events.
1 Introduction
With the growth of technologies managing the extraction of events and their participants
from texts (e.g. [1,7]), interest has spread in using these low level tools as the base for
event processing and reasoning in a higher level abstraction for events. The idea is
to be able to discover, starting from low level descriptions of events extracted from
text, complex events and their succession (viz. stories) with respect to their participant
entities.
This is one of the goals of the NewsReader project3 . In this project, Natural Lan-
guage Processing (NLP) technologies, such as Named Entity Recognition (NER) and
Semantic Role Labelling (SRL), are exploited to process large streams of news in multi-
ple languages, to extract events and event participants, locations, dates. At the end of the
extraction and processing, events and related information are represented using RDF.
In order to define and reason over the features and effects of events, an OWL ontology
has been defined for such data, called the Event and Situation Ontology (ESO) [11].
In this paper we propose to reinterpret and formalize the event model defined by the
ESO ontology using a context-based framework for representation of Semantic Web
data, the Contextualized Knowledge Repositories (CKR) framework [12,2,4,5]: this en-
ables to exploit the structure and reasoning possibilities offered by the contextual frame-
work in order to perform complex inferences about and inside knowledge associated
with events.
Intuitively, CKR is a description logics (DL) based framework defined as a two-
layered structure: a lower layer contains a set of knowledge bases representing each
context, while the upper layer contains context independent knowledge and meta-data
3
www.newsreader-project.eu
defining the structure of contexts. The CKR framework has not only been presented as
a theoretical framework, but also actual implementations based on its definitions [5,3]
have been proposed. In particular, in [5] we presented an implementation for the CKR
framework over state-of-the-art tools for storage and inference over RDF graphs: in-
tuitively, the CKR architecture can be implemented by representing the global context
and the local object contexts as distinct RDF named graphs, while inference inside (and
across) named graphs is implemented as SPARQL-based forward rules.
From a formal ontology point of view, in the approach we propose in this paper
we clearly distinguish ontology from knowledge. The upper layer of our system con-
tains the underlying ontology, that is, the description of the organization of the world.
This part lists types of entities, relations and constraints that are assumed to exist or to
simply be possible. More generally, the upper layer should be thought as including two
elements: a general (foundational or domain) ontology describing what can exist, and
the organization of a set of knowledge modules characterizing roles and relationships
in (typically social) standardized scenarios like economic transactions or soccer games.
Regarding the first, we do not make a specific commitment towards one ontology or an-
other although we explicitly commit to the existence of physical and social objects, like
people and organizations, and temporal happenings like weddings and thunderstorms.
These entities come with their usual physical and temporal properties: weight, shape,
duration and so on. Regarding the modules, we do explore their role in the system to
infer new facts from given knowledge and thus we will present their general setting and
how they are used. The lower layer of the system, on the other hand, collects claims
about what happens in the world, that is, claims about how things are or change at some
time. Throughout the paper these are what we call events, but note that they are not
events in the ontological sense, rather they are descriptions of happenings.
Each event is associated with three state-like entities, namely happenings charac-
terized by some continuously holding property or relation like “begin married”, “being
running” or “being employed by” (e.g. see the notion of stative perdurant in DOLCE [9]).
These state-like entities are called situations in our system; more specifically the situa-
tion holding before the event is called pre-situation, the one holding after post-situation
and the one holding during the event itself is the during-situation. Informally, these sit-
uations are used to make explicit relevant properties that (a) persist during the whole
event, or (b) hold (or don’t hold) before/after the event and their truth values change
“because” of the event. We will use situations to formalize (and reason on) the precon-
ditions for an event of a certain type to happen, the postconditions (or consequences) of
its happening, and what can be taken as stable during the whole event of that type.
Although we take the received events at face value, it is possible that the news are
imprecise or incomplete, that the text processing used to acquire the news is faulty
and misinterprets them, that statements extracted from different sources about the same
happenings contradict each other. For this reason, we take each piece of information ex-
tracted from an outside source as a contextual perspective on the world. This means that
the content of a news is not equated to absolute knowledge (it is not indisputable). In-
deed, the extracted information constitutes a context annotated with its original source
(possibly with different degrees of reliability which can change over time). This allows
to integrate pieces of information coming from different sources into large and artic-
ulated event descriptions by integrating different contexts (in turn, news statements).
These extrapolated events reconstruct how an entity like a person or an organization,
changes over time by changing its status (size, capacity, death) or its relationships with
other entities (property acquisition, employment, marriage). Furthermore, missing in-
formation on these complex events can be detected via logical reasoning based on the
ontology at the upper layer, leading to infer changes that have not been reported (or
detected) in the news. Finally, note that, when a contradiction arises, the system can
isolate the conflicting pieces of information and establish which contexts are to be kept
apart and need to be verified.
In this paper we propose the first sketch and example of use for the contextual-
ized ESO model: in the next section we briefly present the ESO model and provide an
example of event modelling; in Section 3, we summarize the base definitions for the
CKR framework; in the following section we describe the CKR-ESO model, that is our
contextual based realization of ESO, we show how it models the previously presented
event example and provide some insights in the advantages of such representation; we
conclude by outlining some of the current and future directions for our work.
2 Events and Situations
One of the objectives in the NewsReader project is the representation of events and of
their effects on the entities participating in them. For example, in a “giving” event, we
have at least two actors (people, organizations) and an object, e.g., a person A giving
something to another person B at some time T . The event also describes a change in
these entities: at the time T , person A owns the object, while person B does not (aka,
pre-situation); after T , the opposite is true (aka, post-situation).
To achieve this objective, the Event and Situation Ontology (ESO) [11] has been
developed. It defines two main classes of entities: events and situations. An event de-
scribes an happening, typically a change, in the world (for instance, person A giving
an object to person B at time T ), has a certain number of participants (here, the two
people and the object) and an associated period of time (here, T ). A situation describes
a state, i.e., takes a set of statements as describing (part of) the world at some point or
interval of time. In the example “person A gives an object to person B at time T ”, we
can identify a pre-situation (the state of the world at the initial time point of T ):
– person A owns the object,
– person B does not own the object
and a post-situation (the state of the world at the ending time point of T ):
– person A does not own the object
– person B owns the object.
For instance, in the sentence: “The chairman of India’s Tata Group has confirmed that
his company is acquiring the Jaguar and Land Rover businesses from Ford.”, an event
is described: the acquisition by Tata Group of Jaguar and Land Rover from Ford. The
event is represented in ESO terminology as follows:
<:evID, rdf:type, sem:Event>
<:evID, rdf:type, eso:Getting>
<:evID, rdfs:label, acquire>
<:evID, eso:possession-owner 1, dbp:Ford>
<:evID, eso:possession-owner 2, dbp:Tata Group>
<:evID, eso:possession-theme, :Jag and L. Rover>
<:evID, sem:hasTime, #tmxID>
where #tmxID is the RDF representation of “August 26th, 2007”.
According to ESO, the event eso:Getting has both pre- and post-situation, con-
taining eso:hasInPossession and eso:notHasInPossession assertions, respectively. In the
pre-situation of our example, Ford has in possession Jaguar and Land Rover, and Tata
Group does not have in possession them; in the post-situation, the contrary holds. In the
ESO model, such facts specific to a situation are stored in a RDF named graph identified
by the URI of the situation. Therefore, the following set of n-tuples is generated:4
<:evID, eso:hasPreSituation, :evID pre>
<:evID pre, rdf:type, eso:Situation>
<:evID pre, sem:hasTime, #tmxID>
<:evID, eso:hasPostSituation, :evID post>
<:evID post, rdf:type, eso:Situation>
<:evID post, sem:hasTime, #tmxID>
In NewsReader, all the events and related information, instantiated according to the
ESO metamodel, are stored, together with the original news article from where they
were extracted in the KnowledgeStore [6], a scalable, fault-tolerant, and Semantic
Web grounded storage system to jointly store, manage, retrieve, and query both struc-
tured and unstructured data.
3 Contextualized Knowledge Repositories
In the following we provide an informal summary of the definitions for the CKR frame-
work: for a formal and detailed description and for complete examples, we refer to [5].
