=Paper=
{{Paper
|id=Vol-2870/paper30
|storemode=property
|title=Knowledge Based Situation Awareness Process Based on Ontologies
|pdfUrl=https://ceur-ws.org/Vol-2870/paper30.pdf
|volume=Vol-2870
|authors=Yevhen Burov
|dblpUrl=https://dblp.org/rec/conf/colins/Burov21
}}
==Knowledge Based Situation Awareness Process Based on Ontologies==
https://ceur-ws.org/Vol-2870/paper30.pdf
Knowledge Based Situation Awareness Process Based on
Ontologies
Yevhen Burova
a
Lviv Polytechnic national university, St.Bandery street, 12, Lviv, 790013, Ukraine
Abstract
The growing complexity of modern world and inherent limitations of human cognition create
the need to offload the situation assessment and decision making to intelligent systems. The
achievement of situation awareness in such systems presents a major challenge because it
should include the patterns recognition and reasoning using knowledge about previous
situations. The representation of knowledge using ontologies is widely used for reasoning
implementation in intelligent systems. However, in systems with situational awareness the
complexity of existing ontologies and the limitations on expressivity precludes their efficient
usage, because decisions should be done in real time. In this article another approach,
formulated within the well-known JDL model of situational awareness process is proposed. It
is based on the usage of dynamically changing and small contextual, situational, task
ontologies and contextual graphs. The data transformations occurring on each level of JDL
model is described. The dependencies between different ontology types used on different
levels of JDL model are specified. The obtained results can be used as a basis for situational
awareness process.
Keywords 1
Situational awareness, ontology, context, situation, artificial intelligence
1. Introduction
We live in dynamic world where the problems arise spontaneously and unpredictably and are
requiring from us to make quick decisions. Situational awareness and decision making based on this
awareness are the important parts of human cognition. However, the growing complexity of modern
world and inherent limitations of humans in making fast and correct decisions in complex situations,
create a driving force to offload the part of situation assessment and decision making to intelligent,
computer-based systems. The implementation of situation awareness in such systems has become a
major challenge in the area of artificial intelligence.
The major factors, contributing to this challenge are:
• the need to combine both the perception and recognition of patterns in environment with
reasoning about them using conceptual knowledge;
• the need to implement focusing and selection in perceptive part and in reasoning part of the
system;
• the highly dynamic nature of environment leads to the need of constantly updating focusing
policies and used knowledge models;
• taking in consideration the contextual nature of knowledge, we need to quickly identify
current context;
• the decision making should be done in real-time.
COLINS-2021: 5th International Conference on Computational Linguistics and Intelligent Systems, April 22–23, 2021, Kharkiv, Ukraine
EMAIL: yevhen.v.burov@lpnu.ua (Y. Burov)
ORCID: 0000-0001-8653-1520 (Y. Burov)
©️ 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
Moreover, an intelligent system able to function in real world environment need to manifest the
full situation awareness process, including the perception on the world, interpreting its results,
identifying situation, making decisions, acting and correcting knowledge based on feedback.
2. Background research
According to definition [1] situational awareness is the continuous extraction of environmental
information, integration of this information with previous knowledge to form a coherent mental
picture, and the use of this picture in directing further perception and anticipating future events.
The concept of situational awareness was mentioned first during the First World War by the pilot
and military tactician Oswald Boelke, who argued the importance to realize the awareness of the
enemy before the enemy gets similar knowledge "[2]. The idea of the distinction between the
understanding of the state of the system by human operator and the actual state of the system
underlies the modern take on situational awareness.
The first studies on situational awareness as a part of decision-making systems were conducted for
military, aviation and other complex human-machine systems to support the activities of operators
[3]. In such systems the price of an error is very large, and the operator has to take into account a
large number of factors in the decision, so computer support of decision making becomes important.
