paper: Temporal Understanding of Cybersecurity Threats

May 28th, 2020
Click to view this narrated presentation from the conference

Temporal Understanding of Cybersecurity Threats

Jennifer Sleeman, Tim Finin, and Milton Halem, Temporal Understanding of Cybersecurity Threats, IEEE International Conference on Big Data Security on Cloud, May 2020.

As cybersecurity-related threats continue to increase, understanding how the field is changing over time can give insight into combating new threats and understanding historical events. We show how to apply dynamic topic models to a set of cybersecurity documents to understand how the concepts found in them are changing over time. We correlate two different data sets, the first relates to specific exploits and the second relates to cybersecurity research. We use Wikipedia concepts to provide a basis for performing concept phrase extraction and show how using concepts to provide context improves the quality of the topic model. We represent the results of the dynamic topic model as a knowledge graph that could be used for inference or information discovery.

Why does Google think Raymond Chandler starred in Double Indemnity?

November 14th, 2019

In my knowledge graph class yesterday we talked about the SPARQL query language and I illustrated it with DBpedia queries, including an example getting data about the movie Double Indemnity. I had brought a google assistant device and used it to compare its answers to those from DBpedia. When I asked the Google assistant “Who starred in the film Double Indemnity”, the first person it mentioned was Raymond Chandler. I knew this was wrong, since he was one of its screenwriters, not an actor, and shared an Academy Award for the screenplay. DBpedia’s data was correct and did not list Chandler as one of the actors.

I did not feel too bad about this — we shouldn’t expect perfect accuracy in these huge, general purpose knowledge graphs and at least Chandler played an important role in making the film.

After class I looked at the Wikidata page for Double Indemnity (Q478209) and saw that it did list Chandler as an actor. I take this as evidence that Google’s knowledge Graph got this incorrect fact from Wikidata, or perhaps from a precursor, Freebase.

The good news 🙂 is that Wikidata had flagged the fact that Chandler (Q180377) was a cast member in Double Indemnity with a “potential Issue“. Clicking on this revealed that the issue was that Chandler was not known to have an occupation property that a “cast member” property (P161) expects, which includes twelve types, such as actor, opera singer, comedian, and ballet dancer. Wikidata lists chandler’s occupations as screenwriter, novelist, write and poet.

More good news 😀 is that the Wikidata fact had provenance information in the form of a reference stating that it came from CSFD (Q3561957), a “Czech and Slovak web project providing a movie database”. Following the link Wikidata provided led me eventually to the resource, which allowed my to search for and find its Double Indemnity entry. Indeed, it lists Raymond Chandler as one of the movie’s Hrají. All that was left to do was to ask for a translation, which confirmed that Hrají means “starring”.

Case closed? Well, not quite. What remains is fixing the problem.

The final good news 🙂 is that it’s easy to edit or delete an incorrect fact in Wikidata. I plan to delete the incorrect fact in class next Monday. I’ll look into possible options to add an annotation in some way to ignore the incorrect ?SFD source for Chander being a cast member over the weekend.

Some possible bad news 🙁 that public knowledge graphs like Wikidata might be exploited by unscrupulous groups or individuals in the future to promote false or biased information. Wikipedia is reasonably resilient to this, but the problem may be harder to manage for public knowledge graphs, which get much their data from other sources that could be manipulated.