Discovering Relations by Entity Search in Lightweight Semantic Text Graphs

Entity search is becoming a popular alternative for full text search. Recently Google released its entity search based on confirmed, human-generated data such as Wikipedia. In spite of these developments, the task of entity discovery, search, or relation search in unstructured text remains a major challenge in the fields of information retrieval and information extraction. This paper tries to address that challenge, focusing specifically on entity relation discovery. This is achieved by processing unstructured text using simple information extraction methods, building lightweight semantic graphs and reusing them for entity relation discovery by applying algorithms from graph theory. An important part is also user interaction with semantic graphs, which can significantly improve information extraction results and entity relation search. Entity relations can be discovered by various text mining methods, but the advantage of the presented method lies in the similarity between the lightweight semantics extracted from a text and the information networks available as structured data. Both graph structures have similar properties and similar relation discovery algorithms can be applied. In addition, we can benefit from the integration of such graph data. We provide both a relevance and performance evaluations of the approach and showcase it in several use case applications

Email Analysis and Information Extraction for Enterprise Benefit

In spite of rapid advances in multimedia and interactive technologies, enterprise users prefer to battle with email spam and overload rather than lose the benefits of communicating, collaborating and solving business tasks over email. Many aspects of email have significantly improved over time, but its overall integration with the enterprise environment remained practically the same. In this paper we describe and evaluate a light-weight approach to enterprise email communication analysis and information extraction. We provide several use cases exploiting the extracted information, such as the enrichment of emails with relevant contextual information, social network extraction and its subsequent search, creation of semantic objects as well as the relationship between email analysis and information extraction on one hand, and email protocols and email servers on the other. The proposed approach was partially tested on several small and medium enterprises (SMEs) and seems to be promising for enterprise interoperability and collaboration in SMEs that depend on emails to accomplish their daily business tasks

A Novel Way of Using Simulations to Support Urban Security Operations

The growing importance of security operations in urban terrain has triggered many attempts to address the perceived gaps in the readiness of security forces for this type of combat. One way to tackle the problem is to employ simulation techniques. Simulations are widely used to support both mission rehearsal and mission analysis, but these two applications tend to be seen as distinctly separate. We argue that integrating them in a unified framework can bring significant benefits for end-users. We perform a structured walk-through of such a unified system, in which a novel approach to integration through the behaviour cloning enabled the system to capture the operational knowledge of security experts, which is often difficult to express verbally. This capability emerged as essential for the operation of the integrated system. We also illustrate how the interplay between the system components for the mission analysis and mission rehearsal is realized

Agent-based Simulation Platform Evaluation in the Context of Human Behavior Modeling

Abstract. In this paper we provide a brief survey of agent based simulation (ABS) platforms and evaluate two of them – NetLogo and MASON – by implementing an exemplary scenario in the context of human behavior modeling. We define twelve evaluation points, which we discuss for both of the evaluated systems. The purpose of our evaluation is to identify the best ABS platform for parametric studies (data farming) of human behavior, but we intend to use the system also for training purposes. That is why we also discuss one of serious game platform representatives – VBS2