A CKR is a two layered structure: (1) the upper layer consists of a knowledge base
G, called global context, containing (a) meta-knowledge, i.e. the structure and properties
of contexts, and (b) global (context-independent) object knowledge, i.e., knowledge that
applies to every context; (2) the lower layer consists of a set of (local) contexts that
contain locally valid facts and can refer to what holds in other contexts. The intuitive
structure of a CKR knowledge base is depicted in Figure 1: in the following we detail
its formal components and interpretation.
Syntax. The meta-knowledge of a CKR is expressed in a DL language containing the
elements that define the contextual structure: the meta-vocabulary Γ is a DL signature
containing, in particular, the sets of symbols for context names N, module names M and
context classes C, including the class of all contexts Ctx. Intuitively, modules represent
pieces of knowledge specific to a context or a context class. The role mod defined on
N × M expresses associations between contexts and their modules. The meta-language
LΓ of a CKR is a DL language over Γ.
The knowledge in contexts of a CKR is expressed via a DL language LΣ , called
object-language, based on an object-vocabulary Σ. The expressions of the object lan-
guage are evaluated locally to each context, i.e., contexts can interpret each symbol
4
Whenever applicable, default named graph is omitted.
Metaknowledge Global object knowledge
Global context
mod_cls1 A ⊑ B, B ⊑ C, ...
Class1 R ⊑ S, ...
A(a), B(a), ...
mod_ctx1 mod_ctx2 mod_ctx3
Rel1 R(a, b), S(a, c) ...
Context1 Context2 Context3
Local modules
D ⊑ B, D(a)... C ⊑ B, C(a)... E ⊑ B, E(a)... D ⊑ E, D(b)...
mod_cls1 mod_ctx1 mod_ctx2 mod_ctx3
Fig. 1. CKR structure
independently. To access the interpretation of expressions inside a specific context or
context class, we extend LΣ to LeΣ with eval expressions of the form eval(X, C), where
X is a concept or role expression of LΣ and C is a concept expression of LΓ (with
C v Ctx). Intuitively, eval(X, C) can be read as “the interpretation of X in all the con-
texts of type C”.
We define a Contextualized Knowledge Repository (CKR) as a structure K = hG, {Km }m∈M i
where: (i) G is a DL knowledge base over LΓ ∪ LΣ ; (ii) every Km is a DL knowledge
base over LeΣ , for each module name m ∈ M. We note that the knowledge in a CKR can
be expressed by means of any DL language: in our work, we consider SROIQ-RL [5]
as language of reference. SROIQ-RL is a restriction of SROIQ syntax corresponding
to OWL RL [10].
Semantics. The semantics of CKR basically extends the usual model-based semantics
of DL knowledge bases to the two layered structure of the framework. A CKR interpre-
tation is a structure I = hM, Ii s.t.: (i) M is a DL interpretation of Γ ∪ Σ (respecting
the intuitive interpretation of Ctx as the class of all contexts); (ii) for every context
x ∈ CtxM , I(x) is a DL interpretation over Σ (with same domain and interpretation
of individual names of M). The interpretation of ordinary DL expressions on M and
I(x) is as usual while eval expressions are interpreted as follows: for every x ∈ CtxM ,
eval(X, C)I(x) represents the union of all elements in X I(e) for all contexts e in CM .
A CKR interpretation I is a CKR model of K iff: (i) for α ∈ LΣ ∪ LΓ in G, M |= α;
(ii) for hx, yi ∈ modM with y = mM , I(x) |= Km ; (iii) for α ∈ G ∩ LΣ and x ∈ CtxM ,
I(x) |= α. Intuitively, this means that I verifies the contents of global and local modules
associated with contexts and global object knowledge has to be propagated to local
contexts.
Materialization calculus. Reasoning in CKR has been formalized as a materialization
calculus [8], a datalog-based calculus for instance checking in SROIQ-RL CKRs.
