The Human Capital Ontology (HCO) is an ontology that represents data standards maintained and employed by the Office of Personnel Management (OPM) to represent Human Capital Operations and to classify job positions. The HCO is an extension of the Common Core Ontologies and the upper-level Basic Formal Ontology (BFO). HCO provides representation of OPM Natures of Action (NOA) that are used to identify human resource personnel actions, as well as their corresponding codes. HCO also represents Occupational Groups and Job Families, the Occupational Series into which these subdivide, as well as their corresponding codes, used by OPM to classify and grade both white- and blue-collar jobs in the Federal Government. HCO also encodes crosswalks between OPM Occupational Series and corresponding Standard Occupational Classification Codes maintained by the U.S. Bureau of Labor Statistics.
The paper sketches some initial results from an ongoing project to develop an ontology-based digital form for representing uncertain information. We frame this work as a journey from lower to higher levels of digital maturity across a technology divide. The paper first sets a baseline by describing the basic challenges any project dealing with digital uncertainty faces. It then describes how the project is facing them. It shows firstly how an extensional ontology (such as the BORO Foundational Ontology or the Information Exchange Standard) can be extended with a Lewisian counterpart approach to formalizing uncertainty that is adapted to computing. And then it shows how this is expressive enough to handle the challenges. Keywords: actuality, BORO Foundational Ontology, counterpart, Information Exchange Standard, informational uncertainty, my doxastic actualities, two-dimensional semantics.
The paper explores the nature of risk, providing a characterization using the categories of the Basic Formal Ontology (BFO). It argues that the category Risk is a subclass of BFO:Role, contrasting it with a similar view classifying Risk as a subclass of BFO:Disposition. This modeling choice is applied on one example of risk, which represents objects, processes (both physical and mental) and their interrelations, then generalizing from the instances in the example to obtain an overall analysis of risk, making explicit what are the sufficient conditions for being a risk. Plausible necessary conditions are also mentioned for future work. Index Terms: ontology, risk, BFO, role, disposition
This short paper is a primer on the nature of state sovereignty and the importance of claims about it. It also aims to reveal (merely reveal) a strategy for working with vague or contradictory data about which states, in fact, are sovereign. These goals together are intended to set the stage for applied work in ontology about international affairs.
We argue that ontology-structured knowledge graphs can play a crucial role in generating predictions about future events. By leveraging the semantic framework provided by Basic Formal Ontology (BFO) and Common Core Ontologies (CCO), we demonstrate how data such as the movements of a fishing vessel can be organized in and retrieved from a knowledge graph. These query results are then used to create Markov chain models, allowing us to predict future states based on the vessel's history. To fully support this process, we introduce the term `spatiotemporal instant' to complete the necessary structural semantics. Additionally, we critique the prevailing ontological model of probability, according to which probabilities are about the future. We propose an alternative view, where at least some probabilities are treated as being about actual process profiles, which better captures the dynamics of real-world phenomena. Finally, we demonstrate how our Markov chain-based probability calculations can be seamlessly integrated back into the knowledge graph, enabling further analysis and decision-making.
Decision making is a big topic in Intelligence, Defense, and Security fields. However, very little work can be found in the literature about ontology languages that simultaneously support decision making under uncertainty, abstractions/generalizations with first-order expressiveness, and forward/backward compatibility with OWL—a standard language for ontologies. This work proposes PR-OWL Decision, a language which extends PR-OWL—an extension of OWL to support uncertainty—to support first-order expressiveness, decision making under uncertainty, and backward/forward compatibility with OWL and PR-OWL. Keywords—ontology, decision making, uncertainty, OWL
Cybersecurity is a complex and dynamic area where multiple actors act against each other through computer networks largely without any commonly accepted rules of engagement. Well-managed cybersecurity operations need a clear terminology to describe threats, attacks and their origins. In addition, cybersecurity tools and technologies need semantic models to be able to automatically identify threats and to predict and detect attacks. This paper reviews terminology and models of cybersecurity operations, and proposes approaches for seman tic modelling of cybersecurity threats and attacks.
In this paper, we present work in progress on using the Information Domain ontologies of CCO (Common Core Ontologies) as a domain model for land combat. Our goal is to use the domain model as a common semantics for multiple land combat logical models. In the paper, we show how our domain model can be mapped to different logical models in a manner that is less labor intensive than the approach commonly used by users of CCO. We demonstrate our approach by describing how our domain model, which is a domain ontology of CCO, is mapped to logical models created in Ecore and NIEM (National Information Exchange Model).