In the process of situational awareness study multiple models were developed. They can be
classified as process, functional and formal models. The most known process model is John Boyd’s
Observe-Orient-Decide-Act (OODA) loop [4] or Predict-MatchExtract-Search loop [4]. Functional
models are represented by Endsley model [3] or JDL model [5]. The efforts to build formal models
are using different formal frameworks such as Category theory, generalized information theory,
interpreted systems, ontologies and specification languages to formalize situation awareness.
However, the most widely accepted framework of conceptualizing situational awareness process is
functional JDL model.
JDL model considers five levels in situational awareness process:
• level 0. Signal/Feature assessment. On this level the signals from various sensors are gathered
and interpreted as input data, corresponding to attributes of measured entities; The confidences of
measured data are assessed;
• level 1. Entity assessment. The data obtained are interpreted as attributes of entities from
knowledge base;
• level 2. Situation assessment. The entities involved in the current context and their
relationships are analyzed in order to build the model of situation;
• level 3. Impact assessment. Planning actions according to detected situations. Analyzing the
impact of the situation;
• level 4. Performance assessment. Evaluating the correspondence between current situation
and system goals, performance analysis.
JDL model is constantly evolving and is revised. For example in [6] the authors propose to extend
this model with remarks about co-processing with abductive/inductive logic, deductive inferencing
and support of distributed data fusion.
However, while JDL model describes the process of situational awareness, it does not reveal how
different types of knowledge are used and how they are processed in different levels of this model.
The accepted way to formally represent conceptual domain knowledge is to use ontologies –
formal specification of conceptualizations.
Ontologies are widely used to represent knowledge in JDL model [8,9]. Research often focuses on
representing different approaches to reasoning, supporting situation identification and decision
making in the context of JDL model. For example, [10] proposes the enhancement of JDL model with
processing complex events structures, building actionable abstractions from event streams and
dependencies.
The assessing of the situation and making decisions are often required to be done in real time.
Therefore, it is important to simplify the process and decrease the resources usage by adding
constraints and reducing the number of concepts and relations from real world which should be taken
into consideration.
The attempts to formalize domain conceptualization in form of an ontology usually produce large
ontologies. For example, Cyc ontology has hundreds of thousands concepts and more than
million rules [11], the number of concepts and Worldnet has more than 117 thousands of synsets
(synonym groups) [12].
The different aspects of ontology complexity are addressed in current research separately. One of
the important components of ontology complexity is structural complexity. In [13] an ontology
is considered as a kind of a complex, compositional system. The author states the two main
causes of such systems complexity. The first one is the nonlinear increase of the dependencies
between elements, when the number of elements grows. The second one roots in the sensitivity of the
whole system even to the minor change. Every such change in one element propagates to others,
dependent objects and thus requires the review of the whole ontology.
The early idea of counteracting the structural complexity of ontology was to split the general
ontology into parts according to generality/granularity of used concepts level. The reusable part
became top (upper ontology) and specialized part domain ontology. This created additional problem
of integrating top and domain ontologies [14]. Moreover, domain ontologies remained too structurally
complex and redundant when resolving specific practical problems or used in specific business
environments.
One of the possible solutions for subjectivity in ontology creation is the use of well-researched
sets of foundational ontology components. The effort of revealing and formalizing the most basic
concepts and relations which can be reused consistently across multiple ontologies resulted in creation
of foundational ontologies SUMO, UFO, and GFO [15, 16] and libraries of objects and patterns
such as [17].
Another solution to the complexity problem is modularization and partitioning of ontologies.
In [18] the large ontology is split into smaller modules using taxonomy structure. The authors of
[19] propose extracting a module by specifying the part of larger ontology based on specific set of
interrelated concepts, which form the signature of the module. The module defines the subset of
knowledge, which can be used separately from original ontology for specified tasks.
The modern research in the domain of ontologies tend to express ontologies as set of smaller
patterns and patterns groups [20], paving a way to the development of pattern languages [21].
