Integration Provenance And Temporal Queries For Large Scale Knowledge Bases
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| Author | : Shi Gao |
| Publisher | : |
| Total Pages | : 111 |
| Release | : 2016 |
| Genre | : |
| ISBN | : |
Knowledge bases that summarize web information in RDF triples deliver many benefits, including support for natural language question answering and powerful structured queries that extract encyclopedic knowledge via SPARQL. Large scale knowledge bases grow rapidly in terms of scale and significance, and undergo frequent changes in both schema and content. Two critical problems have thus emerged: (i) how to support temporal queries that explore the history of knowledge bases or flash-back to the past; (ii) how to integrate knowledge from difference sources and improve the quality of integrated knowledge base while preserving the provenance information. In this dissertation, we propose a framework that supports knowledge integration, temporal query evaluation and user-friendly interfaces for large-scale knowledge bases. Towards this goal, we make the following contributions: (i) We propose SPARQLT, a temporal extension of structured query language SPARQL based on a point temporal model which simplifies the expression of temporal joins and eliminates the need for temporal coalescing. This approach makes possible an end-user interface HKB (Historical Knowledge Browser) where users can browse the evolution history of knowledge bases and express historical queries via simple by-example conditions in the infoboxes of Wikipedia pages. (ii) We have designed and implemented RDF-TX (RDF Temporal eXpress), an efficient system for managing temporal RDF data and evaluating SPARQLT queries. RDF-TX takes advantage of compressed Multiversion B+ trees to achieve fast evaluation of temporal queries. The experimental result demonstrates that our indexing and query optimization techniques deliver superior performance over other systems. (iii) We propose a framework for knowledge extraction and integration. We first introduce IBMiner, a novel NLP-based system that derives knowledge bases from free text and preserves the provenance of extracted triples. IBminer uses a deep NLP-based approach to extract subject-attribute-value triples from free text, and maps the attributes to those introduced in existing knowledge bases. Then we integrate public knowledge bases with the knowledge base generated by IBMiner into one of superior quality and coverage, called IKBStore. User-friendly interfaces are provided to manage the knowledge in IKBStore while maintaining provenance information.
| Author | : Ashok N. Srivastava |
| Publisher | : CRC Press |
| Total Pages | : 314 |
| Release | : 2017-08-01 |
| Genre | : Computers |
| ISBN | : 1315354462 |
From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest...I hope that this book will inspire more computer scientists to focus on environmental applications, and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences." --Vipin Kumar, University of Minnesota Large-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science, computer science, statistics, and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book, and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events, as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning, as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution, as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources, in the final chapter of the book.
| Author | : Aidan Hogan |
| Publisher | : Morgan & Claypool Publishers |
| Total Pages | : 257 |
| Release | : 2021-11-08 |
| Genre | : Computers |
| ISBN | : 1636392369 |
This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale. The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced with notions of schema, identity, and context. The book discusses how ontologies and rules can be used to encode knowledge as well as how inductive techniques—based on statistics, graph analytics, machine learning, etc.—can be used to encode and extract knowledge. It covers techniques for the creation, enrichment, assessment, and refinement of knowledge graphs and surveys recent open and enterprise knowledge graphs and the industries or applications within which they have been most widely adopted. The book closes by discussing the current limitations and future directions along which knowledge graphs are likely to evolve. This book is aimed at students, researchers, and practitioners who wish to learn more about knowledge graphs and how they facilitate extracting value from diverse data at large scale. To make the book accessible for newcomers, running examples and graphical notation are used throughout. Formal definitions and extensive references are also provided for those who opt to delve more deeply into specific topics.
| Author | : Russ B Altman |
| Publisher | : World Scientific |
| Total Pages | : 472 |
| Release | : 2018-11-28 |
| Genre | : Computers |
| ISBN | : 9813279834 |
The Pacific Symposium on Biocomputing (PSB) 2019 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2019 will be held on January 3 - 7, 2019 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2019 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field.
| Author | : Hannah Bast |
| Publisher | : |
| Total Pages | : 170 |
| Release | : 2016-06-07 |
| Genre | : Computers |
| ISBN | : 9781680831641 |
Provides a comprehensive overview of the broad area of semantic search on text and knowledge bases. It is as self-contained as possible, and serves as a good tutorial for newcomers to this fascinating and highly topical field.
