=Paper=
{{Paper
|id=Vol-2043/paper-06
|storemode=property
|title=An Ontology Design Pattern for Microblog Entries
|pdfUrl=https://ceur-ws.org/Vol-2043/paper-06.pdf
|volume=Vol-2043
|authors=Cogan Shimizu,Michelle Cheatham
|dblpUrl=https://dblp.org/rec/conf/semweb/ShimizuC17
}}
==An Ontology Design Pattern for Microblog Entries==
https://ceur-ws.org/Vol-2043/paper-06.pdf
An Ontology Design Pattern for Microblog
Entries
Cogan Shimizu and Michelle Cheatham
Data Semantics Laboratory, Wright State University, Dayton, OH, USA
Abstract. Due to the exponential growth of the Internet of Things and
use of Social Media Platforms, observers have an unprecedented level
of detailed information available on the behavior of communities. How-
ever, due to the highly heterogeneous nature and the immense volume
of the data, a composite view is difficult to generate. Such a compos-
ite view would be exceptionally useful in the realms of insider threat
detection, after-action forensics, and hazardous situation detection and
avoidance. The Semantic Web, via ontology modeling, offers a powerful
tool for fusing the disparate data sources and formats. To this end, we
have created an ontology design pattern (ODP) for the modeling of a
simple microblog entry. This ODP is intended to fit within an ecosystem
for fusing social media, support advanced visualization, and provide a
preliminary framework for trust assessment.
1 Motivation & Scope
In recent years, access to data has become increasingly trivial as Social Media
Platforms and the Internet of Things (IoT) continue to grow. However, important
latent or implicit information runs the risk of obfuscation simply by the sheer
volume of collected data. Further, the data is presented and accessed via highly
disparate vectors (e.g. microblog entries, visual media, and geotagged textual
data). Thus, it is increasingly necessary to identify and develop methods for
seamless fusion and visualization of information extracted from heterogeneous
social media data.
Such methods are especially important for obtaining an accurate and com-
prehensive view of a crisis theater or battlespace (e.g. formulating a “Common
Operating Picture”1 ). For these use cases, it is also important to take into ac-
count the provenance and trustworthiness of the acquired data and for any con-
clusions drawn from such data. To support the fusion of such heterogeneous data
and the capture of its metadata, we will build an ecosystem of ontology design
patterns [6]. ODPs enable sophisticated visualizations that leverage the inherent
concept hierarchy, such as models displaying varying levels of granularity and
interconnectedness. Figure 1, provides two examples of possible visualization
methods that the microblog entry (MBE) will help support. We are currently
1
A Common Operating Picture is a single identical display of relevant operational
information on materiel shared by more than one Command. This term is frequently
investigating other visualizations in collaboration with domain experts from the
United States Air Force. In this paper, we describe a pattern for a MBE as an
entry point into developing the ecosystem.
The MBE pattern is important for a number of reasons. First, microblog en-
tries are representative of a fairly large subset of publicly available social media
data. For example, Twitter2 the popular, public-facing microblogging platform,
allows a Tweet’s payload to contain text, hyperlinks, images, or video. The en-
tries may also be geotagged and may explicitly refer to other users. Additionally,
there are many existing datasets that capture Tweets during natural disasters
and humanitarian crises (e.g. CrisisLex3 ).
By definition and intent, microtext4 is simple; its model is relatively straight-
forward and requires little of the complexity that OWL brings to the table. Re-
gardless, it is important to note that this pattern is a fundamental building block
of the intended ODP ecosystem. However, due to its simplicity, it is relatively
straightforward to fit with many existing patterns. Specifically, we foresee easy
integration with the ModifiedHazardousSituation Design Pattern [4] and Re-
portingEvent [7]. As the ecosystem matures, we also foresee including existing
patterns regarding maps, climate, and public infrastructure.
Finally, the MBE pattern has some components that allow for interesting
interaction: spatiotemporal extent and author trustworthiness. Spatiotemporal
extent of information is of particular interest to the modeling community as there
are still many open questions on its handling. However, it is an integral part of
any sort of response or intelligence operation. In a perfect world, we could assume
that any author neither seeks to mislead nor propagate lies. However, in light of
recent events, as well as the ODP’s relevance to crisis and operational intelligence
management, it is necessary to include a component for the trustworthiness of
an author. Thus, the model for the microblog entry seeks to answer, at least,
the following competency questions. Due to the strong emphasis on geospatial
and temporal components of the fused data, we assume that these queries will
be executed using geoSPARQL5 .
1. Who is the author of entry x ?
2. What are all the entries authored by y?
3. What entries from time A to time B originate from region of interest C with
radius D?
4. What is the trust value v for author y?
5. What is the trust value v for entry x ?
6. What entries from authors with a trust value greater than v originate from
a region of C with radius D?
7. What entries relate to topic T ?
2
https://twitter.com
3
http://crisislex.org/
4
Microtext is any sufficiently short parcel of information in natural language. An
MBE is an instance of microtext.
