Geographic Knowledge Graph Summarization

Geographic knowledge graphs play a significant role in the geospatial semantics paradigm for fulfilling the interoperability, the accessibility, and the conceptualization demands in geographic information science. However, due to the immense quantity of information accompanying and the enormous diversity of geographic knowledge graphs, there are many challenges that hinder the applicability and mass adoption of such useful structured knowledge. In order to tackle these challenges, this dissertation focuses on devising ways in which geographic knowledge graphs can be digested and summarized. Such a summarization task, on the one hand lifts the burden of information overload for end users, on the other hand facilitates the reduction of data storage, speeds up queries, and helps eliminate noise. The main contribution of this dissertation is that it introduces the general concept of geospatial inductive bias and explains different ways this idea can be used in the geographic knowledge graph summarization task. By decomposing the task into separate but related components, this dissertation is based upon three peer-reviewed articles which focus on the hierarchical place type structure, multimedia leaf nodes, and general relation and entity components respectively. A spatial knowledge map interface that illustrates the effectiveness of summarizing geographic knowledge graphs is presented. Throughout the dissertation, top-down knowledge engineering and bottom-up knowledge learning methods are integrated. We hope this dissertation would promote the awareness of this fascinating area and motivate researchers to investigate related questions

Supporting the Discovery, Reuse, and Validation of Cybersecurity Requirements at the Early Stages of the Software Development Lifecycle

The focus of this research is to develop an approach that enhances the elicitation and specification of reusable cybersecurity requirements. Cybersecurity has become a global concern as cyber-attacks are projected to cost damages totaling more than $10.5 trillion dollars by 2025. Cybersecurity requirements are more challenging to elicit than other requirements because they are nonfunctional requirements that requires cybersecurity expertise and knowledge of the proposed system. The goal of this research is to generate cybersecurity requirements based on knowledge acquired from requirements elicitation and analysis activities, to provide cybersecurity specifications without requiring the specialized knowledge of a cybersecurity expert, and to generate reusable cybersecurity requirements. The proposed approach can be an effective way to implement cybersecurity requirements at the earliest stages of the system development life cycle because the approach facilitates the identification of cybersecurity requirements throughout the requirements gathering stage. This is accomplished through the development of the Secure Development Ontology that maps cybersecurity features and the functional features descriptions in order to train a classification machine-learning model to return the suggested security requirements. The SD-SRE requirements engineering portal was created to support the application of this research by providing a platform to submit use case scenarios and requirements and suggest security requirements for the given system. The efficacy of this approach was tested with students in a graduate requirements engineering course. The students were presented with a system description and tasked with creating use case scenarios using the SD-SRE portal. The entered models were automatically analyzed by the SD-SRE system to suggest the security requirements. The results showed that the approach can be an effective approach to assist in the identification of security requirements

KEER2022

Avanttítol: KEER2022. DiversitiesDescripció del recurs: 25 juliol 202

Tackling the veracity and variety of big data

This thesis tackles the veracity and variety challenges of big data, especially focusing on graphs and relational data. We start with proposing a class of graph association rules (GARs) to specify regularities between entities in graphs, which capture both missing links and inconsistencies. A GAR is a combination of a graph pattern and a dependency; it may take as predicates machine learning classifiers for link prediction. We formalize association deduction with GARs in terms of the chase, and prove its Church-Rosser property. We show that the satisfiability, implication and association deduction problems for GARs are coNP-complete, NP-complete and NP-complete, respectively. The incremental deduction problem is DP-complete for GARs. In addition, we provide parallel algorithms for association deduction and incremental deduction. We next develop a parallel algorithm to discover GARs, which applies an applicationdriven strategy to cut back rules and data that are irrelevant to users’ interest, by training a machine learning model to identify data pertaining to a given application. Moreover, we introduce a sampling method to reduce a big graph G to a set H of small sample graphs. Given expected support and recall bounds, this method is able to deduce samples in H and mine rules from H to satisfy the bounds in the entire G. Then we propose a class of temporal association rules (TACOs) for event prediction in temporal graphs. TACOs are defined on temporal graphs in terms of change patterns and (temporal) conditions, and may carry machine learning predicates for temporal event prediction. We settle the complexity of reasoning about TACOs, including their satisfiability, implication and prediction problems. We develop a system that discovers TACOs by iteratively training a rule creator based on generative models in a creatorcritic framework, and predicts events by applying the discovered TACOs in parallel. Finally, we propose an approach to querying relations D and graphs G taken together in SQL. The key idea is that if a tuple t in D and a vertex v in G are determined to refer to the same real-world entity, then we join t and v, correlate their information and complement tuple t with additional attributes of v from graphs. We show how to do this in SQL extended with only syntactic sugar, for both static joins when t is a tuple in D and dynamic joins when t comes from intermediate results of sub-queries on D. To support the semantic joins, we propose an attribute extraction scheme based on Kmeans clustering, to identify and fetch graph properties that are linked to v via paths. Moreover, we develop a scheme to extract a relation schema for entities in graphs, and a heuristic join method based on the schema to strike a balance between the complexity and accuracy of dynamic joins

Automatic machine learning:methods, systems, challenges

This open access book presents the first comprehensive overview of general methods in Automatic Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first international challenge of AutoML systems. The book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. Many of the recent machine learning successes crucially rely on human experts, who select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters; however the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself