=Paper=
{{Paper
|id=None
|storemode=property
|title=An Ontology-based Adaptive Reporting Tool
|pdfUrl=https://ceur-ws.org/Vol-808/STIDS2011_CR_P1_MartesonEtAl.pdf
|volume=Vol-808
|dblpUrl=https://dblp.org/rec/conf/stids/MartensonHK11
}}
==An Ontology-based Adaptive Reporting Tool==
https://ceur-ws.org/Vol-808/STIDS2011_CR_P1_MartesonEtAl.pdf
An Ontology-based Adaptive Reporting Tool
Christian Mårtenson, Andreas Horndahl Ziaul Kabir
Swedish Defense Research Agency (FOI) The Royal Institute of Technology (KTH)
Stockholm, Sweden Stockholm, Sweden
firstname.lastname(at)foi .se mzkabir(at)kth.se
Abstract— Intelligence gathering by human observers is • The underlying information model is based on a shared
important for acquiring indirect and non-physical information. understanding, which can prevent misunderstandings
The drawback is that it is often delivered as free text which is not and increase interoperability on a semantic level
well-suited for further exploitation through automatic processing.
In this paper we present a concept for structured human However, the main argument for exploring the topic of
reporting based on an ontology-driven adaptive user-interface. structured data input is that it has the potential to deliver
The concept lays the foundation for the implementation of a completely accurate input already today. In addition, a direct
possibly hand-held in-field reporting system, which can adapt to correspondence between the manual input and the information
the context of the reporting situation as well as to possible model used by the input device greatly improves the conditions
information needs of other agents in the intelligence system. for accomplishing a computer based dialogue system.
Keywords-semantic technologies; ontologies; adaptive user In this paper we present a concept for structured human
interfaces; context aware interaction reporting based on an ontology-driven adaptable user-interface.
The concept lays the foundation for the implementation of a
I. INTRODUCTION possibly hand-held in-field reporting system, which can adapt
to the context of the reporting situation as well as to possible
In spite of constant technological advances, the nature of information needs of other agents in the intelligence system.
today’s conflicts has increased the importance of intelligence More specifically we put the following requirements on the
gathering by human observers. Automatic sensing systems do a system:
good job detecting and monitoring physical features like
vehicle or human movements, but for acquiring indirect • It should be intuitive to a non-expert, who is neither an
information and information referring to the cognitive domain ontology engineer nor a domain expert.
humans are still the main asset. This kind of information is • It should be domain independent, i.e. the system should
often referred to as soft data. The advantage of soft data is its work with ontologies from different domains.
high informational value; the drawback is that it is often
delivered as free text, which though human friendly is less • The output should be rdf-triples adhering to the
suitable for exploitation through automatic processing. Hence ontology.
an important issue in managing soft data is the transformation
of unstructured free text into structured content adhering to a • It should be adaptable to the context of the reporting
formalized information model. Techniques for automatic situation (who is reporting, what is the role of the
structuring of text include linguistic and statistical approaches reporter, where is the reporter, what time).
for entity and relation extraction. Such techniques are • It should be adaptable to the information needs of other
computational intense, often require a lot of training data and agents in the intelligence system.
are never completely accurate. In a human reporting system
these are limiting factors and alternative approaches are of Fig. 1 gives an overview of how the system is intended to
interest. adapt to capture external information needs. The user observes
an event and enters event information in the reporting system.
One might argue that speaking or writing in your native The output of the reporting system is semantic statements.
tongue is the most intuitive method for delivering a human These statements are matched with information needs from
message, and that issues regarding human reporting will be other parts of the systems, which also are expressed as
solved when language processing has been cultivated to semantic statements. If there is a match, the information need is
perfection or near perfection. However, the opposite approach, presented to the user as prioritized information to enter.
forcing the human reporter to directly input structured
information can have other benefits:
• The language is more precise, which can prevent the
user from making unintentional fuzzy statements
• The format is more compact, implying a potential for
faster input
• Graphical ontology query tools are visual query
systems that provide graphical notations to pictorially
express semantic queries to retrieve data from semantic
repositories. A number of scientific prototypes exist
[2][7][8], which all however require the users to have
knowledge about ontologies.
• The final approach for semantic query construction
support is to use forms. In its simplest form it is just a
predefined template, like an instance template in
Protégé. More advanced support can include auto-
completion, filtering and model checking [9].
In this paper we have due to the limitations of the other
approaches chosen to build on the ideas of “smart” forms,
extending them with more advanced methods for adaptation to
context and external information needs.