Intuitively, the calculus is based on a translation to datalog of the input CKR. It
has three components: (i) the input translations Iglob , Iloc , Irl , where given an axiom α
and c ∈ N, each I(α, c) is a set of datalog facts or rules encoding the contents of input
global and local DL knowledge bases; (ii) the deduction rules Ploc , Prl , which are sets
of datalog rules representing the inference rules for the instance-level reasoning over
the translated axioms; and (iii) the output translation O, where given an axiom α and
c ∈ N, O(α, c) is a single datalog fact encoding the ABox assertion α that we want to
prove to be entailed by the input CKR (in the context c).
Intuitively, SROIQ-RL input Irl and deduction Prl rules provide the translation and
interpretation of SROIQ-RL axioms from the input CKR. Global input rules in Iglob
encode the interpretation of Ctx in the global context. Similarly, local input rules Iloc
and deduction rules Ploc provide the translation and rules for the local eval expressions.
The rules in O provide the translation of ABox assertions that can be verified to hold in
a context c by applying the rules of the final program.
The translation of a CKR K to its datalog program PK(K) proceeds in four steps:
we first translate G in the global program PG(G) by applying input rules Iglob and Irl
to G and adding deduction rules Prl ; then, for every context name c ∈ N appearing in
PG(G), we compute its knowledge base Kc as the set of modules Km ∈ K s.t. mod(c, m)
is proved by PG(G); we translate each local program PC(c) by applying input rules Iloc
and Irl to Kc and adding deduction rules Ploc and Prl ; the final CKR program PK(K)
is then obtained as the union of PG(G) with all local programs PC(c). We say that K
entails an axiom α in a context c ∈ N if PK(K) |= O(α, c). We can show (see [5]) that
the presented rules and translation process provide a sound and complete calculus for
instance checking in SROIQ-RL CKR.
CKR implementation on RDF. We recently presented a prototype [5] that implements
the forward reasoning procedure over CKR defined by the materialization calculus. The
prototype accepts RDF input data expressing OWL-RL axioms and assertions for global
and local knowledge modules: these different pieces of knowledge are represented as
distinct named graphs, while we encoded in a OWL vocabulary the CKR contextual
primitives (e.g. the class Context of all context individuals, the class Module of all
modules and the property hasModule corresponding to the role mod). The prototype is
based on an extension of the Sesame RDF Framework5 and structured in a client-server
architecture: the main component, called CKR core and residing in the server-side part,
offers the ability to compute and materialize the inference closure of the input CKR,
add and remove knowledge and execute queries over the complete CKR structure.
The distribution of knowledge in different named graphs asks for a component to
compute inference over multiple graphs in a RDF store, since inference mechanisms
in current stores usually ignore the graph part. This component has been realized as a
general software layer called SPRINGLES (SParql-based Rule Inference over Named
Graphs Layer Extending Sesame) [5]. Intuitively, SPRINGLES provides methods to
demand a closure materialization on the RDF store data: rules are encoded as (named
graphs aware) SPARQL queries and it is possible to customize both the used ruleset and
the evaluation strategy.
In our case, the ruleset basically encodes the rules of the presented materializa-
tion calculus. The rules are evaluated with a strategy that follows the same steps of
the translation process defined for the calculus. The plan goes as follows: (i) we com-
pute the inference closure on the graph for global context G, by a fixpoint on rules
corresponding to Prl ; (ii) we derive associations between contexts and their modules,
by adding dependencies for every assertion of the kind hasModule(c, m) in the global
closure; (iii) we compute the closure of the contexts, by applying rules encoded from
Prl and Ploc and resolving eval expressions by the metaknowledge information in the
global closure.
5
http://www.openrdf.org/
4 Representing events in CKR: CKR-ESO ontology
We can now describe how we translated and implemented a first prototype of the ESO
model in the form of a contextualized ontology for the CKR, that we call the CKR-ESO
ontology.
In this model, the event and situation structures are modelled in the metaknowledge.
Similarly to the ESO model, each event instance is associated in the metaknowledge
with its pre-, during- and post-situations using the object properties hasPreSituation,
hasPostSituation and hasDuringSituation, subproperties of hasSituation. Situation el-
ements associated with events can be generated automatically by SPRINGLES rules
when importing an event.
