Exploiting Semantic Web Knowledge Graphs In Data Mining

Author : P. Ristoski
Publisher : IOS Press
Page : 246 pages
File Size : 50,9 Mb
Release : 2019-06-28
Category : Computers
ISBN : 9781614999812

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Exploiting Semantic Web Knowledge Graphs in Data Mining by P. Ristoski Pdf

Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in this field are knowledge intensive and can benefit from additional knowledge from various sources, so many approaches have been proposed that combine Semantic Web data with the data mining and knowledge discovery process. This book, Exploiting Semantic Web Knowledge Graphs in Data Mining, aims to show that Semantic Web knowledge graphs are useful for generating valuable data mining features that can be used in various data mining tasks. In Part I, Mining Semantic Web Knowledge Graphs, the author evaluates unsupervised feature generation strategies from types and relations in knowledge graphs used in different data mining tasks such as classification, regression, and outlier detection. Part II, Semantic Web Knowledge Graphs Embeddings, proposes an approach that circumvents the shortcomings introduced with the approaches in Part I, developing an approach that is able to embed complete Semantic Web knowledge graphs in a low dimensional feature space where each entity and relation in the knowledge graph is represented as a numerical vector. Finally, Part III, Applications of Semantic Web Knowledge Graphs, describes a list of applications that exploit Semantic Web knowledge graphs like classification and regression, showing that the approaches developed in Part I and Part II can be used in applications in various domains. The book will be of interest to all those working in the field of data mining and KDD.

Author : Anonim
Publisher : Unknown
Page : 128 pages
File Size : 52,7 Mb
Release : 2019
Category : Electronic
ISBN : 3898387429

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Exploiting Semantic Web Knowledge Graphs in Data Mining by Anonim Pdf

Author : Jeff Z. Pan,Guido Vetere,Jose Manuel Gomez-Perez,Honghan Wu
Publisher : Springer
Page : 281 pages
File Size : 49,8 Mb
Release : 2017-01-24
Category : Computers
ISBN : 9783319456546

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Exploiting Linked Data and Knowledge Graphs in Large Organisations by Jeff Z. Pan,Guido Vetere,Jose Manuel Gomez-Perez,Honghan Wu Pdf

This book addresses the topic of exploiting enterprise-linked data with a particular focus on knowledge construction and accessibility within enterprises. It identifies the gaps between the requirements of enterprise knowledge consumption and “standard” data consuming technologies by analysing real-world use cases, and proposes the enterprise knowledge graph to fill such gaps. It provides concrete guidelines for effectively deploying linked-data graphs within and across business organizations. It is divided into three parts, focusing on the key technologies for constructing, understanding and employing knowledge graphs. Part 1 introduces basic background information and technologies, and presents a simple architecture to elucidate the main phases and tasks required during the lifecycle of knowledge graphs. Part 2 focuses on technical aspects; it starts with state-of-the art knowledge-graph construction approaches, and then discusses exploration and exploitation techniques as well as advanced question-answering topics concerning knowledge graphs. Lastly, Part 3 demonstrates examples of successful knowledge graph applications in the media industry, healthcare and cultural heritage, and offers conclusions and future visions.

Author : Aidan Hogan,Eva Blomqvist,Michael Cochez,Claudia d’Amato,Gerard de Melo,Claudio Gutierrez,Sabrina Kirrane,Jose Emilio Labra Gayo,Roberto Navigli,Sebastian Neumaier,Axel Polleres,Sabbir Rashid,Anisa Rula,Antoine Zimmermann,Lukas Schmelzeisen,Axel-Cyrille Ngonga Ngomo,Juan Sequeda,Steffen Staab
Publisher : Springer Nature
Page : 247 pages
File Size : 51,7 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031019180

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Knowledge Graphs by Aidan Hogan,Eva Blomqvist,Michael Cochez,Claudia d’Amato,Gerard de Melo,Claudio Gutierrez,Sabrina Kirrane,Jose Emilio Labra Gayo,Roberto Navigli,Sebastian Neumaier,Axel Polleres,Sabbir Rashid,Anisa Rula,Antoine Zimmermann,Lukas Schmelzeisen,Axel-Cyrille Ngonga Ngomo,Juan Sequeda,Steffen Staab Pdf

