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. However, due to the highly heterogeneous nature and the immense volume of the data, a composite view is di cult to generate. Such a composite 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, o ers 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 t within an ecosystem for fusing social media, support advanced visualization, and provide a preliminary framework for trust assessment.
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 entries 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 entries 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 de nition and intent, microtext4 is simple; its model is relatively straightforward and requires little of the complexity that OWL brings to the table. Regardless, 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 t with many existing patterns. Speci cally, we foresee easy integration with the Modi edHazardousSituation Design Pattern [4] and ReportingEvent [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 su ciently 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 generated by D36. Smaller circles are related to the superimposed circle via subsumption and proximity in the same level of circle denotes a short semantic distance. (b) A standard view of geographic information: pins on a map background. This visualization can be updated in real-time and allows the user to see incoming data.
Microtext is a valuable resource in the Semantic Web Community, as evidenced by [2, 9, 10, 8]. However, to our knowledge this is the rst 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
This pattern was directly informed by the competency questions in the preceding 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 addition, 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 MicroblogEntry pattern. For example, in our implementation7 the light blue boxes are currently wrappers for datatypes. However, it is not hard to imagine increasingly 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 entry 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 le can be found at https://raw.githubusercontent.com/ cogan-shimizu-wsu/MicroblogEntryOWL/master/MicroblogEntry.owl MicroblogEntry v =1hasPayload.Payload MicroblogEntry v =1hasAuthor.Author MicroblogEntry v 1hasLocation.Location (1) (2) (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 established 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 straightforward to integrate the Modi edHazardousSituation [4] pattern to the MicroblogEntry. 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 modi cation).
Media The Media class allows us to represent the platform on which the MicroblogEntry 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 di erent Media will be very important in our use case scenario, especially when considering the TrustMetric for provenance and author. To this point, it seems reasonable to expect the trustworthiness of the platform and corporation to e ect 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 di erent platforms and even high variance of content on the same platform. That is, Twitter allows for many di erent payloads: text, hyperlinks, images, and videos. Facebook, on the other hand, o ers a superset of content types and no length restriction on text payloads.
In addition, we see the Payload playing a large role in de ning 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 \mention" 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 MicroblogEntries 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 categories: a ected individuals, infrastructures and utilities, donations and volunteer, caution and advice, sympathy and emotional support, useful information and unknown.
Our implementation currently wraps an xsd:string. This allows us to dynamically generate a Topic as Tweets are encountered. As the intended ODP ecosystem matures, it is conceivable that this Topic sub-pattern will be more fully eshed 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
[
TrustMetric The TrustMetric sub-pattern has the potential to be the most complex due to its far reaching e ects 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 di cult 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
kast:Evacuation ## Extracted from Box 2 t:Topic; "Car Accident"^^xsd:string; ## Extracted from Box 2 kast:examplepayload rdf:type kast:hasvalue kast:referencesTopic ## As trust in author is distinct from trust in the MBE. tm:TrustMetric; .89^^xsd:double; kast:CoganShimizu ## Extracted from Box 1 a prov:Person, prov:Agent; foaf:givenName "Cogan Shimizu"^^xsd:string; kast:hasTrustMetric kast:cogantm; pz:Media, prov:Entity;
The Microblog Entry Ontology Design Pattern is a useful model for a very commonplace 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 a ect, or investigating 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 di erent visualizations can be e ected by the trust metric. As the work progresses, we will be working closely with domain experts in the United States Air Force.
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