Neural Generation Of Textual Summaries From Knowledge Base Triples
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| Author | : P. Vougiouklis |
| Publisher | : IOS Press |
| Total Pages | : 174 |
| Release | : 2020-04-07 |
| Genre | : Computers |
| ISBN | : 1643680676 |
Most people need textual or visual interfaces to help them make sense of Semantic Web data. In this book, the author investigates the problems associated with generating natural language summaries for structured data encoded as triples using deep neural networks. An end-to-end trainable architecture is proposed, which encodes the information from a set of knowledge graph triples into a vector of fixed dimensionality, and generates a textual summary by conditioning the output on this encoded vector. Different methodologies for building the required data-to-text corpora are explored to train and evaluate the performance of the approach. Attention is first focused on generating biographies, and the author demonstrates that the technique is capable of scaling to domains with larger and more challenging vocabularies. The applicability of the technique for the generation of open-domain Wikipedia summaries in Arabic and Esperanto – two under-resourced languages – is then discussed, and a set of community studies, devised to measure the usability of the automatically generated content by Wikipedia readers and editors, is described. Finally, the book explains an extension of the original model with a pointer mechanism that enables it to learn to verbalise in a different number of ways the content from the triples while retaining the capacity to generate words from a fixed target vocabulary. The evaluation of performance using a dataset encompassing all of English Wikipedia is described, with results from both automatic and human evaluation both of which highlight the superiority of the latter approach as compared to the original architecture.
| Author | : Yunyao Li |
| Publisher | : Springer Nature |
| Total Pages | : 248 |
| Release | : 2023-11-24 |
| Genre | : Computers |
| ISBN | : 3031450434 |
This book presents a comprehensive overview of Natural Language Interfaces to Databases (NLIDBs), an indispensable tool in the ever-expanding realm of data-driven exploration and decision making. After first demonstrating the importance of the field using an interactive ChatGPT session, the book explores the remarkable progress and general challenges faced with real-world deployment of NLIDBs. It goes on to provide readers with a holistic understanding of the intricate anatomy, essential components, and mechanisms underlying NLIDBs and how to build them. Key concepts in representing, querying, and processing structured data as well as approaches for optimizing user queries are established for the reader before their application in NLIDBs is explored. The book discusses text to data through early relevant work on semantic parsing and meaning representation before turning to cutting-edge advancements in how NLIDBs are empowered to comprehend and interpret human languages. Various evaluation methodologies, metrics, datasets and benchmarks that play a pivotal role in assessing the effectiveness of mapping natural language queries to formal queries in a database and the overall performance of a system are explored. The book then covers data to text, where formal representations of structured data are transformed into coherent and contextually relevant human-readable narratives. It closes with an exploration of the challenges and opportunities related to interactivity and its corresponding techniques for each dimension, such as instances of conversational NLIDBs and multi-modal NLIDBs where user input is beyond natural language. This book provides a balanced mixture of theoretical insights, practical knowledge, and real-world applications that will be an invaluable resource for researchers, practitioners, and students eager to explore the fundamental concepts of NLIDBs.
| Author | : L. Asprino |
| Publisher | : IOS Press |
| Total Pages | : 240 |
| Release | : 2020-09-25 |
| Genre | : Computers |
| ISBN | : 1643681095 |
Social robots are embodied agents that perform knowledge-intensive tasks involving several kinds of information from different heterogeneous sources. This book, Engineering Background Knowledge for Social Robots, introduces a component-based architecture for supporting the knowledge-intensive tasks performed by social robots. The design was based on the requirements of a real socially-assistive robotic application, and all the components contribute to and benefit from the knowledge base which is its cornerstone. The knowledge base is structured by a set of interconnected and modularized ontologies which model the information, and is initially populated with linguistic, ontological and factual knowledge retrieved from Linked Open Data. Access to the knowledge base is guaranteed by Lizard, a tool providing software components, with an API for accessing facts stored in the knowledge base in a programmatic and object-oriented way. The author introduces two methods for engineering the knowledge needed by robots, a novel method for automatically integrating knowledge from heterogeneous sources with a frame-driven approach, and a novel empirical method for assessing foundational distinctions over Linked Open Data entities from a common-sense perspective. These effectively enable the evolution of the robot’s knowledge by automatically integrating information derived from heterogeneous sources and the generation of common-sense knowledge using Linked Open Data as an empirical basis. The feasibility and benefits of the architecture have been assessed through a prototype deployed in a real socially-assistive scenario, and the book presents two applications and the results of a qualitative and quantitative evaluation.
