Search (445 results, page 1 of 23)

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Abstract

Why doesn't your home page appear on the first page of search results, even when you query your own name? How do other Web pages always appear at the top? What creates these powerful rankings? And how? The first book ever about the science of Web page rankings, "Google's PageRank and Beyond" supplies the answers to these and other questions and more. The book serves two very different audiences: the curious science reader and the technical computational reader. The chapters build in mathematical sophistication, so that the first five are accessible to the general academic reader. While other chapters are much more mathematical in nature, each one contains something for both audiences. For example, the authors include entertaining asides such as how search engines make money and how the Great Firewall of China influences research. The book includes an extensive background chapter designed to help readers learn more about the mathematics of search engines, and it contains several MATLAB codes and links to sample Web data sets. The philosophy throughout is to encourage readers to experiment with the ideas and algorithms in the text. Any business seriously interested in improving its rankings in the major search engines can benefit from the clear examples, sample code, and list of resources provided. It includes: many illustrative examples and entertaining asides; MATLAB code; accessible and informal style; and complete and self-contained section for mathematics review.

Content

Inhalt: Chapter 1. Introduction to Web Search Engines: 1.1 A Short History of Information Retrieval - 1.2 An Overview of Traditional Information Retrieval - 1.3 Web Information Retrieval Chapter 2. Crawling, Indexing, and Query Processing: 2.1 Crawling - 2.2 The Content Index - 2.3 Query Processing Chapter 3. Ranking Webpages by Popularity: 3.1 The Scene in 1998 - 3.2 Two Theses - 3.3 Query-Independence Chapter 4. The Mathematics of Google's PageRank: 4.1 The Original Summation Formula for PageRank - 4.2 Matrix Representation of the Summation Equations - 4.3 Problems with the Iterative Process - 4.4 A Little Markov Chain Theory - 4.5 Early Adjustments to the Basic Model - 4.6 Computation of the PageRank Vector - 4.7 Theorem and Proof for Spectrum of the Google Matrix Chapter 5. Parameters in the PageRank Model: 5.1 The a Factor - 5.2 The Hyperlink Matrix H - 5.3 The Teleportation Matrix E Chapter 6. The Sensitivity of PageRank; 6.1 Sensitivity with respect to alpha - 6.2 Sensitivity with respect to H - 6.3 Sensitivity with respect to vT - 6.4 Other Analyses of Sensitivity - 6.5 Sensitivity Theorems and Proofs Chapter 7. The PageRank Problem as a Linear System: 7.1 Properties of (I - alphaS) - 7.2 Properties of (I - alphaH) - 7.3 Proof of the PageRank Sparse Linear System Chapter 8. Issues in Large-Scale Implementation of PageRank: 8.1 Storage Issues - 8.2 Convergence Criterion - 8.3 Accuracy - 8.4 Dangling Nodes - 8.5 Back Button Modeling
Chapter 9. Accelerating the Computation of PageRank: 9.1 An Adaptive Power Method - 9.2 Extrapolation - 9.3 Aggregation - 9.4 Other Numerical Methods Chapter 10. Updating the PageRank Vector: 10.1 The Two Updating Problems and their History - 10.2 Restarting the Power Method - 10.3 Approximate Updating Using Approximate Aggregation - 10.4 Exact Aggregation - 10.5 Exact vs. Approximate Aggregation - 10.6 Updating with Iterative Aggregation - 10.7 Determining the Partition - 10.8 Conclusions Chapter 11. The HITS Method for Ranking Webpages: 11.1 The HITS Algorithm - 11.2 HITS Implementation - 11.3 HITS Convergence - 11.4 HITS Example - 11.5 Strengths and Weaknesses of HITS - 11.6 HITS's Relationship to Bibliometrics - 11.7 Query-Independent HITS - 11.8 Accelerating HITS - 11.9 HITS Sensitivity Chapter 12. Other Link Methods for Ranking Webpages: 12.1 SALSA - 12.2 Hybrid Ranking Methods - 12.3 Rankings based on Traffic Flow Chapter 13. The Future of Web Information Retrieval: 13.1 Spam - 13.2 Personalization - 13.3 Clustering - 13.4 Intelligent Agents - 13.5 Trends and Time-Sensitive Search - 13.6 Privacy and Censorship - 13.7 Library Classification Schemes - 13.8 Data Fusion Chapter 14. Resources for Web Information Retrieval: 14.1 Resources for Getting Started - 14.2 Resources for Serious Study Chapter 15. The Mathematics Guide: 15.1 Linear Algebra - 15.2 Perron-Frobenius Theory - 15.3 Markov Chains - 15.4 Perron Complementation - 15.5 Stochastic Complementation - 15.6 Censoring - 15.7 Aggregation - 15.8 Disaggregation

