Entity spatio-temporal evolution summarization in knowledge graphs

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordKnowledge graph has been growing in popularity with extensive applications in recent years, such as entity alignment, entity summarization, question answering, etc. However, the majority of research only focuses on one snapshot of the knowledge graph and neglects its dynamicity in nature, which often causes missing important information contained in other versions of the knowledge graph. Even worse, the incompleteness of the data in the knowledge graph is a challenge issue, which hinders the further utilization of the data. Considering that knowledge graph can evolve with time as well as the changing locations, it is necessary to summarize and integrate the entity temporal and spatial evolution information. To address this challenge, this paper pioneers to formulate the problem of entity spatio-temporal evolution summarization, capturing the entity evolution with time and location changes and integrating the data from two groups of various knowledge graphs. Further, we propose a two-stage approach: 1) generate entity temporal summarization and spatial summarization by utilizing the Triadic Formal Concept Analysis; 2) produce the spatio-temporal evolution summarization of the entity by adopting a fusion strategy. The obtained summarization results can be used to the visualization of the entity spatio-temporal evolution, data integration, and question answering.National Natural Science Foundation of ChinaEuropean Union Horizon 2020Natural Science Basic Research Plan in Shaanxi Province of ChinaFund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shaanxi Provinc

Probability as a physical motive

Recent theoretical progress in nonequilibrium thermodynamics, linking the physical principle of Maximum Entropy Production ("MEP") to the information-theoretical "MaxEnt" principle of scientific inference, together with conjectures from theoretical physics that there may be no fundamental causal laws but only probabilities for physical processes, and from evolutionary theory that biological systems expand "the adjacent possible" as rapidly as possible, all lend credence to the proposition that probability should be recognized as a fundamental physical motive. It is further proposed that spatial order and temporal order are two aspects of the same thing, and that this is the essence of the second law of thermodynamics.Comment: Replaced at the request of the publisher. Minor corrections to references and to Equation 1 added

Geospatial Narratives and their Spatio-Temporal Dynamics: Commonsense Reasoning for High-level Analyses in Geographic Information Systems

The modelling, analysis, and visualisation of dynamic geospatial phenomena has been identified as a key developmental challenge for next-generation Geographic Information Systems (GIS). In this context, the envisaged paradigmatic extensions to contemporary foundational GIS technology raises fundamental questions concerning the ontological, formal representational, and (analytical) computational methods that would underlie their spatial information theoretic underpinnings. We present the conceptual overview and architecture for the development of high-level semantic and qualitative analytical capabilities for dynamic geospatial domains. Building on formal methods in the areas of commonsense reasoning, qualitative reasoning, spatial and temporal representation and reasoning, reasoning about actions and change, and computational models of narrative, we identify concrete theoretical and practical challenges that accrue in the context of formal reasoning about `space, events, actions, and change'. With this as a basis, and within the backdrop of an illustrated scenario involving the spatio-temporal dynamics of urban narratives, we address specific problems and solutions techniques chiefly involving `qualitative abstraction', `data integration and spatial consistency', and `practical geospatial abduction'. From a broad topical viewpoint, we propose that next-generation dynamic GIS technology demands a transdisciplinary scientific perspective that brings together Geography, Artificial Intelligence, and Cognitive Science. Keywords: artificial intelligence; cognitive systems; human-computer interaction; geographic information systems; spatio-temporal dynamics; computational models of narrative; geospatial analysis; geospatial modelling; ontology; qualitative spatial modelling and reasoning; spatial assistance systemsComment: ISPRS International Journal of Geo-Information (ISSN 2220-9964); Special Issue on: Geospatial Monitoring and Modelling of Environmental Change}. IJGI. Editor: Duccio Rocchini. (pre-print of article in press

Identification of binary cellular automata from spatiotemporal binary patterns using a fourier representation

The identification of binary cellular automata from spatio-temporal binary patterns is investigated in this paper. Instead of using the usual Boolean or multilinear polynomial representation, the Fourier transform representation of Boolean functions is employed in terms of a Fourier basis. In this way, the orthogonal forward regression least-squares algorithm can be applied directly to detect the significant terms and to estimate the associated parameters. Compared with conventional methods, the new approach is much more robust to noise. Examples are provided to illustrate the effectiveness of the proposed approach

Metaphysics of science between metaphysics and science

The paper argues that metaphysics depends upon science when it comes to claims about the constitution of the real world. That thesis is illustrated by considering the examples of global supervenience, the tenseless vs. the tensed theory of time and existence, events vs. substances, and relations vs. intrinsic properties. An argument is sketched out for a metaphysics of a four-dimensional block universe whose content are events and their sequences, events consisting in physical properties instantiated at space-time points, these properties being relations rather than intrinsic properties

Identification of the neighborhood and CA rules from spatio-temporal CA patterns

Extracting the rules from spatio-temporal patterns generated by the evolution of cellular automata (CA) usually produces a CA rule table without providing a clear understanding of the structure of the neighborhood or the CA rule. In this paper, a new identification method based on using a modified orthogonal least squares or CA-OLS algorithm to detect the neighborhood structure and the underlying polynomial form of the CA rules is proposed. The Quine-McCluskey method is then applied to extract minimum Boolean expressions from the polynomials. Spatio-temporal patterns produced by the evolution of 1D, 2D, and higher dimensional binary CAs are used to illustrate the new algorithm, and simulation results show that the CA-OLS algorithm can quickly select both the correct neighborhood structure and the corresponding rule