Pascal Hitzler – författare

Neuro-Symbolic AI: Integrating Neural Networks and Symbolic Reasoning explores the convergence of two historically distinct paradigms in artificial intelligence-data-driven neural networks and logic-based symbolic reasoning. This book presents a comprehensive roadmap of this emerging hybrid discipline, offering deep theoretical insights, practical methodologies, and transformative applications across diverse research sectors including healthcare, finance, engineering, and autonomous systems. While neural networks have achieved remarkable success in perception and pattern recognition tasks, they often lack the reasoning, transparency, and generalizability that symbolic systems excel at. Conversely, symbolic AI lacks the flexibility and scalability of deep learning. This handbook directly addresses these challenges by providing a structured approach to Neuro-symbolic AI, presenting rigorous theoretical foundations, state-of-the-art hybrid techniques (e.g., knowledge graphs, compositionality, category theory), and diverse real-world applications. This book consolidates research insights, methodological innovations, and practical use cases into a single, accessible volume. The book is structured into four parts-Foundational Principles, Hybrid Models and Techniques, Real-World Applications, and Emerging Challenges. It brings together cutting-edge research and expert perspectives to highlight how Neuro-Symbolic AI enhances interpretability, reasoning capabilities, and trust in intelligent systems. This book addresses the critical challenge faced by AI researchers and practitioners: how to build intelligent systems that combine the learning capacity of neural networks with the reasoning ability of symbolic methods. Readers often struggle with the lack of unified frameworks, practical tools, and clear guidance for integrating these two approaches. This book provides readers with a comprehensive, structured, and interdisciplinary resource that captures the evolving landscape of Neuro-Symbolic AI.Presents theoretical advances, hybrid model architectures, and practical applications in one volume, providing comprehensive coverage across machine learning, symbolic AI, and cognitive computing.Includes real-world case studies from healthcare, autonomous systems, and scientific discovery that offer readers concrete insights into how Neuro-Symbolic AI is implemented, demonstrating its impact in critical domains where interpretability and reasoning are essential.Features a dedicated section on emerging challenges and future directions, such as scalability, energy efficiency, security, and AGI potential, giving readers a roadmap for future research, innovation, and responsible deployment of hybrid AI systems.

Covering the authors’ own state-of-the-art research results, Mathematical Aspects of Logic Programming Semantics presents a rigorous, modern account of the mathematical methods and tools required for the semantic analysis of logic programs. It significantly extends the tools and methods from traditional order theory to include nonconventional methods from mathematical analysis that depend on topology, domain theory, generalized distance functions, and associated fixed-point theory.The book covers topics spanning the period from the early days of logic programming to current times. It discusses applications to computational logic and potential applications to the integration of models of computation, knowledge representation and reasoning, and the Semantic Web. The authors develop well-known and important semantics in logic programming from a unified point of view using both order theory and new, nontraditional methods. They closely examine the interrelationships between various semantics as well as the integration of logic programming and connectionist systems/neural networks.For readers interested in the interface between mathematics and computer science, this book offers a detailed development of the mathematical techniques necessary for studying the semantics of logic programs. It illustrates the main semantics of logic programs and applies the methods in the context of neural-symbolic integration.

Exploring fundamental research questions, Conceptual Structures in Practice takes you through the basic yet nontrivial task of establishing conceptual relations as the foundation for research in knowledge representation and knowledge mining. It includes contributions from leading researchers in both the conceptual graph and formal concept analysis (FCA) communities.This accessible, self-contained book begins by providing the formal background in FCA and conceptual graphs. It then describes various software tools for analysis and computation, including the ToscanaJ suite. Written by the original visionaries of the field, the next section discusses the history and future directions of conceptual structures. The final chapters explore prominent application areas in computer science, including text analysis, web semantics, and intelligent systems.An unprecedented, state-of-the-art overview from innovators in the field, this volume discusses how FCA and conceptual graphs can be used in many computer science areas. It serves as a benchmark of research on conceptual structures, inspiring further exploration in this discipline.