Foundational ontology GFO [16] introduces the first class ontology object types to describe
situations. Those classes are Situation, defined as “a special configuration which can be
comprehended as a whole and satisfies certain conditions of unity, which are imposed by relations and
categories associated with the situation” and Situoid, defined as “the processes with the boundaries –
situations”. Situoid is a part of the world that is coherent whole and does not need other entities to
exist. Every situoid is framed by object types specifying time characteristics (cronoid) and spatial data
(topoid).
Another dimension, adding to the complexity of situational awareness implementation is the need
to take into consideration different perspectives, goals and intents of agent and also context – as
environment state influencing understanding and decision making. The same concept can have
different conceptualizations depending on the situation, context and agent perspective, dictated by his
intents.
The problem of formalizing contexts and reasoning within them has been widely researched. In
[22] are specified the requirements that context modelling and reasoning techniques should meet,
including the modelling of a variety of context information types and their relationships, of situations
as abstractions of context information facts, of histories of context information, and of uncertainty of
contextual information.
The article [23] considers the idea of recognizing contextual situations using process mining
techniques. The authors propose to use fuzzy matching techniques for situation identification.
In [24] a Context-aware Decision Support (CaDS) system is described, which consists of a
situation model for shared situation awareness modelling and a group of entity agents, one for each
individual user, for focused and customized decision support. By incorporating a rule-based inference
engine, the entity agents provide functions including event classification, action recommendation, and
proactive decision making.
The analysis of existing research in the domain of situational awareness shows that, we cannot use
a single ontology in situation awareness process. It would be very cumbersome, complex to handle
and inflexible. We would rather use a network of simpler, dynamically constructed ontologies,
reflecting the situation, context and the intent of agent in this situation. To correctly specify the
situational knowledge and allow to make correct decisions, those ontologies should have elements
with meanings adequately describing objects and conditions related to the situation.
This article intends to investigate how different forms of ontologies and knowledge models are
used in the situational awareness process represented by JDL model. We will clarify the definitions
and establish dependencies of different smaller ontological knowledge models such as contextual,
situational, task ontologies, contextual graphs and where they are used in the situational awareness
process.
3. Situation awareness process model
First, let’s delineate the main premises of our research:
• As a basis we use the modified JDL model [6] of situation aware system;
• Ontologies are used to model knowledge, necessary to adapt to current situation and make
decisions;
• In order to reduce the complexity, different small ontologies, selected based on current
context are used, which helps to reduce the number of considered situations.
3.1. Zero level of JDL model
On the zero level of JDL model intelligent agent (Ag) is obtaining data from the world. This data
is captured by sensors 𝑆𝑛𝑖 (𝑖 = 1 ÷ 𝑛) and relayed to situational awareness system for further
processing. The amount of data read is constrained by agent’s intent and its properties (intrinsic
limitations). The agent can focus on the part of the sensors, prioritize some sensors over others,
controlling the amount of data read from specific sensor or sensor groups. This control can be also
produced by feedback loops coming from the next levels of situation awareness process, shifting the
focus of observation depending on current situation (Fig.1).
has
observes Agent
Goals/intents
has constrains
Knowledge/
Context/World
properties
constrains
Figure 1: Zero level objects and dependencies
The Context (Con) is the part of the possible world (W) observed by agent. It can be understood as
the environment perceived by the agent in the current moment. The context depends on agent’s
location, intents, and state.
3.2. The first level of JDL model
In the first stage of JDL model saw system obtains data from sensors and interprets them as
attributes/parameters values of concepts from Contextual ontology. This ontology is a formal
conceptualization of the part of the world corresponding to context, observed by agent. Contextual
ontology is constructed as a result of combination (or extraction) from other domain ontologies,
available to the agent, where perceived context and agent’s intent work as a selection filter defining
what will be included in contextual ontology (Fig.2).
Figure 2: First level transformation
On this level the system receives a vector 𝑋̅ = (𝑥1 , 𝑥2 , … , 𝑥𝑛 ) of values read from sensors. As the
result of processing those data a change in the fact information base 𝑡𝐼𝐵 ′ is generated. This change is
comprised with changed facts, which include both concept and relation facts 𝑡𝑖 .
