Hisham Assal

Semantically-enhanced information extraction

Information Extraction using Natural Language Processing (NLP) produces entities along with some of the relationships that may exist among them. To be semantically useful, however, such discrete extractions must be put into context through some form of intelligent analysis. This paper1,2 offers a two-part architecture that employs the statistical methods of traditional NLP to extract discrete information elements in a relatively domain-agnostic manner, which are then injected into an inference-enabled environment where they can be semantically analyzed. Within this semantic environment, extractions are woven into the contextual fabric of a user-provided, domain-centric ontology where users together with user-provided logic can analyze these extractions within a more contextually complete picture. Our demonstration system infers the possibility of a terrorist plot by extracting key events and relationships from a collection of news articles and intelligence reports.

Intelligent software for ecological building design

Building design is a complex process because of the number of elements and issues involved and the number of relationships that exist among them. Adding sustainability issues to the list increases the complexity of design by an order of magnitude. There is a need for computer assistance to manage the increased complexity of design and to provide intelligent collaboration in formulating acceptable design solutions. Software development technology today offers opportunities to design and build an intelligent software system environment that can serve as a reliable intelligent partner to the human designer. In this paper the authors discuss the requirements for an intelligent software design environment, explain the major challenges in designing this environment, propose an architecture for an intelligent design support system for sustainable design and present the existing technologies that can be used to implement that architecture.

An Approach to Airborne Conflict Prediction

Existing conflict alerting systems provide less than acceptable support to air traffic controllers due to lack of integration of all the necessary information such as weather, terrain and airspace information when predicting conflicts. The final integration and interpretation of this information lies with the air traffic controller. The rate at which air traffic has grown in the past few years has not only created congestion but also an increase in controller workload. Future concepts such as free flight will enable pilots to choose autonomous flight paths. Such arbitrary selection of flight paths can lead to potential conflicts. It is therefore essential that an autonomous monitoring system is vital to not only support future concepts such as free flight but also provide relief to controllers and reduce congestion. This paper presents a ground-based autonomous airspace monitoring tool that integrates information from airborne sensors, radar, weather, terrain, airspace, flight rules and pilot intent.

An Approach to Intelligent Threat Level Assessment of Aircraft Deviation in the National Airspace System

The rapid increase in air traffic over the last several years has led to congestion, delays and increase in controller workloads. To handle the large volume of traffic in high density airspaces, an autonomous traffic monitoring system that can monitor aircraft, perform conformance checking, assess and predict threats is essential. This paper presents an ontology-based approach to develop a decision support system to monitor and assess the security threat levels of the National Airspace System.