Exploring fundamental research questions, Conceptual Structures in Practice takes you through the basic yet nontrivial task of establishing conceptual relations as the foundation for research in knowledge representation and knowledge mining. It includes contributions from leading researchers in both the conceptual graph and formal concept analysis (FCA) communities.This accessible, self-contained book begins by providing the formal background in FCA and conceptual graphs. It then describes various software tools for analysis and computation, including the ToscanaJ suite. Written by the original visionaries of the field, the next section discusses the history and future directions of conceptual structures. The final chapters explore prominent application areas in computer science, including text analysis, web semantics, and intelligent systems.An unprecedented, state-of-the-art overview from innovators in the field, this volume discusses how FCA and conceptual graphs can be used in many computer science areas. It serves as a benchmark of research on conceptual structures, inspiring further exploration in this discipline.

With more substantial funding from research organizations and industry, numerous large-scale applications, and recently developed technologies, the Semantic Web is quickly emerging as a well-recognized and important area of computer science. While Semantic Web technologies are still rapidly evolving, Foundations of Semantic Web Technologies focuses on the established foundations in this area that have become relatively stable over time. It thoroughly covers basic introductions and intuitions, technical details, and formal foundations.The book concentrates on Semantic Web technologies standardized by the World Wide Web Consortium: RDF and SPARQL enable data exchange and querying, RDFS and OWL provide expressive ontology modeling, and RIF supports rule-based modeling. The text also describes methods for specifying, querying, and reasoning with ontological information. In addition, it explores topics that are clearly beyond foundations, such as tools, applications, and engineering aspects.Written by highly respected researchers with a deep understanding of the material, this text centers on the formal specifications of the subject and supplies many pointers that are useful for employing Semantic Web technologies in practice.The book has an accompanying website with supplemental information.

Covering the authors’ own state-of-the-art research results, Mathematical Aspects of Logic Programming Semantics presents a rigorous, modern account of the mathematical methods and tools required for the semantic analysis of logic programs. It significantly extends the tools and methods from traditional order theory to include nonconventional methods from mathematical analysis that depend on topology, domain theory, generalized distance functions, and associated fixed-point theory.The book covers topics spanning the period from the early days of logic programming to current times. It discusses applications to computational logic and potential applications to the integration of models of computation, knowledge representation and reasoning, and the Semantic Web. The authors develop well-known and important semantics in logic programming from a unified point of view using both order theory and new, nontraditional methods. They closely examine the interrelationships between various semantics as well as the integration of logic programming and connectionist systems/neural networks.For readers interested in the interface between mathematics and computer science, this book offers a detailed development of the mathematical techniques necessary for studying the semantics of logic programs. It illustrates the main semantics of logic programs and applies the methods in the context of neural-symbolic integration.

Neural approaches have traditionally excelled at perceptual tasks like pattern recognition, whereas symbolic frameworks have offered powerful methods for knowledge representation, logical inference, and interpretability, but the current AI landscape is increasingly defined by hybrid systems that blend these complementary paradigms. This is particularly relevant in the context of knowledge graphs (KGs), which serve as a bridge between symbolic logic and the subsymbolic world of deep learning. The Handbook on Neurosymbolic AI and Knowledge Graphs deals with state-of-the-art neurosymbolic and KG-based AI, reflecting an ecosystem in which large language models, deep neural networks, and symbolic representations converge. It illustrates the progress that has been made, while also revealing emerging challenges in trustworthiness, interpretability, and scalability. The first four chapters are on the foundations of neural and symbolic AI. In the following chapters the authors explore the nuances of KG representation and embeddings, moving on to KG construction, integration, and quality, and covering challenges such as entity alignment, canonicalization, fusion, and the critical aspect of uncertainty management. Offering solutions that seamlessly combine symbolic logic with deep learning pipelines, the handbook deals with question answering, program synthesis, and dynamic KG methods, before moving on to the need to ensure transparency, accountability, and trust in systems operating on increasingly complex data. The final chapters demonstrate problem solving across news analytics, literary studies, life sciences, food computing, social media, and more. This work offers a comprehensive overview of these intersecting fields and will be of interest to researchers and developers looking for a practical guide to building AI systems that are robust, transparent, and ethically grounded.