| 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 | : M. Alam |
| Publisher | : IOS Press |
| Total Pages | : 284 |
| Release | : 2021-09-23 |
| Genre | : Computers |
| ISBN | : 1643682016 |
The field of semantic computing is highly diverse, linking areas such as artificial intelligence, data science, knowledge discovery and management, big data analytics, e-commerce, enterprise search, technical documentation, document management, business intelligence, and enterprise vocabulary management. As such it forms an essential part of the computing technology that underpins all our lives today. This volume presents the proceedings of SEMANTiCS 2021, the 17th International Conference on Semantic Systems. As a result of the continuing Coronavirus restrictions, SEMANTiCS 2021 was held in a hybrid form in Amsterdam, the Netherlands, from 6 to 9 September 2021. The annual SEMANTiCS conference provides an important platform for semantic computing professionals and researchers, and attracts information managers, ITarchitects, software engineers, and researchers from a wide range of organizations, such as research facilities, NPOs, public administrations and the largest companies in the world. The subtitle of the 2021 conference’s was “In the Era of Knowledge Graphs”, and 66 submissions were received, from which the 19 papers included here were selected following a rigorous single-blind reviewing process; an acceptance rate of 29%. Topics covered include data science, machine learning, logic programming, content engineering, social computing, and the Semantic Web, as well as the additional sub-topics of digital humanities and cultural heritage, legal tech, and distributed and decentralized knowledge graphs. Providing an overview of current research and development, the book will be of interest to all those working in the field of semantic systems.
| Author | : L. Heling |
| Publisher | : IOS Press |
| Total Pages | : 326 |
| Release | : 2022-03-08 |
| Genre | : Computers |
| ISBN | : 164368261X |
Knowledge graphs are increasingly used in scientific and industrial applications. The large number and size of knowledge graphs published as Linked Data in autonomous sources has led to the development of various interfaces to query these knowledge graphs. Therefore, effective query processing approaches that enable efficient information retrieval from these knowledge graphs need to address the capabilities and limitations of different Linked Data Fragment interfaces. This book investigates novel approaches to addressing the challenges that arise in the presence of decentralized, heterogeneous sources of knowledge graphs. The effectiveness of these approaches is empirically evaluated and demonstrated using various real world and synthetic large-scale knowledge graphs throughout. First, a sample-based approach for generating fine-grained performance profiles is proposed, and it is demonstrated how the information from such profiles can be leveraged in cost model-based query planning. In addition, a sample-based data distribution profiling approach is advocated which aims to estimate the statistical profile features of large knowledge graphs and the applicability of these estimations in federated querying processing is demonstrated. The remainder of the book focuses on techniques to devise efficient query processing approaches when heterogeneous interfaces need to be queried but no fine-grained statistics are available. Robust techniques to support efficient query processing in these circumstances are investigated and results are shared to demonstrate the way in which these techniques can outperform state-of-the-art approaches. Finally, the author describes a framework for federated query processing over heterogeneous federations of Linked Data Fragments to exploit the capabilities of different sources by defining interface-aware approaches.
| Author | : D.D. Janke |
| Publisher | : IOS Press |
| Total Pages | : 312 |
| Release | : 2020-03-18 |
| Genre | : Computers |
| ISBN | : 1643680692 |
The distributed setting of RDF stores in the cloud poses many challenges, including how to optimize data placement on the compute nodes to improve query performance. In this book, a novel benchmarking methodology is developed for data placement strategies; one that overcomes these limitations by using a data-placement-strategy-independent distributed RDF store to analyze the effect of the data placement strategies on query performance. Frequently used data placement strategies have been evaluated, and this evaluation challenges the commonly held belief that data placement strategies which emphasize local computation lead to faster query executions. Indeed, results indicate that queries with a high workload can be executed faster on hash-based data placement strategies than on, for example, minimal edge-cut covers. The analysis of additional measurements indicates that vertical parallelization (i.e., a well-distributed workload) may be more important than horizontal containment (i.e., minimal data transport) for efficient query processing. Two such data placement strategies are proposed: the first, found in the literature, is entitled overpartitioned minimal edge-cut cover, and the second is the newly developed molecule hash cover. Evaluation revealed a balanced query workload and a high horizontal containment, which lead to a high vertical parallelization. As a result, these strategies demonstrated better query performance than other frequently used data placement strategies. The book also tests the hypothesis that collocating small connected triple sets on the same compute node while balancing the amount of triples stored on the different compute nodes leads to a high vertical parallelization.