Each event is represented in the metaknowledge as an instance of the class Event:
in particular, each event is associated, analogously to the ESO model, with a sub-
class of the Event class that determines the type of associated situations: in particular,
DynamicEvents (e.g. ChangeOfPossession, Constructing) are typically characterized
by their pre- and post-situations, while StaticEvents (e.g. BeingOperational) by their
during-situations.
This classification is provided by restrictions over the definition of such classes. For
example, for the ChangeOfPossession event class, the CKR-ESO ontology states that:
ChangeOfPossession v ∀hasPreSituation.Pre ChangeOfPossession
ChangeOfPossession v ∀hasPostSituation.Post ChangeOfPossession
Each event individual is associated with a knowledge module that corresponds to the
RDF graph of the event in the ESO model. This association is represented in the meta-
knowledge by the property hasEventModule. The following chain axiom is defined
over this property, asserting that situations related to an event inherit the facts asserted
in the event module: (hasSituation)− ◦ hasEventModule v hasModule. As defined
by the ESO model, we expect to find in the event module the instantiation for all the
required roles involved in the event.
The class Situation is defined as a subclass of the Context class in the CKR vocab-
ulary: in other words, in our model we consider situations and their local knowledge
as contexts. The particular (pre, post and during) situations associated with event types
are modelled by specific context classes. Thus, for example, we have that the pre- and
post-situations for events of type ChangeOfPossession are classified as members of
the classes Pre ChangeOfPossession and Post ChangeOfPossession. The associ-
ation between such type of situations and their local axioms (i.e. what its modelled
in the ESO ontology by situation assertions) is performed by linking specific knowl-
edge modules to these context classes. For example, in CKR-ESO we declare that ev-
ery pre-situation of ChangeOfPossession is associated with the knowledge module
pre change-of-possession-m and post-situations to post change-of-possession-m:
Pre ChangeOfPossession v ∃hasModule.{pre change-of-possession-m}
Post ChangeOfPossession v ∃hasModule.{post change-of-possession-m}
Situation assertions are thus encoded inside these specific modules: the assertions can
be basically translated to chain axioms across the roles specified in the event. For ex-
ample, assertions for pre-situations of ChangeOfPossession stating that:
hasInPossession(possession-owner 1, possession-theme)
notHasInPossession(possession-owner 2, possession-theme)
Context
Event
Global context
Situation
DynamicEvent
pre_cop-m
Pre_CoP Post_CoP
post_cop-m ChangeOf
Possession
pre-event1 post-event1
hasPostSituation event1
hasPreSituation event1_m
Kpre_cop-m (possession-owner_1)– ○ possession-theme ⊑ hasInPossession
(possession-owner_2)– ○ possession-theme ⊑ notHasInPossession
Local modules
Kpost_cop-m (possession-owner_2)– ○ possession-theme ⊑ hasInPossession
(possession-owner_1)– ○ possession-theme ⊑ notHasInPossession
Kevent1 possession-owner_1(event1,Ford)
possession-owner_2(event1,Tata_Group)
possession-theme(event1,Jaguar_and_Land_Rover)
Fig. 2. Example event in CKR-ESO model.
is translated in CKR-ESO to these chain axioms across role properties:
(possession-owner 1)− ◦ possession-theme v hasInPossession
(possession-owner 2)− ◦ possession-theme v notHasInPossession
We now can show how to represent our example event from Section 2 using the CKR-
ESO model: we depict this modelling in Figure 2. In the global context, we define
event1 to be of type ChangeOfPossession and associate it with its situations:
ChangeOfPossession(event1)
hasPreSituation(event1, pre-event1)
hasPostSituation(event1, post-event1)
By the above axioms for such event type, we know that the pre- and post-situations of
event1 have to be of type Pre ChangeOfPossession and Post ChangeOfPossession.
By metalevel reasoning, this implies that:
hasModule(pre-event1, pre change-of-possession-m)
hasModule(post-event1, post change-of-possession-m)
and thus the situation assertions associated with the pre- and post-situations of ChangeOfPossession
are imported in the two situations6 . Moreover, the graph associated with the original
event is now defined as a module associated with event1 in the metalevel: hasEventModule(event1, event1 m).