This book constitutes the refereed proceedings of the 16th International Semantic Web Conference, ESWC 2019, held in Portorož, Slovenia.The 39 revised full papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in three tracks: research track, resources track, and in-use track and deal with the following topical areas: distribution and decentralisation, velocity on the Web, research of research, ontologies and reasoning, linked data, natural language processing and information retrieval, semantic data management and data infrastructures, social and human aspects of the Semantic Web, and, machine learning.

This book constitutes the thoroughly refereed post-conference proceedings of the Satellite Events of the 16th Extended Semantic Web Conference, ESWC 2019, held in Portorož, Slovenia, in June 2019.The volume contains 38 poster and demonstration papers, 2 workshop papers,5 PhD symposium papers, and 3 industry track papers, selected out of a total of 68 submissions. They deal with all areas of semantic web research, semantic technologies on the Web and Linked Data.

This book constitutes the refereed proceedings of the 16th International Conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2014, held in Varna, Bulgaria in September 2014.

The two volumes LNCS 10249 and 10250 constitute the refereed proceedings of the 14th International Semantic Web Conference, ESWC 2017, held in Portorož, Slovenia. The 51 revised full papers presented were carefully reviewed and selected from 183 submissions.

The two volumes LNCS 10249 and 10250 constitute the refereed proceedings of the 14th International Semantic Web Conference, ESWC 2017, held in Portorož, Slovenia. The 51 revised full papers presented were carefully reviewed and selected from 183 submissions.

This book constitutes the refereed proceedings of the 15th International Semantic Web Conference, ESWC 2018, held in Heraklion, Crete, Greece.The 48 revised full papers presented were carefully reviewed and selected from 179 submissions. The papers cover a large range of topics such as logical modelling and reasoning, natural language processing, databases and data storage and access, machine learning, distributed systems, information retrieval and data mining, social networks, and Web science and Web engineering.

Das Buch Semantic Web – Grundlagen vermittelt als erstes deutschsprachiges Lehrbuch die Grundlagen des Semantic Web in verständlicher Weise. Es ermöglicht einen einfachen und zügigen Einstieg in Methoden und Technologien des Semantic Web und kann z.B. als solide Grundlage für die Vorbereitung und Durchführung von Vorlesungen genutzt werden. Die Autoren trennen dabei sauber zwischen einer intuitiven Hinführung zur Verwendung semantischer Technologien in der Praxis einerseits, und der Erklärung formaler und theoretischer Hintergründe andererseits. Nur für letzteres werden Grundkenntnisse in Logik vorausgesetzt, die sich bei Bedarf jedoch durch zusätzliche Lektüre und mit Hilfe eines entsprechenden Kapitels im Anhang aneignen lassen.Das Lehrbuch richtet sich primär an Studenten mit Grundkenntnissen in Informatik sowie an interessierte Praktiker welche sich im Bereich Semantic Web fortbilden möchten. Aus den Rezensionen:"… RDF, RDF-S und OWL. Diese Sprachen … werden von den Autoren dargestellt. Bei der Darstellung … fallen sie selten zu schwierigen Fachslang, sondern liefern eine gut nachvollziehbare Schilderung mit einfachen Beispielen, auch Übungsaufgaben runden die Kapitel ab. … Semantic Web ist ein einfach geschriebenes und anschauliches Buch, das In die Grundkonzepte der Semantic-Web-Techniken einführt. Wer sich schnell in RDF, RDF-S und Co. einarbeiten muss und etwas Vorbildung in Logik und Algebra mitbringt, der trifft mit diesem Lehrbuch sicherlich eine gute Wahl …" (http://www.literaturnetz.com/content/view/8742/44/)

