Automated Expert System Knowledge Base Development Method for Information Security Risk Analysis

Information security risk analysis is a compulsory requirement both from the side of regulating documents and information security management decision making process. Some researchers propose using expert systems (ES) for process automation, but this approach requires the creation of a high-quality knowledge base. A knowledge base can be formed both from expert knowledge or information collected from other sources of information. The problem of such approach is that experts or good quality knowledge sources are expensive. In this paper we propose the problem solution by providing an automated ES knowledge base development method. The method proposed is novel since unlike other methods it does not integrate ontology directly but utilizes automated transformation of existing information security ontology elements into ES rules: The Web Ontology Rule Language (OWL RL) subset of ontology is segregated into Resource Description Framework (RDF) triplets, that are transformed into Rule Interchange Format (RIF); RIF rules are converted into Java Expert System Shell (JESS) knowledge base rules. The experiments performed have shown the principal method applicability. The created knowledge base was later verified by performing comparative risk analysis in a sample company

Time-Aware Probabilistic Knowledge Graphs

The emergence of open information extraction as a tool for constructing and expanding knowledge graphs has aided the growth of temporal data, for instance, YAGO, NELL and Wikidata. While YAGO and Wikidata maintain the valid time of facts, NELL records the time point at which a fact is retrieved from some Web corpora. Collectively, these knowledge graphs (KG) store facts extracted from Wikipedia and other sources. Due to the imprecise nature of the extraction tools that are used to build and expand KG, such as NELL, the facts in the KG are weighted (a confidence value representing the correctness of a fact). Additionally, NELL can be considered as a transaction time KG because every fact is associated with extraction date. On the other hand, YAGO and Wikidata use the valid time model because they maintain facts together with their validity time (temporal scope). In this paper, we propose a bitemporal model (that combines transaction and valid time models) for maintaining and querying bitemporal probabilistic knowledge graphs. We study coalescing and scalability of marginal and MAP inference. Moreover, we show that complexity of reasoning tasks in atemporal probabilistic KG carry over to the bitemporal setting. Finally, we report our evaluation results of the proposed model

Inference as a data management problem

Inference over OWL ontologies with large A-Boxes has been researched as a data management problem in recent years. This work adopts the strategy of applying a tableaux-based reasoner for complete T-Box classification, and using a rule-based mechanism for scalable A-Box reasoning. Specifically, we establish for the classified T-Box an inference framework, which can be used to compute and materialise inference results. The inference we focus on is type inference in A-Box reasoning, which we define as the process of deriving for each A-Box instance its memberships of OWL classes and properties. As our approach materialises the inference results, it in general provides faster query processing than non-materialising techniques, at the expense of larger space requirement and slower update speed. When the A-Box size is suitable for an RDBMS, we compile the inference framework to triggers, which incrementally update the inference materialisation from both data inserts and data deletes, without needing to re-compute the whole inference. More importantly, triggers make inference available as atomic consequences of inserts or deletes, which preserves the ACID properties of transactions, and such inference is known as transactional reasoning. When the A-Box size is beyond the capability of an RDBMS, we then compile the inference framework to Spark programmes, which provide scalable inference materialisation in a Big Data system, and our evaluation considers up to reasoning 270 million A-Box facts. Evaluating our work, and comparing with two state-of-the-art reasoners, we empirically verify that our approach is able to perform scalable inference materialisation, and to provide faster query processing with comparable completeness of reasoning.Open Acces

Virtual Knowledge Graphs: An Overview of Systems and Use Cases

In this paper, we present the virtual knowledge graph (VKG) paradigm for data integration and access, also known in the literature as Ontology-based Data Access. Instead of structuring the integration layer as a collection of relational tables, the VKG paradigm replaces the rigid structure of tables with the flexibility of graphs that are kept virtual and embed domain knowledge. We explain the main notions of this paradigm, its tooling ecosystem and significant use cases in a wide range of applications. Finally, we discuss future research directions

Web Ontology Language (OWL)

Web Ontology Language (OWL) is a core world wide web consortium [W3C] standard Knowledge representation language for the Semantic Web

OWL-Miner: Concept Induction in OWL Knowledge Bases

The Resource Description Framework (RDF) and Web Ontology Language (OWL) have been widely used in recent years, and automated methods for the analysis of data and knowledge directly within these formalisms are of current interest. Concept induction is a technique for discovering descriptions of data, such as inducing OWL class expressions to describe RDF data. These class expressions capture patterns in the data which can be used to characterise interesting clusters or to act as classifica- tion rules over unseen data. The semantics of OWL is underpinned by Description Logics (DLs), a family of expressive and decidable fragments of first-order logic. Recently, methods of concept induction which are well studied in the field of Inductive Logic Programming have been applied to the related formalism of DLs. These methods have been developed for a number of purposes including unsuper- vised clustering and supervised classification. Refinement-based search is a concept induction technique which structures the search space of DL concept/OWL class expressions and progressively generalises or specialises candidate concepts to cover example data as guided by quality criteria such as accuracy. However, the current state-of-the-art in this area is limited in that such methods: were not primarily de- signed to scale over large RDF/OWL knowledge bases; do not support class lan- guages as expressive as OWL2-DL; or, are limited to one purpose, such as learning OWL classes for integration into ontologies. Our work addresses these limitations by increasing the efficiency of these learning methods whilst permitting a concept language up to the expressivity of OWL2-DL classes. We describe methods which support both classification (predictive induction) and subgroup discovery (descrip- tive induction), which, in this context, are fundamentally related. We have implemented our methods as the system called OWL-Miner and show by evaluation that our methods outperform state-of-the-art systems for DL learning in both the quality of solutions found and the speed in which they are computed. Furthermore, we achieve the best ever ten-fold cross validation accuracy results on the long-standing benchmark problem of carcinogenesis. Finally, we present a case study on ongoing work in the application of OWL-Miner to a real-world problem directed at improving the efficiency of biological macromolecular crystallisation

RDF graph validation using rule-based reasoning

The correct functioning of Semantic Web applications requires that given RDF graphs adhere to an expected shape. This shape depends on the RDF graph and the application's supported entailments of that graph. During validation, RDF graphs are assessed against sets of constraints, and found violations help refining the RDF graphs. However, existing validation approaches cannot always explain the root causes of violations (inhibiting refinement), and cannot fully match the entailments supported during validation with those supported by the application. These approaches cannot accurately validate RDF graphs, or combine multiple systems, deteriorating the validator's performance. In this paper, we present an alternative validation approach using rule-based reasoning, capable of fully customizing the used inferencing steps. We compare to existing approaches, and present a formal ground and practical implementation "Validatrr", based on N3Logic and the EYE reasoner. Our approach - supporting an equivalent number of constraint types compared to the state of the art - better explains the root cause of the violations due to the reasoner's generated logical proof, and returns an accurate number of violations due to the customizable inferencing rule set. Performance evaluation shows that Validatrr is performant for smaller datasets, and scales linearly w.r.t. the RDF graph size. The detailed root cause explanations can guide future validation report description specifications, and the fine-grained level of configuration can be employed to support different constraint languages. This foundation allows further research into handling recursion, validating RDF graphs based on their generation description, and providing automatic refinement suggestions