In scientific disciplines where research findings have a strong impact on society, reducing the amount of time it takes to understand, synthesize and exploit the research is invaluable. Topic modeling is an effective ...

Physicians are often required to make critical medical decisions that may be based on previous events in the patient's health history. However, these events may be very difficult to locate in the patient record due to the ...

In this report, we describe the Unified Cyber Security ontology (UCO) to support situational awareness in cyber security systems. The ontology is an effort to incorporate and integrate heterogeneous information available ...

Capturing, maintaining, and using context information helps mobile applications provide better services and generates data useful in specifying information sharing policies. Obtaining the full benefit of context information ...

The Web is typically our first source of information about new software vulnerabilities, exploits and cyber-attacks. Information is found in semi-structured vulnerability databases as well as in text from security bulletins, ...

Pointwise mutual information (PMI) is a widely used word similarity measure, but it lacks a clear explanation of how it works. We explore how PMI differs from distributional similarity, and we introduce a novel metric, ...

Ontology alignment describes a process of mapping ontological concepts, classes and attributes between different ontologies providing a way to achieve interoperability. While there has been considerable research in this ...

Current state of the art intrusion detection and prevention systems (IDPS) are signature-based systems that detect threats and vulnerabilities by cross-referencing the threat or vulnerability signatures in their databases. ...

We propose a probabilistic framework to address uncertainty in ontology-based semantic integration and interopera- tion. This framework consists of three main components: 1) BayesOWL that translates an OWL ontology to a ...

This paper presents our ongoing effort on developing a principled methodology for automatic ontology mapping based on BayesOWL, a probabilistic framework we developed for modeling uncertainty in semantic web. The pro-posed ...