Ontologies have been commonly associated with representing a domain using deterministic information. Probabilistic Ontologies extend this capability by incorporating formal probabilistic semantics. PR-OWL is a language that extends OWL with semantics based on Multi-Entity Bayesian Networks (MEBN), a Bayesian probabilistic logic. Developing probabilistic ontologies can be greatly facilitated by the use of a modeling framework such as the Uncertainty Modeling Process for Semantic Technology (UMP-ST). An example of using UMPST was the development of a probabilistic ontology to support PROGNOS (PRobabilistic OntoloGies for Net-Centric Operational Systems), a system that supports Maritime Domain Awareness (MDA). The PROGNOS probabilistic ontology provides semantically aware uncertainty management to support fusion of heterogeneous input and probabilistic assessment of situations to improve MDA. However, manually developing and maintaining a probabilistic ontology is a labor-intensive and insufficiently agile process. Greater automation through a combination of reference models and machine learning methods may enhance agility in probabilistic situation awareness (PSAW) systems. For this reason, a process for Human-aided MEBN Learning in PSAW (HMLP) was suggested. In previous work, we used UMP-ST to develop the PROGNOS probabilistic ontology. This paper presents an extended PROGNOS probabilistic ontology developed using HMLP. The contribution of this research is to introduce the extended PROGNOS probabilistic ontology and present a comparison between two processes (UMP-
Planning air warfare operations has always been a complex endeavor. However, as technology evolves at an increasingly fast pace, so does the complexity of managing its resources. In modern air operations, planners have to deal with a highly changing environment influenced by enemy air defenses, weather forecasts, among many other factors, demanding much effort to handle the great number of constraints and uncertainties presented by them. As a result, a number of decision-support systems have emerged attempting to facilitate the planning of air warfare operations. These systems usually rely on a wide variety of methodologies, which sometimes present a challenge in themselves when it comes to assessing the feasibility and effectiveness of the produced plans. Computer simulations are a practical way of providing this assessment, usually by running the resulting plans multiple times and checking the results against key criteria. Yet, establishing the right criteria, properly accounting for the “fog of war,” and avoiding impractical simulation run times and costs are still major challenges. This paper addresses such challenges by proposing the development of a decisionsupport framework that combines ontology-based agile knowledge and a simulation-based mission planning methodology that accounts for the inherent uncertainties that air operations face. We avoid costly computation times required by simulationintensive course-of-action analyzers by initially pruning the solution space through ontological reasoning. Moreover, the approach complies with the Effects-Based Approach to Operations, having a clear correspondence of processes with it. The explanations are focused on a specific scenario concerning intelligence, surveillance, and reconnaissance operations. Keywords—ontologies; effects-based planning; modeling and simulation; semantic matchmaking
Several vulnerability databases and standards are currently available for assessing the degree of security of IT infrastructures in general. These standards focus on different aspects of the systems, while generally failing to provide support for holistic analyses a key aspect in ensuring a secure IT infrastructure. This work aims to address this gap by presenting a new methodology for evaluating the overall security risks of a networked system that adopts an ontology-based approach we presented in previous work. We leverage current security standards and databases, while also considering the human factors to build a broader and interconnected view. Our methodology is meant to achieve a more realistic picture of the network security, hence improving situation awareness for its administrators. To illustrate our approach, this paper brings a case study applying the new methodology to a few target networks. The proof of concept is meant to underscore the methodology’s effectiveness in assessing the security of the whole network.
Clinical medical practice and biomedical research utilize genetic information for specific purposes. Irrespective of the purpose of obtaining genetic material, methodologies for protecting the privacy of patients/donors in both clinical and research settings have not kept pace with rapid advances in genetics research. When the usage of genetic information is not predicated on the latest laws and policies, the result places all-important patient/donor privacy at risk. Some methodologies err on the side of overly stringent policies that may inhibit research and open-ended diagnostic activity, whereas an opposite approach advocates a high-degree of openness that can jeopardize patient privacy, inappropriately identify disease susceptibility of patients and their genetic relatives, and thereby erode the doctor-patient privilege. As a solution, we present a framework based on the premise that acceptable clinical treatment regimens are captured in workflows used by caregivers and researchers and therefore their associated purpose are inherent to and therefore can be extracted from these workflows. We combine these purposes with applicable consents that are derived from applicable laws and practice standards to ascertain the releasability of genetic information. Given that federal, state and institutional laws, rules and regulations govern the use, retention and sharing of genetic information, we create a three-level rule hierarchy to apply the laws to a request and auto-generate consents prior to releasing. Our hierarchy also identifies all pre-conditions that must be met prior to the genetic information release, any restrictions and constraints to be enforced after release, and the penalties that may be assessed for violating these terms. We prototype our system using open source tools, while ensuring that the results can be added to existing Electronic Medical Records (EMR) systems.