| Author | : M. Acosta |
| Publisher | : IOS Press |
| Total Pages | : 262 |
| Release | : 2023-10-03 |
| Genre | : Computers |
| ISBN | : 1643684256 |
Semantic computing is an integral part of modern technology, an essential component of fields as diverse as artificial intelligence, data science, knowledge discovery and management, big data analytics, e-commerce, enterprise search, technical documentation, document management, business intelligence, and enterprise vocabulary management. This book presents the proceedings of SEMANTICS 2023, the 19th International Conference on Semantic Systems, held in Leipzig, Germany, from 20 to 22 September 2023. The conference is a pivotal event for those professionals and researchers actively engaged in harnessing the power of semantic computing, an opportunity to increase their understanding of the subject’s transformative potential while confronting its practical limitations. Attendees include information managers, IT architects, software engineers, and researchers from a broad spectrum of organizations, including research facilities, non-profit entities, public administrations, and the world's largest corporations. For this year’s conference a total of 54 submissions were received in response to a call for papers. These were subjected to a rigorous, double-blind review process, with at least three independent reviews conducted for each submission. The 16 papers included here were ultimately accepted for presentation, with an acceptance rate of 29.6%. Areas covered include novel research challenges in areas such as data science, machine learning, logic programming, content engineering, social computing, and the Semantic Web. The book provides an up-to-date overview, which will be of interest to all those wishing to stay abreast of emerging trends and themes within the vast field of semantic computing.
| Author | : A. Dimou |
| Publisher | : IOS Press |
| Total Pages | : 236 |
| Release | : 2022-09-29 |
| Genre | : Computers |
| ISBN | : 1643683217 |
Semantic systems lie at the heart of modern computing, interlinking with areas as diverse as AI, data science, knowledge discovery and management, big data analytics, e-commerce, enterprise search, technical documentation, document management, business intelligence, enterprise vocabulary management, machine learning, logic programming, content engineering, social computing, and the Semantic Web. This book presents the proceedings of SEMANTiCS 2022, the 18th International Conference on Semantic Systems, held as a hybrid event – live in Vienna, Austria and online – from 12 to 15 September 2022. The SEMANTiCS conference is an annual meeting place for the professionals and researchers who make semantic computing work, who understand its benefits and encounter its limitations, and is attended by information managers, IT architects, software engineers, and researchers from organizations ranging from research facilities and NPOs, through public administrations to the largest companies in the world. The theme and subtitle of the 2022 conference was Towards A Knowledge-Aware AI, and the book contains 15 papers, selected on the basis of quality, impact and scientific merit following a rigorous review process which resulted in an acceptance rate of 29%. The book is divided into four chapters: semantics in data quality, standards and protection; representation learning and reasoning for downstream AI tasks; ontology development; and learning over complementary knowledge. Providing an overview of emerging trends and topics in the wide area of semantic computing, the book will be of interest to anyone involved in the development and deployment of computer technology and AI systems.
| Author | : G. Cota |
| Publisher | : IOS Press |
| Total Pages | : 244 |
| Release | : 2020-12-02 |
| Genre | : Computers |
| ISBN | : 1643681435 |
Semantic Web technologies enable people to create data stores on the Web, build vocabularies, and write rules for handling data. They have been in use for several years now, and knowledge extraction and knowledge discovery are two key aspects investigated in a number of research fields which can potentially benefit from the application of semantic web technologies, and specifically from the development and reuse of ontologies. This book, Applications and Practices in Ontology Design, Extraction, and Reasoning, has as its main goal the provision of an overview of application fields for semantic web technologies. In particular, it investigates how state-of-the-art formal languages, models, methods, and applications of semantic web technologies reframe research questions and approaches in a number of research fields. The book also aims to showcase practical tools and background knowledge for the building and querying of ontologies. The first part of the book presents the state-of-the-art of ontology design, applications and practices in a number of communities, and in doing so it provides an overview of the latest approaches and techniques for building and reusing ontologies according to domain-dependent and independent requirements. Once the data is represented according to ontologies, it is important to be able to query and reason about them, also in the presence of uncertainty, vagueness and probabilities. The second part of the book covers some of the latest advances in the fields of ontology, semantics and reasoning, without losing sight of the book’s practical goals.