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 : A. Ławrynowicz
Publisher : IOS Press
Page : 210 pages
File Size : 52,5 Mb
Release : 2017-04-18
Category : Computers
ISBN : 9781614997467

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Semantic Data Mining by A. Ławrynowicz Pdf

Ontologies are now increasingly used to integrate, and organize data and knowledge, particularly in data and knowledge-intensive applications in both research and industry. The book is devoted to semantic data mining – a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to mine knowledge encoded in domain ontologies and knowledge graphs, rather than only purely empirical data. The introductory chapters of the book provide theoretical foundations of both data mining and ontology representation. Taking a unified perspective, the book then covers several methods for semantic data mining, addressing tasks such as pattern mining, classification and similarity-based approaches. It attempts to provide state-of-the-art answers to specific challenges and peculiarities of data mining with use of ontologies, in particular: How to deal with incompleteness of knowledge and the so-called Open World Assumption? What is a truly “semantic” similarity measure? The book contains several chapters with examples of applications of semantic data mining. The examples start from a scenario with moderate use of lightweight ontologies for knowledge graph enrichment and end with a full-fledged scenario of an intelligent knowledge discovery assistant using complex domain ontologies for meta-mining, i.e., an ontology-based meta-learning approach to full data mining processes. The book is intended for researchers in the fields of semantic technologies, knowledge engineering, data science, and data mining, and developers of knowledge-based systems and applications.

Author : Sanju Tiwari,Fernando Ortiz Rodriguez,Sarra Ben Abbes,Patience Usoro Usip,Rim Hantach
Publisher : CRC Press
Page : 217 pages
File Size : 49,6 Mb
Release : 2023-08-21
Category : Computers
ISBN : 9781000911183

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Semantic AI in Knowledge Graphs by Sanju Tiwari,Fernando Ortiz Rodriguez,Sarra Ben Abbes,Patience Usoro Usip,Rim Hantach Pdf

Existing research papers do not have complete information in depth about the Semantic AI in Knowledge Graphs. This book has all the basic information required to gain in-depth knowledge of this field. Covers neuro-symbolic AI, explainable AI and deep learning to knowledge discover and mining, and knowledge representation and reasoning.

Author : Agnieszka Ławrynowicz
Publisher : Unknown
Page : 194 pages
File Size : 47,9 Mb
Release : 2017
Category : Data mining
ISBN : 3898387240

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Semantic Data Mining by Agnieszka Ławrynowicz Pdf

"Ontologies are now increasingly used to integrate, and organize data and knowledge, particularly in data and knowledge-intensive applications in both research and industry. The book is devoted to semantic data mining--a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to mine knowledge encoded in domain ontologies and knowledge graphs, rather than only purely empirical data."--page [4] of cover.

Author : Lior Rokach,Oded Maimon,Erez Shmueli
Publisher : Springer Nature
Page : 975 pages
File Size : 45,8 Mb
Release : 2023-08-17
Category : Computers
ISBN : 9783031246289

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Machine Learning for Data Science Handbook by Lior Rokach,Oded Maimon,Erez Shmueli Pdf

This book organizes key concepts, theories, standards, methodologies, trends, challenges and applications of data mining and knowledge discovery in databases. It first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. It also gives in-depth descriptions of data mining applications in various interdisciplinary industries.

Author : Mayank Kejriwal,Craig A. Knoblock,Pedro Szekely
Publisher : MIT Press
Page : 559 pages
File Size : 50,6 Mb
Release : 2021-03-30
Category : Computers
ISBN : 9780262045094

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Knowledge Graphs by Mayank Kejriwal,Craig A. Knoblock,Pedro Szekely Pdf

A rigorous and comprehensive textbook covering the major approaches to knowledge graphs, an active and interdisciplinary area within artificial intelligence. The field of knowledge graphs, which allows us to model, process, and derive insights from complex real-world data, has emerged as an active and interdisciplinary area of artificial intelligence over the last decade, drawing on such fields as natural language processing, data mining, and the semantic web. Current projects involve predicting cyberattacks, recommending products, and even gleaning insights from thousands of papers on COVID-19. This textbook offers rigorous and comprehensive coverage of the field. It focuses systematically on the major approaches, both those that have stood the test of time and the latest deep learning methods.