5
http://www.opengeospatial.org/standards/geosparql
(a) A Circle Packing visualization (b) A standard view of geographic
generated by D36 . Smaller circles information: pins on a map back-
are related to the superimposed ground. This visualization can be
circle via subsumption and prox- updated in real-time and allows
imity in the same level of circle de- the user to see incoming data.
notes a short semantic distance.
Fig. 1: Both visualizations will utilize the MBE pattern at the most granular
level (i.e. smallest circles and map pins).
Microtext is a valuable resource in the Semantic Web Community, as evi-
denced by [2, 9, 10, 8]. However, to our knowledge this is the first attempt at
modeling an MBE as an entity, instead of only modeling extracted information.
The rest of the paper is organized as follows. Section 2 will address the design
decisions in the structure of the pattern and accompanying axioms. Section 3
provides a motivating example and interaction with real data. Section 4 addresses
future work and collaborations.
2 Pattern Overview
This pattern was directly informed by the competency questions in the preced-
ing section; the competency questions are fairly straightforward and have a one
to one correspondence with the concepts in the pattern. As such, the microblog
entry pattern must capture both the entry’s payload and its provenance. In ad-
dition, it must capture any information extracted from the payload and analysis
of the author, such as answers to the questions: “To what is the microblog entry
referring?” or “How trusted is the author by their peers?”
We will discuss the main design aspects of this pattern by referring its class
diagram as depicted in Figure 2. Yellow boxes indicate datatypes, light blue
boxes with dashed borders indicate external patterns. Purple is used for external
6
Circle Packing is an arrangement of circles on a surface so that all circles touch one
another. D3 is a powerful JavaScript library used for generating visualizations.
Fig. 2: A graphical representation of the microblog entry design pattern. Yellow
boxes indicate datatypes, light blue boxes with dashed borders indicate external
patterns. Purple is used for external classes belonging to PROV-O [5]. Green
is used for external classes belonging to [7]. White arrowheads represent the
owl:SubclassOf relation.
classes belonging to PROV-O [5]. Green depicts external classes belonging to [7].
White arrowheads represent the owl:SubclassOf relation.
By indicating several of the classes as “external,” we intend to convey that
the models for said classes are not indicative of the functionality of the Mi-
croblogEntry pattern. For example, in our implementation7 the light blue boxes
are currently wrappers for datatypes. However, it is not hard to imagine increas-
ingly complex models for each class. Below, we will discuss our implementation
and future iterations. We will consider the pattern in the context of our use-case:
event detection during a crisis. Furthermore, we assume that any microblog en-
try populating the ontology occurs within the time-frame and are shown to be
relevant to the crisis situation.
MicroblogEntry The MicroblogEntry is the core class. Here, we will describe
a few limitations placed upon its relations.
7
The OWL file can be found at https://raw.githubusercontent.com/
cogan-shimizu-wsu/MicroblogEntryOWL/master/MicroblogEntry.owl
MicroblogEntry v =1hasPayload.Payload (1)
MicroblogEntry v =1hasAuthor.Author (2)
MicroblogEntry v ≤1hasLocation.Location (3)
1. A MicroblogEntry may only have one Payload.
2. A MicroblogEntry may only have one Author.
3. A MicroblogEntry might not have a location attached to it.
ReportingEvent The ReportingEvent pattern is documented in [7]. This es-
tablished pattern provides for a lot of interplay with MicroblogEntry, as well as
providing structure for how information is shared.
As ReportingEvent is itself a subclass of Situation, it will be reasonably straight-
forward to integrate the ModifiedHazardousSituation [4] pattern to the Microblo-
gEntry. Additionally, ReportingEvent provides a framework for connecting the
“report” to an ActualEvent; thus, along with Topic, ground the MicroblogEntry
in reality. Finally, the fact that a ReportingEvent isBasedOn a Source, provides
us a vehicle for capturing the fact that a MicroblogEntry has been re-Tweeted or
shared (without modification).
Media The Media class allows us to represent the platform on which the Mi-
croblogEntry was posted. In the case of our example in the next section, this
would be Twitter. However, it is also conceivable that Media may represent
CNN, Fox News, BBC, and so on. Obviously, these establishments are fairly
complex in their own right.
Media is also drawn from [7], though is largely left for others to implement.
Monitoring different Media will be very important in our use case scenario, es-
pecially when considering the TrustMetric for provenance and author. To this
point, it seems reasonable to expect the trustworthiness of the platform and
corporation to effect the trustworthiness of the reported data.
Payload The Payload is the content of the MicroblogEntry. In Figure 3, this is the
content in Box 2. For the general pattern, we opted to leave this as an external
pattern due to the expected heterogeneity of MBEs of different platforms and
even high variance of content on the same platform. That is, Twitter allows for
many different payloads: text, hyperlinks, images, and videos. Facebook, on the
other hand, offers a superset of content types and no length restriction on text
payloads.