Figure 1. An overview of the process for capturing external information III. SCENARIO
needs. The following scenario illustrates the usage of the
suggested system:
II. RELATED WORK An army patrol is visiting a village. An officer of the patrol
There is not much work reported on supporting manual talks to the village leader who explains that the village was
input of semantic data (i.e. ontology instances). Standard visited by a group of Talibans the week before. The village
ontology editors, such as Protégé, allow instance creation but leader further describes the group as consisting of
require advanced user knowledge both regarding the domain approximately 100-150 people and that they were threatening
and ontology engineering. The Disciple-RKF system [1] the population in order to get food.
supports semantic user input through “knowledge elicitation The officer uses the reporting tool to enter information
scripts”, which specifies natural language queries to be shown about the event. After manually choosing “threatening” as the
to the user and then how to process the user’s answer main event type the tool automatically asks for related
semantically. This gives a good input support for a non-expert information, e.g. generic attributes as event “date” and
user, but requires an extensive manual work for the system “location”, but also attributes and relationships specific to
engineers when defining the scripts as the logic of the GUI is “threatening” like who is the “perpetrator” and “victim”. The
defined there rather than in the ontology itself. tool stores the information as triples in an rdf-repository. Once
More effort has been put into developing user friendly there, it is matched to external requests for information (RFIs)
systems for the querying of semantic repositories, although as which have been posted by other people in the system. In this
stated in [2] the works are mainly for ontology engineers and case there happens to be an RFI from the headquarter asking
not meant to assist domain experts or novice users. Semantic for information about what kind of weapons the Talibans
querying share common ground with semantic data input as it possess. The statements of the report that our patrol officer is
includes the creation of semantic statements, which are used as entering match this RFI as they are both about Talibans. The
templates for matching the repository content. There are at least match triggers the reporting tool to present the RFI, so that the
four approaches to support users in constructing semantic officer can make additional queries to the village leader.
queries: natural language, controlled natural language,
graphical editors and forms. IV. CONCEPTUAL DESIGN
• Natural language query interfaces for semantic A. Overview
querying is a daunting task as it involves all issues
related to natural language processing plus the The overall idea of the reporting system is that it should
additional constraint that the output must comply with adapt the interface based on what the user is reporting and take
a specific ontology. Its usability for querying large external information needs into consideration. In the event
semantic web database is discussed in [3]. reporting scenario described above, the system should be
loaded with a suitable military reporting ontology with
• Controlled natural language (CNL) defines a restricted attributes from e.g. the JC3IEDM. As an entry point the
form of natural language (e.g. English). It is used in a reporter is encouraged to report some basic event information
number of tools [4][5][6] developed for editing and consisting of the event type, time and place and information
querying ontologies. The disadvantage of CNL is that about the source (Fig. 2).
although the user can write and understand queries
there is still an issue with learning the specific rules
and boundaries of that particular CNL.
“Taliban” for the perpetrator, this will trigger a match with the
RFI. An additional field will emerge in the reporting tool
asking for weapons information (“B” in Fig. 3).
A starting point is to match actors, places and event types
between the event and external information need. If there is a
match, the user might possess or have access to additional
valuable information not reported yet. The matching process
could also be done by executing a SPARQL query on the
statements. If the result, with a degree of fuzziness, matches the
information, the system asks the user some additional
questions. A detailed description of the matching process is
given in Fig. 4.
Figure 2. Initially the interface only includes fields for basic event
information.
Depending on what event type is chosen, new fields will
emerge for the reporter to fill in. In the case of the Taliban
scenario, the reporter chooses “threatening” as event type and
will then be asked about which actors that were involved, there
respective roles (perpetrator or victim) and additional
properties that are related in the underlying ontology (“A” in
Fig. 3)
A
B
Figure 3. Depending on the user’s choice of event type, related actor types Figure 4. A detailed description of the matching process.
emerge as new tabs (A). External information needs (B) emerge when entered
information matches an RFI.
C. Adaptable interface
B. Matching external information needs The ontology can be used to filter out irrelevant input fields
In addition to adapting the user interface by adding or and selection options. Besides type definitions, an ontology
removing input options based on what the user enters, the also defines relationship types and specifies when and how the
system will also match the event description with external relationships can be used. A relationship type can be restricted
information needs. In the Taliban scenario, an external to only be valid from one kind of instance (domain) to another
information need had been registered in the form of an RFI, kind of instance type (range). Specifying domain and range
asking about the kind of weapons that the Talibans possess. provides means for creating a user interface with an increased
The RFI is expressed as a set of semantic statements, which level of usability since unsuitable input fields can be hidden.
allows semantic matching. When the reporter enters affiliation For instance, if the user wants to add a fact about an actor or an
event, only the properties that have the corresponding domain be used to answer any additional RFI related questions that the
will be accessible. system presents to the test person. The resulting report is then
compared to Part C and evaluated according to the following
The available input fields can in our concept also be measures:
prioritized. In a time critical situation, it’s important that the
observer focus on what’s important rather than trying to fill out • the time to enter the information,
all available fields. In a threat scenario, the victim’s ethnicity
may be a prioritized attribute to report, whereas in a crime • the correctness of the resulting report,
investigating scenario, the shoe size may be a relevant attribute. • the completeness of the entered information, and
How the attributes are prioritized are scenario and context • the number of RFIs that were correctly answered.
dependent. The priorities are also influenced by external RFI’s.
Consequently, the priorities are dynamic and the reporting
system should be able to adapt to new priorities on the fly. In ACKNOWLEDGMENT
order to speed up the reporting, available contextual This work was supported by the FOI research project
information should be used. This could mean automatically "Tools for information management and analysis", which is
inserting information about time and place (by using GPS funded by the R&D programme of the Swedish Armed Forces
information).
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