Author : Albert Meroño-Peñuela,Pasquale Lisena,Carlos Martínez-Ortiz
Publisher : Springer Nature
Page : 92 pages
File Size : 49,6 Mb
Release : 2022-05-31
Category : Computers
ISBN : 9783031019173

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Web Data APIs for Knowledge Graphs by Albert Meroño-Peñuela,Pasquale Lisena,Carlos Martínez-Ortiz Pdf

This book describes a set of methods, architectures, and tools to extend the data pipeline at the disposal of developers when they need to publish and consume data from Knowledge Graphs (graph-structured knowledge bases that describe the entities and relations within a domain in a semantically meaningful way) using SPARQL, Web APIs, and JSON. To do so, it focuses on the paradigmatic cases of two middleware software packages, grlc and SPARQL Transformer, which automatically build and run SPARQL-based REST APIs and allow the specification of JSON schema results, respectively. The authors highlight the underlying principles behind these technologies—query management, declarative languages, new levels of indirection, abstraction layers, and separation of concerns—, explain their practical usage, and describe their penetration in research projects and industry. The book, therefore, serves a double purpose: to provide a sound and technical description of tools and methods at the disposal of publishers and developers to quickly deploy and consume Web Data APIs on top of Knowledge Graphs; and to propose an extensible and heterogeneous Knowledge Graph access infrastructure that accommodates a growing ecosystem of querying paradigms.

Author : Valentina Janev,Damien Graux,Hajira Jabeen,Emanuel Sallinger
Publisher : Springer Nature
Page : 212 pages
File Size : 45,6 Mb
Release : 2020-07-15
Category : Computers
ISBN : 9783030531997

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Knowledge Graphs and Big Data Processing by Valentina Janev,Damien Graux,Hajira Jabeen,Emanuel Sallinger Pdf

This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.

Author : B. Yan
Publisher : IOS Press
Page : 170 pages
File Size : 40,5 Mb
Release : 2019-08-08
Category : Computers
ISBN : 9781614999898

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Geographic Knowledge Graph Summarization by B. Yan Pdf

Geographic knowledge graphs can have an important role in delivering interoperability, accessibility and the demands of conceptualization in geographic information science (GIS). However, the massive amount of accompanying information and the enormous diversity of geographic knowledge graphs limits their applicability and hinders the widespread adoption of this useful structured knowledge. This book, Geographic Knowledge Graph Summarization, focuses on the ways in which geographic knowledge graphs can be digested and summarized. Such a summarization would relieve the burden of information overload for end users and reduce data storage, as well as speeding up queries and eliminating ‘noise’. The book introduces the general concept of geospatial inductive bias and explains the different ways in which this idea can be used in the summarization of geographic knowledge graphs. The book breaks up the task of summarization into separate but related components, and after an introduction and a brief overview of concepts and theories, Chapters 3, 4 and 5 explore hierarchical place type structure, multimedia leaf nodes, and general relation and entity components respectively. Chapter 6 presents a spatial knowledge map interface which illustrates the effectiveness of summarization. The book integrates top-down knowledge engineering and bottom-up knowledge learning methods, and will do much to promote awareness of this fascinating area and related issues.

Author : Leslie F. Sikos,Oshani W. Seneviratne,Deborah L. McGuinness
Publisher : Springer Nature
Page : 110 pages
File Size : 48,9 Mb
Release : 2021-04-26
Category : Computers
ISBN : 9783030676810

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Provenance in Data Science by Leslie F. Sikos,Oshani W. Seneviratne,Deborah L. McGuinness Pdf

