Since the 2001 envisioning of the Semantic Web (SW) [1] as an extension to the World Wide Web, the main research focus in SW reasoning has been on the soundness and completeness of reasoners. While these reasoners assume the veracity of the input data, the reality is that the Web of data is inherently noisy. Although there has been recent work on noise-tolerant reasoning, it has focused on type inference rather than full RDFS reasoning. Even though RDFS closure generation can be seen as a Knowledge Graph (KG) completion problem (link prediction in particular), the problem setting is different—making KG embedding techniques that were designed for link prediction not suitable for RDFS reasoning. This paper documents a novel approach that applies advances in deep learning to extend noise-tolerance in the SW to full RDFS reasoning; this is a stepping stone towards bridging the Neural-Symbolic gap for RDFS reasoning and beyond. Our embedding technique—that is tailored for RDFS reasoning—consists of layering RDF graphs and encoding them in the form of 3D adjacency matrices where each layer layout forms a graph word. Each input graph and its entailments are then represented as sequences of graph words, and RDFS inference can be formulated as translation of these graph words sequences, achieved through neural machine translation. Our evaluation confirms that deep learning can in fact be used to learn the RDFS inference rules from both synthetic and real-world SW data while demonstrating a noise-tolerance unavailable with rule-based reasoners; learning the inference on the LUBM synthetic dataset achieved 98.4% validation and 98% test accuracy while it achieved 87.76% validation accuracy on a subset of DBpedia.

Deep learning for noise-tolerant RDFS reasoning

The hybrid intelligent information system approach as the basis for cognitive architecture

There is no doubt that cognitive architecture model must be capable of describing very complex hierarchical systems. The basic lower-level “lingua franca” model is required for cognitive architecture description. We propose to use a metagraph model as a basic model. The key element of the metagraph model is metavertex which may include inner vertices and edges. From the general system theory point of view, a metavertex is a special case of the manifestation of the emergence principle, which means that a metavertex with its private attributes and connections become a whole that cannot be separated into its parts. The metagraph agents are used for metagraph transformation. The distinguishing feature of the metagraph agent is its homoiconicity, which means that it can be a data structure for itself. Thus, a metagraph agent can change the structure of other metagraph agents. The structures similar to neural networks are typical elements of cognitive architecture. It is shown that proposed metagraph approach is suitable enough to represent neural networks.

Metagraph Approach as a Data Model for Cognitive Architecture

Mutual relations of countries, primarily in the socio-economic and political spheres, have been and are important aspects of fully-fledged development for centuries. In modern realities, different approaches are used for assessing different risks and threats, and decision support systems are created that allow real-time data collection, analysis and reporting on the further development of the situation. This article is devoted to analysis of the main approaches used in the field of strategic risk assessment and threats in decision support systems to obtain a comprehensive assessment result. Particular attention is paid to the use of the Goldstein scale in the decision support system to obtain quantitative estimates of events for further prediction of the situation. As a result of the research, an assumption is made on combining the Goldstein scale with the hierarchy analysis method to obtain a comprehensive assessment of risks and threats.

Basic approaches to assessing risks and threats in decision support systems

The integration of meta-knowledge on the Web of data is essential to support trustworthiness. This is in fact an issue because of the enormous amount of data that exists on the Web of Data. Meta-kn...

State-of-the-Art Approaches for Meta-Knowledge Assertion in the Web of Data

This paper proposes an approach for addressing Resource Description Framework (RDF) knowledge representation limitations using an emergent graph model. The metagraph model is proposed to solve this problem. The formal definition of the metagraph model is given. Metagraph and hypergraph models are compared and it is shown that the hypergraph model does not implement the emergence principle. Textual predicate representation of the metagraph model is given covering all the main elements of the metagraph data model. RDF reification limitation and N-ary relationship limitation are examined. It is shown that the metagraph model addresses RDF limitations in a natural way without emergence loss. Proposed textual representation of the metagraph model allows clear and emergent description of the examined problems.

/pdf/using-the-metagraph-approach-for-addressing-rdf-knowledge-1ygpwl849k.pdf

Using the metagraph approach for addressing RDF knowledge representation limitations

This paper is aimed to overcome the limitation of the traditional multidimensional model in order to allow usage of numerical, textual and object-oriented information as multidimensional model measures The ontological approach is reviewed The formal definition of multidimensional approach is given The idea of multidimensional and ontological approaches hybridization is discussed The hybrid multidimensional-ontological data model requirements are proposed The formal definitions of the metagraph data model and metagraph agent model are given The examples of data metagraph and metagraph rule agent are discussed The representation of object-oriented data structures in form of metagraph is given The hybrid multidimensional-ontological data model based on metagraph approach is proposed Predicate representation of metagraph model considered as a physical data model for metagraph approach implementation is given

The Hybrid Multidimensional-Ontological Data Model Based on Metagraph Approach

The main ideas of the metagraph data model and the metagraph agent model are discussed. The metagraph approach for semantic complex event processing is presented. The metagraph agent implementation based on Functional Reactive Programming is proposed.

The Implementation of Metagraph Agents Based on Functional Reactive Programming

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Using the metagraph approach for addressing RDF knowledge representation limitations

The Hybrid Multidimensional-Ontological Data Model Based on Metagraph Approach

The Implementation of Metagraph Agents Based on Functional Reactive Programming