𝑡𝐼𝐵 ′ = (𝑡1 , 𝑡2 , … , 𝑡𝑘 ). (1)
Each changed fact belongs to one or more classes (concepts) of Contextual ontology:
∀𝑡𝑖 : 𝑇𝑦𝑝𝑒(𝑡𝑖 ) = {𝑇1 , 𝑇2 , … , 𝑇𝑚 }, 𝑚 > 0, ∀𝑗: 𝑇𝑗 ∈ 𝑂𝑛𝑐𝑜𝑛 ⊆ 𝑂𝑛, (2)
where 𝑂𝑛𝑐𝑜𝑛 is a contextual ontology and 𝑂𝑛 is ontology (or set of ontologies) used for construction
of 𝑂𝑛𝑐𝑜𝑛 . The ontology 𝑂𝑛 is a source of conceptual knowledge used to update Contextual ontology
over time when the current context changes.
The data processing (mapping) performed on the first stage of JDL model is not straightforward.
Currently to implement it we often use the neural networks algorithms to capture and recognize
shapes and objects of specific classes from contextual ontology in the real world (such as automobile,
a tree, a pedestrian, a cyclist etc) in the raw data flow produced by sensors. So we assume that the
concepts instances are recognized first and their parameters in information base are changed second.
As a result of the first level entity assessment a conceptual model 𝐶𝑚𝑐𝑜𝑛 of current context (a
scene) is formed using concepts and relations from the contextual ontology.
The context is constantly changing with the flow of time. Therefore, the contextual ontology and
conceptual model of context is changing too adding new and discarding non relevant concepts and
relations, updating facts identified in previous time moments. The task of identification of entities is
simplified by reusing the entities identified in previous time moments, because in practice there are
only small changes introduced in context from one moment to another. For example, if an automobile
was identified in the near lane in the previous moment of time, there’s no need to identify it again, we
can only change its parameters.
The conceptual model built in previous time moments can help to identify and reason about new
objects introduced into Context, using the affinity of elements belonging to the current scene to
simplify the detection of new elements. For example, when on the road we expect to see other
vehicles, road signs, pedestrians, cyclists etc. While being in kitchen we are likely to see tables,
kitchen utensils, an owen.
Therefore, the conceptual model of Context and corresponding contextual ontology is changing
over time. However, for the long periods of time the differences between conceptual models in
adjacent moments are minimal. Based on similarities between contextual ontologies we can discern
typical Contexts. (environments) in which Agent is placed. In each of those Contexts different
contextual ontologies are viable, different sets of situations can be detected, and different actions
taken.
3.3. Second level of JDL model – Situation assessment
In the second level of JDL model a situation assessment is provided. We will understand a
situation as a state of the world which presents some pragmatic (depending on agent’s values and
goals) interest to agent (and therefore being worth to be identified).
As an input on this level the current conceptual model of context 𝐶𝑚𝑐𝑜𝑛,𝑖 and the content of
information base 𝐼𝐵 is used. As a result, the set of identified situations 𝑆𝑡 = {𝑠𝑡1 , 𝑠𝑡2 , … , 𝑠𝑡𝑚 } is
produced (Fig.3).
Figure 3: Second level transformation
The contextual ontology serves as a filter, limiting the number of items to be looked for in IB. The
situations 𝑆𝑖𝑡 = {𝑆𝑖𝑡𝑗 , 𝑗 = 1 ÷ 𝑛} are stored as conceptual models – patterns of situations - in a
separate repository. Each conceptual model of situation 𝐶𝑚𝑠𝑖𝑡,𝑖 is described by specific situational
ontology 𝑂𝑛𝑠𝑖𝑡,𝑖 , with classes, relations and axioms belonging to this situational pattern. Situations
occur in current context. In order to be correctly identified there should be a mapping between subsets
of elements from contextual and situational ontology:
∀𝑖∃1 𝑀𝑎𝑝𝑜𝑛𝑡 : 𝑆𝑢𝑏(𝑂𝑛𝑐𝑜𝑛 ) → 𝑆𝑢𝑏(𝑂𝑛𝑠𝑖𝑡,𝑖 ). (3)
Therefore, contextual and situational ontologies partly overlap and contextual ontology provide
factual values for some elements of situational ontology. However, some elements from contextual
ontology can be not present in situational ontology, being not relevant to the situation. On the other
hand, situational ontology may contain references to data, not available in contextual ontology, such
as historical, geographical, normative data. Those data should be obtained additionally from external
sources in order to fully identify the situation.
