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The book is composed of two main parts. The first part is a general study of Semantic Web Search. The second part specifically focuses on the use of semantics throughout the search process, compiling a big picture of Process-oriented Semantic Web Search from different pieces of work that target specific aspects of the process. In particular, this book provides a rigorous account of the concepts and technologies proposed for searching resources and semantic data on the Semantic Web. To collate the various approaches and to better understand what the notion of Semantic Web Search entails, this book presents a general Semantic Web Search model. With respect to this model, the book provides a comprehensive discussion of the state-of-the-art. It elaborates on approaches for crawling, managing and searching Semantic Web resources as well as the various schemes proposed for ranking search results. Besides these specific approaches, search is also studied in a general multi-data-source scenario. This shall demonstrate how this work on search is extended and applied to the Web setting. A major feature of the book is that it considers search and the use of semantics for search also from a process point of view. Extending the general model, the book introduces the notion of Process-oriented Semantic Web Search, where semantics is exploited throughout the entire search process – from query construction to query processing up to result presentation and query refinement. Specific pieces of work targeting these individual steps of the process are combined to form a coherent and consistent picture of Process-oriented Semantic Web Search. In order to convey this general notion as well as the specific concepts and technologies developed for supporting the search process, this book presents a compilation of work called SemSearchPro and provides detailed descriptions on the underlying approaches.
This book describes methodologies for developing semantic applications. Semantic applications are software applications which explicitly or implicitly use the semantics, i.e. the meaning of a domain terminology, in order to improve usability, correctness, and completeness. An example is semantic search, where synonyms and related terms are used for enriching the results of a simple text-based search. Ontologies, thesauri or controlled vocabularies are the centerpiece of semantic applications. The book includes technological and architectural best practices for corporate use. The authors are experts from industry and academia with experience in developing semantic applications.
The exponential growth of digital information available in companies and on the Web creates the need for search tools that can respond to the most sophisticated information needs. Many user tasks would be simplified if Search Engines would support typed search, and return entities instead of just Web documents. For example, an executive who tries to solve a problem needs to find people in the company who are knowledgeable about a certain topic._x000D_ In the first part of the book, we propose a model for expert finding based on the well-consolidated vector space model for Information Retrieval and investigate its effectiveness. In the second part of the book, we investigate different methods based on Semantic Web and Natural Language Processing techniques for ranking entities of different types both in Wikipedia and, generally, on the Web. _x000D_ In the third part of this thesis, we study the problem of Entity Retrieval for news applications and the importance of the news trail history (i.e., past related articles) to determine the relevant entities in current articles. We also study opinion evolution about entities: We propose a method for automatically extracting the public opinion about political candidates from the blogosphere.
This book constitutes the refereed proceedings of the joint 6th International Semantic Web Conference, ISWC 2007, and the 2nd Asian Semantic Web Conference, ASWC 2007, held in Busan, Korea, in November 2007. The 50 revised full academic papers and 12 revised application papers presented together with 5 Semantic Web Challenge papers and 12 selected doctoral consortium articles were carefully reviewed and selected from a total of 257 submitted papers to the academic track and 29 to the applications track. The papers address all current issues in the field of the semantic Web, ranging from theoretical and foundational aspects to various applied topics such as management of semantic Web data, ontologies, semantic Web architecture, social semantic Web, as well as applications of the semantic Web. Short descriptions of the top five winning applications submitted to the Semantic Web Challenge competition conclude the volume.
During the last years, the amount of RDF data has increased exponentially over the Web, exposed via SPARQL endpoints. These SPARQL endpoints allow users to direct SPARQL queries to the RDF data. Federated SPARQL query processing allows to query several of these RDF databases as if they were a single one, integrating the results from all of them. This is a key concept in the Web of Data and it is also a hot topic in the community. Besides of that, the W3C SPARQL-WG has standardized it in the new Recommendation SPARQL 1.1._x000D_ This book provides a formalisation of the W3C proposed recommendation. This formalisation allows to identify existing errors and allows to correct them before the implementation phase or when the execution of these federated queries start. The book constitutes a valuable resource for any implementer since it also proposes solutions to the problems identified as well as proposing a set of SPARQL pattern reordering rules, which reduce the execution time of federated queries significantly._x000D_ Another strong point of this book is the research methodology followed. It states clearly the problems in the state of the art, next defines the research hypothesis for next providing a thoroughly analysis of the semantics of the SPARQL 1.1 specification. Once the theoretical part is concluded the book steps into the implementation part, describing clearly the implementation decisions for finally evaluating the overall system.