RDF-based knowledge graphs require additional formalisms to be fully context-aware, which is presented in this book. This book also provides a collection of provenance techniques and state-of-the-art metadata-enhanced, provenance-aware, knowledge graph-based representations across multiple application domains, in order to demonstrate how to combine graph-based data models and provenance representations. This is important to make statements authoritative, verifiable, and reproducible, such as in biomedical, pharmaceutical, and cybersecurity applications, where the data source and generator can be just as important as the data itself. Capturing provenance is critical to ensure sound experimental results and rigorously designed research studies for patient and drug safety, pathology reports, and medical evidence generation. Similarly, provenance is needed for cyberthreat intelligence dashboards and attack maps that aggregate and/or fuse heterogeneous data from disparate data sources to differentiate between unimportant online events and dangerous cyberattacks, which is demonstrated in this book. Without provenance, data reliability and trustworthiness might be limited, causing data reuse, trust, reproducibility and accountability issues. This book primarily targets researchers who utilize knowledge graphs in their methods and approaches (this includes researchers from a variety of domains, such as cybersecurity, eHealth, data science, Semantic Web, etc.). This book collects core facts for the state of the art in provenance approaches and techniques, complemented by a critical review of existing approaches. New research directions are also provided that combine data science and knowledge graphs, for an increasingly important research topic.

Author : Mayank Kejriwal
Publisher : Springer
Page : 107 pages
File Size : 40,6 Mb
Release : 2019-03-04
Category : Computers
ISBN : 9783030123758

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Domain-Specific Knowledge Graph Construction by Mayank Kejriwal Pdf

The vast amounts of ontologically unstructured information on the Web, including HTML, XML and JSON documents, natural language documents, tweets, blogs, markups, and even structured documents like CSV tables, all contain useful knowledge that can present a tremendous advantage to the Artificial Intelligence community if extracted robustly, efficiently and semi-automatically as knowledge graphs. Domain-specific Knowledge Graph Construction (KGC) is an active research area that has recently witnessed impressive advances due to machine learning techniques like deep neural networks and word embeddings. This book will synthesize Knowledge Graph Construction over Web Data in an engaging and accessible manner. The book will describe a timely topic for both early -and mid-career researchers. Every year, more papers continue to be published on knowledge graph construction, especially for difficult Web domains. This work would serve as a useful reference, as well as an accessible but rigorous overview of this body of work. The book will present interdisciplinary connections when possible to engage researchers looking for new ideas or synergies. This will allow the book to be marketed in multiple venues and conferences. The book will also appeal to practitioners in industry and data scientists since it will have chapters on both data collection, as well as a chapter on querying and off-the-shelf implementations. The author has, and continues to, present on this topic at large and important conferences. He plans to make the powerpoint he presents available as a supplement to the work. This will draw a natural audience for the book. Some of the reviewers are unsure about his position in the community but that seems to be more a function of his age rather than his relative expertise. I agree with some of the reviewers that the title is a little complicated. I would recommend “Domain Specific Knowledge Graphs”.

Author : D. Collarana
Publisher : IOS Press
Page : 158 pages
File Size : 53,8 Mb
Release : 2020-01-24
Category : Computers
ISBN : 9781643680477

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Strategies and Techniques for Federated Semantic Knowledge Integration and Retrieval by D. Collarana Pdf

The vast amount of data available on the web has led to the need for effective retrieval techniques to transform that data into usable machine knowledge. But the creation of integrated knowledge, especially knowledge about the same entity from different web data sources, is a challenging task requiring the solving of interoperability problems. This book addresses the problem of knowledge retrieval and integration from heterogeneous web sources, and proposes a holistic semantic knowledge retrieval and integration approach to creating knowledge graphs on-demand from diverse web sources. Semantic Web Technologies have evolved as a novel approach to tackle the problem of knowledge integration from heterogeneous data, but because of the Extraction-Transformation-Load approach that dominates the process, knowledge retrieval and integration from web data sources is either expensive, or full physical integration of the data is impeded by restricted access. Focusing on the representation of data from web sources as pieces of knowledge belonging to the same entity which can then be synthesized as a knowledge graph helps to solve interoperability conflicts and allow for a more cost-effective integration approach, providing a method that enables the creation of valuable insights from heterogeneous web data. Empirical evaluations to assess the effectiveness of this holistic approach provide evidence that the methodology and techniques proposed in this book help to effectively integrate the disparate knowledge spread over heterogeneous web data sources, and the book also demonstrates how three domain applications of law enforcement, job market analysis, and manufacturing, have been developed and managed using the approach.