Current context 𝐶𝑜𝑛𝑖 limits the number of possible situations, which can occur in this context -
𝑆𝑖𝑡𝑐𝑜𝑛,𝑖 .
One way to identify the set of possibly relevant situational patterns is to use the similarity between
contextual and situational ontologies. However in practice, the look up into the list of possible
situations is not efficient, especially if situation identification should be done in real time. The more
efficient method is looking for cues, where cue is the combination of specific parameters values (or
ranges of values) from the objects in contextual ontology, which can point to the probable situation
(like symptoms point into possible disease).
𝐶𝑢𝑒𝑘 = (𝐶𝑠𝑘 , 𝑆𝑢𝑏𝑘 (𝑆𝑖𝑡𝑐𝑜𝑛,𝑖 )), (4)
where 𝐶𝑠𝑘 is a set of conditions, defined on the values of elements from contextual ontology, which
should be true for the specified subset of situations 𝑆𝑢𝑏𝑘 (𝑆𝑖𝑡𝑐𝑜𝑛,𝑖 ) to occur.
While many different situations can be identified in current context it is important to prioritize
situations as early as possible in the process of identification and focus resources on most important
situations. Giving the large number of concepts, relations and their attributes in contextual ontology it
is important to detect important cues and focus attention on them as early as possible.
In order to detect the important situation earlier the cues could be organized in hierarchies (or
networks), where each parent node will encompass cue for multiple possible situations, child nodes
will contain the specialization and elaboration of cues for more specific situations and arcs, leading to
child node will be weighted with relative importance for corresponding situations, so they will be
visited first in the process of analysis.
There could be different approaches to triggering the process of detecting cues in the process of
constant monitoring of data state in information base:
• based on anomalies, when compared to baseline values;
• based on the known lists of situations to be checked in the current context;
• using neural networks trained on real life cases to detect cues pointing to specific situations or
groups of situations.
The baseline using approach monitors a set of important attributes and detects any deviation from
baseline values. This detection can either point to specific situation pattern in the repository, or in
initiate additional probing of parameters, and reasoning aiming to get more details in order to identify
a situation.
The second approach is like running through checklist and is a standard operational procedure, for
example, for surgery teams or airline pilots. Each typical context 𝐶𝑜𝑛𝑖 has associated checklists. Each
checklist contains the list of conditions, specified using the values of attributes from contextual
ontology. The deviations from those conditions points to specific situation or requires and additional
investigation.
In the approach when situations are detected using neural network, this network is trained with real
life situations examples to incorporate the cues and their importance into the fabric of neural network.
The neural network approach is used in artificial intelligence applications.
3.4. Third level of JDL model – Impact assessment
On the third level of JDL model, based on identified situation we have chosen to act upon, we
make decision about required actions, specify those actions in more detail, and assess the impact of
those actions’ execution.
As an input on this level the conceptual model of selected situation 𝐶𝑚𝑠𝑖𝑡 , situational ontology
𝑂𝑛𝑠𝑖𝑡 and the content of information base 𝐼𝐵 is used. As a result, the contextual graph 𝐺𝑟𝑐𝑜𝑛 = () as
structure of linked context evaluation nodes and action nodes is produced (Fig.4).
Figure 4: Third level transformation
Next, the Agent should make decision about the course of actions to undertake given the identified
situation. Basically, this requires answering two questions: a) what change in the world is desirable
given situation identified? b) how to introduce this change?