In addition, we see the Payload playing a large role in defining how MBEs will
interact with each other. In the case of Tweets, a Tweet may be “Retweeted,”
thus embedding a Tweet inside of a Payload. Furthermore, a Payload may “men-
tion” another user or author. Our next steps will include ways to more accurately
model these relationships between Authors, Payloads, and MicroblogEntries.
For our initial implementation, as our test sets do not include Tweets with
pictures or hyperlinks, Payload wraps an xsd:string. Additionally, relevant Mi-
croblogEntries must have a relevant Payload. That is, the Payload must refer to
some Topic relevant to the crisis situation.
Topic In some cases, it may make sense to have Topic include a targeted list
of terms from a controlled vocabulary. Or, instead, to have the Topic act as a
category. For example, in [3], Tweets were partitioned into the following cate-
gories: affected individuals, infrastructures and utilities, donations and volun-
teer, caution and advice, sympathy and emotional support, useful information
and unknown.
Our implementation currently wraps an xsd:string. This allows us to dynam-
ically generate a Topic as Tweets are encountered. As the intended ODP ecosys-
tem matures, it is conceivable that this Topic sub-pattern will be more fully
fleshed out, allowing for more interesting interaction between MicroblogEntries
referencing the same Topic.
Location There are many methods for representing location, e.g. the POI:Place
[1] pattern or using WellKnownText (WKT) from OpenGIS, among others. To
promote reusability, we do not constrain the top-level pattern to use one or
another. In our implementation, however, we opted to use a WKT literal for
simplicity’s sake. In the future, we expect to be able to augment this part of the
model by including relevant descriptors, such as the name of the location taken
from a gazetteer.
TrustMetric The TrustMetric sub-pattern has the potential to be the most
complex due to its far reaching effects on the interplay between Author, Payload,
and Media. In addition, the actual metric for trust will need its own provenance
and uncertainty measures. Until the system is actually implemented, it will be
difficult to completely model. Thus, in our implementation, we assume we are
getting a value between 0 and 1 from some black-box system. As such, we wrap
xsd:double.
3 Example Triples
Figure 3 shows an example Tweet. The relevant data that will be extracted has
been boxed in red.
kast:CarAccident ## Extracted from Box 2
rdf:type t:Topic;
t:hasName "Car Accident"^^xsd:string;
.
kast:Evacuation ## Extracted from Box 2
Fig. 3: An example Tweet with extracted data highlighted in red. Note, this
example does not have a geolocation.
rdf:type t:Topic;
t:hasName "Evacuation"^^xsd:string;
.
kast:examplepayload ## Extracted from Box 2
rdf:type pl:Payload;
kast:hasvalue "There is a car accident on 4th and
Main. Be careful out there!
#evac"^^xsd:string;
kast:referencesTopic kast:CarAccident, kast:Evacuation;
.
kast:cogantm ## Note here that there are two trust metrics.
rdf:type tm:TrustMetric;
tm:hasValue .99^^xsd:double;
.
kast:mbetm ## As trust in author is distinct from trust in the MBE.
rdf:type tm:TrustMetric;
tm:hasValue .89^^xsd:double;
.
kast:CoganShimizu ## Extracted from Box 1
a prov:Person, prov:Agent;
foaf:givenName "Cogan Shimizu"^^xsd:string;
kast:hasTrustMetric kast:cogantm;
.
kast:Twitter
rdf:type pz:Media, prov:Entity;
.
kast:examplets ## Extracted from Box 3
rdf:type time:Instant;
time:inXSDDateTimeStamp "2017-07-12T10:01:00-5:00"^^xsd:dateTimeStamp;
.
And finally,
kast:exampletweet
rdf:type kast:MicroblogEntry, pz:ReportingEvent;
kast:hasPayload kast:examplepayload;
kast:writtenBy kast:CoganShimizu;
kast:presentedon kast:Twitter;
kast:hasTrustMetric kast:mbetm;
kast:kastTimestamp kast:examplets;
.
4 Conclusions and Future Work
The Microblog Entry Ontology Design Pattern is a useful model for a very com-
monplace structure, especially as the amount of social media data available for
inspection continues to increase. The potential applications of this pattern are
widespread, from determining public sentiment, measuring affect, or investigat-
ing community formation and evolution on social media networks.
The Microblog Entry pattern is foundational. On its own, it is not particularly
remarkable. However, in the ecosystem it plays a fundamental role. In similar
systems, it is analogous to entity extraction. Knowing the entities in play is
important, but ultimately provides only a small facet of a crisis situation. The
Microblog Entry pattern serves a similar role. It provides the threads to weave
a more comprehensive picture. At this time, the pattern heavily relies on many
external patterns, though many of them can be implemented as simple wrappers
for datatypes. Future work will be focused on developing the ecosystem of ODPs
for building a Common Operating Picture for a crisis situation. We will also
investigate how the different visualizations can be effected by the trust metric.
As the work progresses, we will be working closely with domain experts in the
United States Air Force.
Acknowledgement. The authors acknowledge support by the Dayton Area Grad-
uate Studies Institute (DAGSI) and input from Vincent Schmidt, Ph.D.
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