The event1 m module (i.e. the associated RDF graph) now contains the following facts
that are shared with all the situations associated with this event:
possession-owner 1(event1, Ford)
possession-owner 2(event1, T ata Group)
possession-theme(event1, Jaguar and Land Rover)
6
In Figure 2 we abbreviate classes of pre- and post-situations of ChangeOfPossession with
Pre CoP and Post CoP and their modules with pre cop-m and post cop-m.
Using the situation assertions in the module associated with the pre-situation, the CKR
thus derives the following facts in the context of pre-event1:
hasInPossession(Ford, Jaguar and Land Rover)
notHasInPossession(T ata Group, Jaguar and Land Rover)
Similarly, in the context of post-event1 we obtain:
notHasInPossession(Ford, Jaguar and Land Rover)
hasInPossession(T ata Group, Jaguar and Land Rover)
We note that representation of the described event can be completed with its associated
during-situation: among the facts that are known to hold during the event, for example,
we can assert the existence of the actors and of the possession theme (using the exists
property in the ESO).
This contextual re-interpretation of the ESO model can bring several advantages from
the point of view of reasoning capabilities inside and across events. First of all, every
aspect of the reasoning procedure is now strictly ruled by logical reasoning: situation
assertions and their association with the type of situation are now directly modelled
by the CKR structure and local axioms, without demanding an external reasoner to
consider the situation rules and the local reasoning inside situations. Furthermore, the
propagation of global object knowledge to local knowledge allows the use of context
independent background knowledge in local reasoning. In our example, we can assert
in the global knowledge that both actors (Ford and T ata Group) are classified as car
companies and their features can be used in local reasoning. More in general, the ad-
vantages of an explicit and structured representation of contexts (as the one offered by
the CKR) with respect to a modelling based on reification have been shown in [2].
On the other hand, the clear separation of meta and object level reasoning can be
exploited to exchange information across the two levels. For example, depending on
the type and specific patterns of situation and events, by adding custom SPRINGLES
rules it is possible to generate implicit events that have to occur for the completion
of the event sequence in a story. In our example, if we have a second event event2
representing another ChangeOfPossession of Jaguar and Land Rover between two
companies Company1 and Company2, different than the two companies from event1,
and event2 has a timestamp greater than event1, then we can infer that there have been
another two events (possibly being the same one): in one Jaguar and Land Rover has
been sold from T ata Group and in the other it has been acquired from Company1. Sim-
ilarly, we can recognize cases in which we can assert the equality of certain situations:
this can be used to compile sequences of events in a story.
Metalevel information for situations and events can be derived from local reason-
ing: we might recognize incompatible descriptions of the same event from different
news. For example, let us suppose a different representation of the scenario shown in
the example in Figure 2: assume that event1 is now classified as Buying (subclass of
ChangeOfPossession) while another event event2 is extracted as Selling (also sub-
class of ChangeOfPossession), but they both represent the same conceptual event (i.e.
the acquisition of Jaguar and Land Rover by T ata Group from Ford). Thus, at the
level of the metaknowledge, the two events are modelled as:
Buying(event1)
hasPreSituation(event1, pre-event1)
hasPostSituation(event1, post-event1)
Selling(event2)
hasPreSituation(event2, pre-event2)
hasPostSituation(event2, post-event2)
Since they represent the same happening, the event modules event1 m and event2 m
basically share the same contents: that is, the actors are the same and they take the
same role. However, suppose that, due to the extraction from different news sources, the
metamodel property sem:hasTime associated to post-event1 has value “August 26th,
2007” while the value associated to pre-event2 is “August 28th, 2007”. Then, using
this metalevel information and the local contents of the event modules, we can easily
write a reasoning rule that recognizes that the two events are incompatible and adds
the assertion event1 incompatibleWith event2 in the global context. Similarly, we can
recognize inconsistent situations by local reasoning: this can be used both to exclude
further inferences from inconsistent contexts, by marking as “inconsistent” the situation
individual in the metaknowledge, but also to repair (possibly with some ad-hoc rules)
the local axioms. We note that, on the other hand, this kind of reasoning requires to
define ad-hoc rules to recognize such different situations.