In most cases, once the situation pattern is identified, the set of possible decisions/actions is
already known. This is the basis of Natural Decision Making (NDM) approach. In case, if there are
several different alternative decisions available, we can use the methods of decision theory, such as
analytical hierarchy analysis, to select the decision preferable according to specified criteria from
several alternatives.
The goal of decision implementation can be specified as desired change of the state of the world,
defined on the elements of situational ontology.
Let’s 𝑡𝐼𝐵 be the state of information base, which describes the state of the world, as the Agent sees
it. We introduce the algebraic type of Goal 𝑇𝐺𝐿 with instances specifying the set of states of
information base, where goal conditions are met.
𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛(𝑇𝐺𝐿 ) = 𝑆𝑢𝑏(𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛(𝑇𝐼𝐵 )) = 𝑡̂𝐺𝐿 . (5)
The goal function 𝐹𝐺𝐿 (𝑡𝐼𝐵 ) allow to find whether in specific state of information base the goal is
met:
`
𝑡𝑟𝑢𝑒 |𝑡𝐼𝐵 ∈ 𝑡̂𝐺𝐿 (6)
𝐹𝐺𝐿 (𝑡𝐼𝐵 ) = { .
𝑓𝑎𝑙𝑠𝑒 |𝑡𝐼𝐵 ∉ 𝑡̂𝐺𝐿
One of possible ways to define the goal function 𝐹𝐺𝐿 (𝑡𝐼𝐵 ) is to represent it as a list of assertions
𝐿(𝑡𝐴𝑆𝑅 (𝑡𝐼𝐵 )) formulated on the state of information base 𝑡𝐼𝐵 , and which should be true and cover all
required conditions for the goal:
𝐹𝐺𝐿 (𝑡𝐼𝐵 ) = 𝐿(𝑡𝐴𝑆𝑅 (𝑡𝐼𝐵 )). (7)
The definition of goal function is also helpful on the later level of assessment whether the goal was
reached as the result of actions performed.
The process of implementing the change according to specific goal depends on factual values of
attributes in the conceptual model of situation. It can be formalized using contextual graph [26].
The model of context graphs was developed for the analysis of variants of processing of abnormal
situations by subway operators [25]. The authors of this model note that usually the list of possible
abnormal situations is well known and formalized in the company's contingency documents.
However, the specific ways to resolve the abnormal situation depends on the conditions of its
occurrence (context) and are determined by the human operator individually, based on experience.
This method of solving the problem in [26] is called a practice. The task of context graphs is to
explicitly define contextual data that influence the choice of a practice and present a sequence of
possible practices for a particular situation indicating what information is used from the conceptual
model of situation for each practice.
A context graph is an acyclic graph (Fig. 5) with one vertex-input and one vertex-output. The input
vertex is the identified situation (with associated goal) that needs to be processed. A context graph has
two types of vertices: context elements and actions. Contextual elements reflect contextual knowledge
- that is, knowledge relevant to a given configuration of values from the instance of conceptual model
of situation. The structure of this knowledge is specified by contextual element ontology 𝑂𝑛𝑐𝑜𝑛−𝑒𝑙 ⊆
𝑂𝑛𝑠𝑖𝑡 which is a fragment of situational ontology. More precisely, the context element reflects
relevant data, information and knowledge. The arcs of the graph emanating from the vertices of the
context elements have marks that specify which part of the contextual knowledge is used by this or
that practice (called procedural context). In addition to the context vertices that branch, reflecting
different options for solving the problem, there are joining vertices, in which the different path of the
process merge again.
Figure 5: An example of contextual graph structure
3.5. Performing actions and evaluating results on level four of JDL model
After the course of actions is selected, those actions should be performed and results are evaluated
against the goal, defined in previous level.
Figure 6: Fourth level transformation
The processing on this level receives as an input the contextual graph 𝐺𝑟𝑐𝑜𝑛 , the ontology of
situation 𝑂𝑛𝑠𝑖𝑡 , current state of information base 𝑡𝐼𝐵 . The actions, specified in contextual graphs are
executed and resulting state of information base (after updating it with new facts) 𝑡𝐼𝐵 ′ is evaluated
against the goal 𝐹𝐺𝐿 (Fig. 6).