th The 14 International Conference on Conceptual Structures (ICCS 2006) was held in Aalborg, Denmark during July 16 – 21, 2006. Responding to the Call for Papers, we received 62 papers from 20 different countries, representing six different continents. This clearly indicates the international nature of the ICCS community as well as the widespread interest which was spawned by the previous conferences. By a thorough review process, 24 papers were selected to be included in this volume. In addition, six invited speakers made contributions which can be found in the first section of this volume. The theme of ICCS 2006––Conceptual Structures: Inspiration and Application–– points to a dual focus of interest that is also reflected in the constellation of papers. From the beginning of the planning of this conference, we focused on inspirational sources that have led to the current state of research in our community, by tracing important historical influences which daily effect work in representing knowledge and in handling representations of conceptual structures. At the same time, we also focused on ways in which these legacies are employed to further advance theory and practice in the field of knowledge representation and processing. With this volume, we believe that a valuable contribution to both aspects of this field is being made. We wish to express our appreciation to all the authors of submitted papers, to the members of the Editorial Board and the Program Committee for all their work and valuable comments.

The human brain possesses the remarkable capability of understanding, - terpreting, and producing human language, thereby relying mostly on the left hemisphere. The ability to acquire language is innate as can be seen from d- orders such as speci?c language impairment (SLI), which manifests itself in a missing sense for grammaticality. Language exhibits strong compositionality and structure. Hence biological neural networks are naturally connected to processing and generation of high-level symbolic structures. Unlike their biological counterparts, arti?cial neural networks and logic do not form such a close liason. Symbolic inference mechanisms and statistical machine learning constitute two major and very di?erent paradigms in ar- ?cial intelligence which both have their strengths and weaknesses: Statistical methods o?er ?exible and highly e?ective tools which are ideally suited for possibly corrupted or noisy data, high uncertainty and missing information as occur in everyday life such as sensor streams in robotics, measurements in medicine such as EEG and EKG, ?nancial and market indices, etc. The m- els, however, are often reduced to black box mechanisms which complicate the integration of prior high level knowledge or human inspection, and they lack theabilitytocopewitharichstructureofobjects,classes,andrelations. S- bolic mechanisms, on the other hand, are perfectly applicative for intuitive human-machine interaction, the integration of complex prior knowledge, and well founded recursive inference. Their capability of dealing with uncertainty andnoiseandtheire?ciencywhenaddressingcorruptedlargescalereal-world data sets, however, is limited. Thus, the inherent strengths and weaknesses of these two methods ideally complement each other.

The human brain possesses the remarkable capability of understanding, - terpreting, and producing human language, thereby relying mostly on the left hemisphere. The ability to acquire language is innate as can be seen from d- orders such as speci?c language impairment (SLI), which manifests itself in a missing sense for grammaticality. Language exhibits strong compositionality and structure. Hence biological neural networks are naturally connected to processing and generation of high-level symbolic structures. Unlike their biological counterparts, arti?cial neural networks and logic do not form such a close liason. Symbolic inference mechanisms and statistical machine learning constitute two major and very di?erent paradigms in ar- ?cial intelligence which both have their strengths and weaknesses: Statistical methods o?er ?exible and highly e?ective tools which are ideally suited for possibly corrupted or noisy data, high uncertainty and missing information as occur in everyday life such as sensor streams in robotics, measurements in medicine such as EEG and EKG, ?nancial and market indices, etc. The m- els, however, are often reduced to black box mechanisms which complicate the integration of prior high level knowledge or human inspection, and they lack theabilitytocopewitharichstructureofobjects,classes,andrelations. S- bolic mechanisms, on the other hand, are perfectly applicative for intuitive human-machine interaction, the integration of complex prior knowledge, and well founded recursive inference. Their capability of dealing with uncertainty andnoiseandtheire?ciencywhenaddressingcorruptedlargescalereal-world data sets, however, is limited. Thus, the inherent strengths and weaknesses of these two methods ideally complement each other.