Another interesting possibility is the one of having inter-situation knowledge prop-
agation. For example, if two situations or two events are recognized as consequent in a
story, unmodified knowledge from the previous situations can be propagated to subse-
quent situations (e.g. the marital status of Obama did not change when he was elected
US president). This clearly presents problems of non-monotonicity, since one has to
consider which knowledge can be seamlessly propagated without incurring in contra-
dictory states. In this regard, we recently introduced in CKR a notion of defeasible
axioms and their overriding across different contexts [3].
5 Conclusions and future works
In this paper we introduced the model of the CKR-ESO ontology, a re-interpretation
of the Event and Situation Ontology under the contextual view of knowledge offered
by the CKR framework. We discussed informally the advantages of such representation
and demonstrated its application by means of an example.
We are currently completing the translation of the full ESO ontology to its contex-
tualized version: we remark that, given the direct translation across the two models, we
can easily automatize this transformation.
Our goal is to be able to apply some of the proposed complex reasoning services to
the events currently represented in the KnowledgeStore of the NewsReader project:
to this aim, we plan to integrate the RDF-based CKR implementation with the Knowl-
edgeStore and encode such reasoning services with respect to CKR contextual model.
Acknowledgments. The research leading to this paper was supported by the European
Union’s 7th Framework Programme via the NewsReader Project (ICT-316404).
References
1. Björkelund, A., Hafdell, L., Nugues, P.: Multilingual semantic role labelling. In: Proceedings
of CoNLL-2009. Boulder, CO, USA (2009)
2. Bozzato, L., Ghidini, C., Serafini, L.: Comparing contextual and flat representations of
knowledge: a concrete case about football data. In: K-CAP 2013. pp. 9–16. ACM (2013)
3. Bozzato, L., Eiter, T., Serafini, L.: Contextualized Knowledge Repositories with Justifiable
Exceptions. In: DL2014. CEUR-WP, vol. 1193, pp. 112–123. CEUR-WS.org (2014)
4. Bozzato, L., Homola, M., Serafini, L.: Towards More Effective Tableaux Reasoning for CKR.
In: DL2012. CEUR-WP, vol. 824, pp. 114–124. CEUR-WS.org (2012)
5. Bozzato, L., Serafini, L.: Materialization Calculus for Contexts in the Semantic Web. In:
DL2013. CEUR-WP, vol. 1014. CEUR-WS.org (2013)
6. Corcoglioniti, F., Rospocher, M., Cattoni, R., Magnini, B., Serafini, L.: Interlinking unstruc-
tured and structured knowledge in an integrated framework. In: Proc. of 7th IEEE Interna-
tional Conference on Semantic Computing (ICSC), Irvine, CA, USA (2013), (to appear)
7. Das, D., Schneider, N., Chen, D., Smith, N.: Probabilistic frame-semantic parsing. In: Human
Language Technologies: The 2010 Annual Conference of the North American Chapter of the
Association for Computational Linguistics. HLT’10 (2010)
8. Krötzsch, M.: Efficient inferencing for OWL EL. In: JELIA 2010. Lecture Notes in Computer
Science, vol. 6341, pp. 234–246. Springer (2010)
9. Masolo, C., Borgo, S., Gangemi, A., Guarino, N., Oltramari, A., Schneider, L.: DOLCE: a
descriptive ontology for linguistic and cognitive engineering. WonderWeb Project, Deliver-
able D17 v2 1 (2003)
10. Motik, B., Fokoue, A., Horrocks, I., Wu, Z., Lutz, C., Grau, B.C.: OWL 2 Web Ontology Lan-
guage Profiles. W3C recommendation, W3C (Oct 2009), http://www.w3.org/TR/2009/REC-
owl2-profiles-20091027/
11. Segers, R., Vossen, P., Rospocher, M., Serafini, L., Laparra, E., Rigau, G.: ESO: a Frame
based Ontology for Events and Implied Situations. In: Proceedings of the MAPLEX 2015
Workshop (2015)
12. Serafini, L., Homola, M.: Contextualized knowledge repositories for the semantic web. J. of
Web Semantics 12 (2012)