The actions in contextual graph are specific tasks models. Each task corresponds to an elementary
action, which is considered as a whole and not decomposed into smaller actions. With each a task’s
goal is associated, which allows to check whether task execution was successful or not. The task
knowledge is reused in multiple situations and presents a body of knowledge separate from the
knowledge about contexts or situations. This knowledge is stored in the separate repository of tasks
models.
The interest in formalizing task knowledge structure dates from the early ages of information
technologies development, on the first work on this topic being [27]. This research started with
investigation of task structures performed by humans, with a purpose to build better human-machine
interfaces. Today the research on tasks knowledge shifts in direction of implementing task executing
robots, and use of ontologies for task knowledge specification.
Each task type 𝑇𝑡𝑎𝑠𝑘,𝑗 is described by conceptual model of task 𝐶𝑚𝑡𝑎𝑠𝑘,𝑗 which, in turn is
formulated based on the task ontology 𝑂𝑛𝑡𝑎𝑠𝑘,𝑗 .
In order to be instantiated and executed the conceptual model of task should receive parameter
values from the instance of contextual element in contextual graph it is linked to. Therefore, there
should be a mapping between the contextual element ontology and task ontology.
𝑀𝑎𝑝𝑐𝑜𝑛−𝑒𝑙,𝑡𝑎𝑠𝑘 : 𝑂𝑛𝑐𝑜𝑛−𝑒𝑟 → 𝑂𝑛𝑡𝑎𝑠𝑘 . (8)
During the execution of contextual graph tasks, the constantly repeating action pattern is to
evaluate the success of task execution by checking whether task goal was reached. This is done by
collecting the information from the environment (Context) into the information base (Level 1 and 2)
of the process and checking the state of information base against the goal conditions. The success/fail
of task execution defines the changes the course of actions in contextual graph. For example, in data
transmission failed, the system can retry transmission or renounce from it altogether. Such
verification is also done for the contextual graph as a whole, after last action is successfully
performed.
The results of actions from contextual graph may be not immediately manifested. Therefore, each
goal can have a specified time delay after which the success of situation processing should be
evaluated.
The evaluation of results is performed as a separate process, loosely connected with a history of
previous situation identifications, and provides information for analysis and leads to eventual update
of situation or task knowledge.
4. Conclusions and discussion
The implementation of situational awareness is a major challenge in the area of intelligent
information technologies. To do this, we need to achieve the fusion of data, obtained from sensors and
analog reasoning, such as neural networks with conceptual reasoning based on logic and models. The
conceptualizations are formalized and processed using methods of ontology engineering. Due to large
size of domain ontologies, the reasoning and decision making with them is quite limited and resource
intensive. However, acquiring situational awareness should be done in real- time, because decisions
and corresponding actions, depending on this awareness also should done quickly. In this article I
argue, that situational awareness process should be based on small ontologies, relevant to current
context and situations, which can occur in this context. The ontology- based situational awareness
process is based on JDL model. The process uses contextual, situational ontologies and contextual
graphs. The transformation, provided on each level of JDL model, using those knowledge structures is
described.
The usage of smaller contextual ontologies allows reducing the size of model, used for decisions.
From the analysis of elements included in contextual model, we can deduce the set of possible
situations, and identify situations, based on cues, depending on the values of attributes of entities
included in contextual model.
Since the context in real world keeps constantly changing, the contextual model and the set of
possible situations also keep changing from one moment to another. However, the large part of
context from previous moment tends to be preserved in next one. The research on dynamically
changing context and correspondingly updates on contextual ontology present a promising area for
future research.
Another promising trajectory of development is aimed at analysis, formalization, and development
of implementation methods for changes based on feedback, coming from higher levels of JDL model
to lower levels. Thus, we would be able, for example to update the selection of data from sensors,
focusing the attention of system on important objects found during situation assessment level.
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