| Author | : Merrick Lex Berman |
| Publisher | : Indiana University Press |
| Total Pages | : 279 |
| Release | : 2016-08-08 |
| Genre | : Social Science |
| ISBN | : 0253022568 |
Well before the innovation of maps, gazetteers served as the main geographic referencing system for hundreds of years. Consisting of a specialized index of place names, gazetteers traditionally linked descriptive elements with topographic features and coordinates. Placing Names is inspired by that tradition of discursive place-making and by contemporary approaches to digital data management that have revived the gazetteer and guided its development in recent decades. Adopted by researchers in the Digital Humanities and Spatial Sciences, gazetteers provide a way to model the kind of complex cultural, vernacular, and perspectival ideas of place that can be located in texts and expanded into an interconnected framework of naming history. This volume brings together leading and emergent scholars to examine the history of the gazetteer, its important role in geographic information science, and its use to further the reach and impact of spatial reasoning into the digital age.
| Author | : I. Tiddi |
| Publisher | : IOS Press |
| Total Pages | : 314 |
| Release | : 2020-05-06 |
| Genre | : Computers |
| ISBN | : 1643680811 |
The latest advances in Artificial Intelligence and (deep) Machine Learning in particular revealed a major drawback of modern intelligent systems, namely the inability to explain their decisions in a way that humans can easily understand. While eXplainable AI rapidly became an active area of research in response to this need for improved understandability and trustworthiness, the field of Knowledge Representation and Reasoning (KRR) has on the other hand a long-standing tradition in managing information in a symbolic, human-understandable form. This book provides the first comprehensive collection of research contributions on the role of knowledge graphs for eXplainable AI (KG4XAI), and the papers included here present academic and industrial research focused on the theory, methods and implementations of AI systems that use structured knowledge to generate reliable explanations. Introductory material on knowledge graphs is included for those readers with only a minimal background in the field, as well as specific chapters devoted to advanced methods, applications and case-studies that use knowledge graphs as a part of knowledge-based, explainable systems (KBX-systems). The final chapters explore current challenges and future research directions in the area of knowledge graphs for eXplainable AI. The book not only provides a scholarly, state-of-the-art overview of research in this subject area, but also fosters the hybrid combination of symbolic and subsymbolic AI methods, and will be of interest to all those working in the field.
| Author | : National Research Council |
| Publisher | : National Academies Press |
| Total Pages | : 469 |
| Release | : 2006-01-01 |
| Genre | : Science |
| ISBN | : 030909612X |
Advances in computer science and technology and in biology over the last several years have opened up the possibility for computing to help answer fundamental questions in biology and for biology to help with new approaches to computing. Making the most of the research opportunities at the interface of computing and biology requires the active participation of people from both fields. While past attempts have been made in this direction, circumstances today appear to be much more favorable for progress. To help take advantage of these opportunities, this study was requested of the NRC by the National Science Foundation, the Department of Defense, the National Institutes of Health, and the Department of Energy. The report provides the basis for establishing cross-disciplinary collaboration between biology and computing including an analysis of potential impediments and strategies for overcoming them. The report also presents a wealth of examples that should encourage students in the biological sciences to look for ways to enable them to be more effective users of computing in their studies.
| Author | : Rajiv Pandey |
| Publisher | : Springer Nature |
| Total Pages | : 415 |
| Release | : 2021-04-12 |
| Genre | : Technology & Engineering |
| ISBN | : 303064619X |
This book is focused on an emerging area, i.e. combination of IoT and semantic technologies, which should enable breaking the silos of local and/or domain-specific IoT deployments. Taking into account the way that IoT ecosystems are realized, several challenges can be identified. Among them of definite importance are (this list is, obviously, not exhaustive): (i) How to provide common representation and/or shared understanding of data that will enable analysis across (systematically growing) ecosystems? (ii) How to build ecosystems based on data flows? (iii) How to track data provenance? (iv) How to ensure/manage trust? (v) How to search for things/data within ecosystems? (vi) How to store data and assure its quality? Semantic technologies are often considered among the possible ways of addressing these (and other, related) questions. More precisely, in academic research and in industrial practice, semantic technologies materialize in the following contexts (this list is, also, not exhaustive, but indicates the breadth of scope of semantic technology usability): (i) representation of artefacts in IoT ecosystems and IoT networks, (ii) providing interoperability between heterogeneous IoT artefacts, (ii) representation of provenance information, enabling provenance tracking, trust establishment, and quality assessment, (iv) semantic search, enabling flexible access to data originating in different places across the ecosystem, (v) flexible storage of heterogeneous data. Finally, Semantic Web, Web of Things, and Linked Open Data are architectural paradigms, with which the aforementioned solutions are to be integrated, to provide production-ready deployments.
| Author | : |
| Publisher | : |
| Total Pages | : 456 |
| Release | : 1995 |
| Genre | : Aeronautics |
| ISBN | : |
Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.