Human behavioral factors are fundamental to understanding, detecting and mitigating insider threats, but to date insufficiently represented in a formal ontology. We report on the design and development of an ontology that emphasizes individual and organizational sociotechnical factors, and incorporates technical indicators from previous work. We compare our ontology with previous research and describe use cases to demonstrate how the ontology may be applied. Our work advances current efforts toward development of a comprehensive knowledge base to support advanced reasoning for insider threat mitigation. Keywords— insider threat; sociotechnical indicators ontology; domain knowledge representation; SME knowledge modeling; human behavioral modeling; domain knowledge sharing
When dealing with large volumes of data in organizations, there is always a need to associate data with its appropriate meaning, since the same data object may have different meaning to different users. This creates a problem of delivering search results that is different from a requester’s intended purpose. To solve this problem, we propose a parallelizable framework capable of capturing user specified constraints that are both semantically relevant to a search/domain in question as well as contextually relevant to a user and/or
Cyber security remains one of the most serious challenges to national security and the economy that we face today. Systems employing well known but static defenses are increasingly vulnerable to penetration from determined, diverse, and well resourced adversaries launching targeted attacks such as Advanced Persistent Threats (APTs). Due to the heavy focus on cyber security technologies in both commercial and government environments over the last decade, an overwhelming array of cyber defense technologies have become available for cyber defenders to use. As the number and complexity of these defenses increase, cyber defenders face the problem of selecting, composing, and configuring them, a process which to date is performed manually and without a clear understanding of integration points and risks associated with each defense or combination of defenses. As shown in Figure 1, the current state-of-the-art approach for selecting and configuring cyber defenses is manual in nature and is often done without a clear understanding of security metrics associated with attack surfaces. Due to the talent Unknown& Security& Metrics Manual&Selection&and& Configuration&of&Cyber&Defenses
When the U.S. conducts warfare, elements of a force are drawn from different Services and work together as a single team to accomplish an assigned mission on the basis of joint doctrine. To achieve such unified action, it is necessary that specific Service doctrines be both consistent with and subservient to joint doctrine. But there are two further requirements that flow from the ways in which unified action increasingly involves not only live forces but also automated systems. First, the information technology that is used in joint warfare must be aligned with joint doctrine. Second, the separate information systems used by the different elements of a joint force must be interoperable, in the sense that data and information that is generated by each element must be usable (understandable, processable) by all the other elements that need them. Currently, such interoperability is impeded by multiple inconsistencies among the different data and software standards used by warfighters. We describe here the on-going project of creating a Joint Doctrine Ontology (JDO), which uses joint doctrine to provide shared computer-accessible content valid for any field of military endeavor, organization, and information system. JDO addresses the two previouslymentioned requirements of unified action by providing a widely applicable benchmark for use by developers of information systems that will both guarantee alignment with joint doctrine and support interoperability. Keywords—joint doctrine, military doctrine, ontology, Basic Formal Ontology (BFO), Common Core Ontologies (CCO), joint warfare, unified operations, interoperability, terminology,
In this paper we outline a holistic approach for understanding and simulating human decision making in knowledge-intensive tasks. To this purpose, we integrate semantic and cognitive models in a hybrid computational architecture. The contribution of the paper is twofold: first we describe a packetcentric ontology to represent network traffic. We show how the ontology is used to describe real-world network traffic and also serve as a basis for higher level ontologies of cyber operation, threat and risk. Second, we demonstrate how the combination of the packet-centric ontology with an adaptive cognitive agent with learning capabilities, can be used to understand the human defender reasoning processes when monitoring network traffic. Through simulation experiments we evaluated the proposed hybrid computational architecture and demonstrate its ability to successfully detect malicious port scanning within legitimate network traffic. We discuss the implications of these findings for improving our understanding of the cognitive processes and knowledge requirements of the cyber defender, as well as the possible use of the hybrid architecture as a cognitively inspired decision support tool.
The Cyber Security is a crucial aspect of networks management. The Reachability Matrix computation is one of the main challenge in this field. This paper presents an intelligent solution in order to address the Reachability Matrix computational problem. In this paper we describe our contribute in the PANOPTESEC 1 project. PANOPTESEC aims to deliver beyond-state-of-the-art prototype of a cyber defence decision support system, demonstrating the benefit of a risk based approach to automated cyber defence. PANOPTESEC takes into account of the dynamic nature of information and communications technologies (ICT) and of the constantly evolving capabilities of cyber attackers in order to propose a solution based on knowledge representation and reasoning.
Ontologies formally represent reality in a way that limits ambiguity and facilitates automated reasoning and data fusion, but is often daunting to the non-technical user. Thus, many researchers have endeavored to hide the formal syntax and semantics of ontologies behind the constructs of Controlled Natural Languages (CNLs), which retain the formal properties of ontologies while simultaneously presenting that information in a comprehensible natural language format. In this paper, we build upon previous work in this field by evaluating prospects of implementing International Technology Alliance Controlled English (ITACE) as a middleware for ontology editing. We also discuss at length a prototype of a natural language conversational interface application designed to facilitate ontology editing via the formulation of CNL constructs. Keywords—Ontology; Controlled English; Intelligence Collection