Over the last decade, ontology has become an important modeling component in software engineering. Semantic Web Enabled Software Engineering presents some critical findings on opening a new direction of the research of Software Engineering, by exploiting Semantic Web technologies. Most of these findings are from selected papers from the Semantic Web Enabled Software Engineering (SWESE) series of workshops starting from 2005. Edited by two leading researchers, this advanced text presents a unifying and contemporary perspective on the field. The book integrates in one volume a unified perspective on concepts and theories of connecting Software Engineering and Semantic Web. It presents state-of-the-art techniques on how to use Semantic Web technologies in Software Engineering and introduces techniques on how to design ontologies for Software Engineering.
The geosciences are one of the fields leading the way in advancing semantic technologies. This book continues the dialogue and feedback between the geoscience and semantic web communities. Increasing data volumes within the geosciences makes it no longer practical to copy data and perform local analysis. Hypotheses are now being tested through online tools that combine and mine pools of data. This evolution in the way research is conducted is commonly referred to as e-Science. As e-Science has flourished, the barriers to free and open access to data have been lowered and the need for semantics has been heighted. As the volume, complexity, and heterogeneity of data resources grow, geoscientists are creating new capabilities that rely on semantic approaches. Geoscience researchers are actively working toward a research environment of software tools and interfaces to data archives and services with the goals of full-scale semantic integration beginning to take shape. The members of this emerging semantic e-Science community are increasingly in need of semantic-based methodologies, tools and infrastructure. A feedback system between the geo- and computational sciences is forming. Advances in knowledge modeling, logic-based hypothesis checking, semantic data integration, and knowledge discovery are leading to advances in scientific domains, which in turn are validating semantic approaches and pointing to new research directions. We present mature semantic applications within the geosciences and stimulate discussion on emerging challenges and new research directions.
Mathematics is becoming increasingly collaborative, but software does not sufficiently support that: Social Web applications do not currently make mathematical knowledge accessible to automated agents that have a deeper understanding of mathematical structures. Such agents exist but focus on individual research tasks, such as authoring, publishing, peer-review, or verification, instead of complex collaboration workflows. This work effectively enables their integration by bridging the document-oriented perspective of mathematical authoring and publishing, and the network perspective of threaded discussions and Web information retrieval. This is achieved by giving existing representations of mathematical and relevant related knowledge about applications, projects and people a common Semantic Web foundation. Service integration is addressed from the two perspectives of enriching published documents by embedding assistive services, and translating between different knowledge representations inside knowledge bases. A usability evaluation of a semantic wiki that coherently integrates knowledge production and consumption services points out the remaining challenges in making such heterogeneously integrated environments support realistic workflows. The results of this thesis will soon also enable collaborative acquisition of new mathematical knowledge, as well as the contributions of existing knowledge collections of the Web of Data.
Linked Data publishing has brought about a novel “Web of Data”: a wealth of diverse, interlinked, structured data published on the Web. These Linked Datasets are described using the Semantic Web standards and are openly available to all, produced by governments, businesses, communities and academia alike. However, the heterogeneity of such data – in terms of how resources are described and identified – poses major challenges to potential consumers. Herein, we examine use cases for pragmatic, lightweight reasoning techniques that leverage Web vocabularies (described in RDFS and OWL) to better integrate large scale, diverse, Linked Data corpora. We take a test corpus of 1.1 billion RDF statements collected from 4 million RDF Web documents and analyse the use of RDFS and OWL therein. We then detail and evaluate scalable and distributed techniques for applying rule-based materialisation to translate data between different vocabularies, and to resolve coreferent resources that talk about the same thing. We show how such techniques can be made robust in the face of noisy and often impudent Web data. We also examine a use case for incorporating a PagerRank-style algorithm to rank the trustworthiness of facts produced by reasoning, subsequently using those ranks to fix formal contradictions in the data. All of our methods are validated against our real world, large scale, open domain, Linked Data evaluation corpus.
Perspectives on Ontology Learning brings together researchers and practitioners from different communities − natural language processing, machine learning, and the semantic web − in order to give an interdisciplinary overview of recent advances in ontology learning. Starting with a comprehensive introduction to the theoretical foundations of ontology learning methods, the edited volume presents the state-of-the-start in automated knowledge acquisition and maintenance. It outlines future challenges in this area with a special focus on technologies suitable for pushing the boundaries beyond the creation of simple taxonomical structures, as well as on problems specifically related to knowledge modeling and representation using the Web Ontology Language. Perspectives on Ontology Learning is designed for researchers in the field of semantic technologies and developers of knowledge-based applications. It covers various aspects of ontology learning including ontology quality, user interaction, scalability, knowledge acquisition from heterogeneous sources, as well as the integration with ontology engineering methodologies.