The Semantic Web aims at allowing knowledge to be freely accessed and - changed by software. It is now widely recognized that if the Semantic Web is to contain deep knowledge, the need for new representation and reasoning te- niques is critical. These techniques need to ?nd the right trade-o? between - pressiveness, scalability, and robustness to deal with the inherently incomplete, contradictory, and uncertain nature of knowledge on the Web. The annual International Conference on Web Reasoning and Rule Systems (RR) addresses these needs and has grown into a major international forum for the discussion and dissemination of new results concerning Web Reasoning and Rule Systems. The ?rst three International Conferences on Web Reasoning and Rule Systems (see http://www.rr-conference.org), held in Innsbruck, A- tria (2007), Karlsruhe, Germany (2008), and Chantilly, Virginia, USA (2009), received enthusiastic support from the Web Reasoning community.This volume contains the papers presented at the Fourth International C- ference on Web Reasoning and Rule Systems (RR 2010), which was held in Bressanone/Brixen, Italy, September 22-24, 2010, and which continued the - cellence of the RR series. It contains nine full papers, six short papers, four poster/position papers, one PhD paper, and two system descriptions, which wereselectedoutof31submissionsfollowinga rigorousreviewingprocess,where each submission was reviewed by at least three program committee members. The volumealso containsextended abstractsof the threeinvited talks/tutorials.

The two-volume set LNCS 6496 and 6497 constitutes the refereed proceedings of the 9th International Semantic Web Conference, ISWC 2010, held in Shanghai, China, during November 7-11, 2010. Part I contains 51 papers out of 578 submissions to the research track. Part II contains 18 papers out of 66 submissions to the semantic Web in-use track, 6 papers out of 26 submissions to the doctoral consortium track, and also 4 invited talks. Each submitted paper were carefully reviewed. The International Semantic Web Conferences (ISWC) constitute the major international venue where the latest research results and technical innovations on all aspects of the Semantic Web are presented. ISWC brings together researchers, practitioners, and users from the areas of artificial intelligence, databases, social networks, distributed computing, Web engineering, information systems, natural language processing, soft computing, and human computer interaction to discuss the major challenges and proposed solutions, the success stories and failures, as well the visions that can advance research and drive innovation in the Semantic Web.

The two-volume set LNCS 6496 and 6497 constitutes the refereed proceedings of the 9th International Semantic Web Conference, ISWC 2010, held in Shanghai, China, during November 7-11, 2010. Part I contains 51 papers out of 578 submissions to the research track. Part II contains 18 papers out of 66 submissions to the semantic Web in-use track, 6 papers out of 26 submissions to the doctoral consortium track, and also 4 invited talks. Each submitted paper were carefully reviewed. The International Semantic Web Conferences (ISWC) constitute the major international venue where the latest research results and technical innovations on all aspects of the Semantic Web are presented. ISWC brings together researchers, practitioners, and users from the areas of artificial intelligence, databases, social networks, distributed computing, Web engineering, information systems, natural language processing, soft computing, and human computer interaction to discuss the major challenges and proposed solutions, the success stories and failures, as well the visions that can advance research and drive innovation in the Semantic Web.

The two-volume set LNCS 7044 and 7045 constitutes the refereed proceedings of three confederated international conferences: Cooperative Information Systems (CoopIS 2011), Distributed Objects and Applications - Secure Virtual Infrastructures (DOA-SVI 2011), and Ontologies, DataBases and Applications of SEmantics (ODBASE 2011) held as part of OTM 2011 in October 2011 in Hersonissos on the island of Crete, Greece. The 55 revised full papers presented were carefully reviewed and selected from a total of 141 submissions. The 28 papers included in the second volume constitute the proceedings of DOA-SVI 2011 with 15 full papers organized in topical sections on performance measurement and optimization, instrumentation, monitoring, and provisioning, quality of service, security and privacy, and models and methods, and ODBASE 2011 with 9 full papers organized in topical sections on acquisition of semantic information, use of semantic information, and reuse of semantic information and 4 short papers.