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Content

Enthält u.a. die Beiträge: Liu, S., F. Liu u. C. Yu u.a.: An effective approach to document retrieval via utilizing WordNet and recognizing phrases; Lau, R.Y.K., P.D. Bruza u. D. Song: Belief revision for adaptive information retrieval; Kokiopoulou, E., Y. Saad: Polynomial filtering in Latent semantic indexing for information retrieval; He, X., D. Cai u. H. Liu u.a.: Locality preserving indexing for document representation; Tang, C., S. Dwarkadas u. Z. Xu u.a.: On scaling Latent semantic indexing for large peer-to peer systems; Yu, W., Y. Gong: Document clustering by concept factorization; Kazai, G., M. Lalmas: The overlap problem in content-oriented XML retrieval evaluation; Kamps, J., M. de Rijke u. B. Sigurbjörnsson: Length normalization in XML retrieval; Liu, A., Q. Zou u. W.W. Chu: Configurable indexing and ranking for XML information retrieval; Zhang, L., Y. Pan u. T. Zhang: Focused named entity recognition using machine learning; Xu, J., R. Weischedel u. A. Licuanan: Evaluation of an extraction-based approach to answering definitional questions; Chieu, H.L., Y.K. Lee: Query based event extraction along a timeline; Yu, K., V. Tresp u. S. Yu: A nonparametric hierarchical Bayesian framework for information filtering; Liu, X., W.B. Croft: Cluster-based retrieval using language models; Silvestri, F., A. Orlando u. R. Perego: Assigning identifters to documents to enhance the clustering property of fulltext indexes; Amitay, E., D. Carmel u. R. Lempel u.a.: Scaling IR-system evaluation using Term Relevance Sets; Buckley, C., E.M. Voorhees: Retrieval evaluation with incomplete information; Cheng, P.J., J.W. Teng u. R.C. Chen u.a.: Translating unknown queries with Web corpora for cross-language information languages; Fan, J., Y. Gao u. H. Luo u.a.: Automatic image automation by using concept-sensitive salient objects for image content representation; Amitay, E., N. Har'El u. R. Sivian u.a.: Web-a-Where: geotagging web content; Shen, D., Z. Chen u. Q. Yang u.a.: Web page classification through summarization; McLaughlin, M.R., J.L. Herlocker: A collaborative filtering algorithm and evaluation metric that accurately model the user experience; Fan, W., M. Luo u. L. Wang u.a.: Tuning before feedback: combining ranking discovery and blind feedback for robust retrieval.

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Abstract

Recent technological progress in computer science, Web technologies, and constantly evolving information available on the Internet has drastically changed the landscape of search and access to information. Web search has significantly evolved in recent years. In the beginning, web search engines such as Google and Yahoo! were only providing search service over text documents. Aggregated search was one of the first steps to go beyond text search, and was the beginning of a new era for information seeking and retrieval. These days, new web search engines support aggregated search over a number of vertices, and blend different types of documents (e.g., images, videos) in their search results. New search engines employ advanced techniques involving machine learning, computational linguistics and psychology, user interaction and modeling, information visualization, Web engineering, artificial intelligence, distributed systems, social networks, statistical analysis, semantic analysis, and technologies over query sessions. Documents no longer exist on their own; they are connected to other documents, they are associated with users and their position in a social network, and they can be mapped onto a variety of ontologies. Similarly, retrieval tasks have become more interactive and are solidly embedded in a user's geospatial, social, and historical context. It is conjectured that new breakthroughs in information retrieval will not come from smarter algorithms that better exploit existing information sources, but from new retrieval algorithms that can intelligently use and combine new sources of contextual metadata.
With the rapid growth of web-based applications, such as search engines, Facebook, and Twitter, the development of effective and personalized information retrieval techniques and of user interfaces is essential. The amount of shared information and of social networks has also considerably grown, requiring metadata for new sources of information, like Wikipedia and ODP. These metadata have to provide classification information for a wide range of topics, as well as for social networking sites like Twitter, and Facebook, each of which provides additional preferences, tagging information and social contexts. Due to the explosion of social networks and other metadata sources, it is an opportune time to identify ways to exploit such metadata in IR tasks such as user modeling, query understanding, and personalization, to name a few. Although the use of traditional metadata such as html text, web page titles, and anchor text is fairly well-understood, the use of category information, user behavior data, and geographical information is just beginning to be studied. This book is intended for scientists and decision-makers who wish to gain working knowledge about search engines in order to evaluate available solutions and to dialogue with software and data providers.

Content

Vert, S.: Extensions of Web browsers useful to knowledge workers. Chen, L.-C.: Next generation search engine for the result clustering technology. Biskri, I., L. Rompré: Using association rules for query reformulation. Habernal, I., M. Konopík u. O. Rohlík: Question answering. Grau, B.: Finding answers to questions, in text collections or Web, in open domain or specialty domains. Berri, J., R. Benlamri: Context-aware mobile search engine. Bouidghaghen, O., L. Tamine: Spatio-temporal based personalization for mobile search. Chaudiron, S., M. Ihadjadene: Studying Web search engines from a user perspective: key concepts and main approaches. Karaman, F.: Artificial intelligence enabled search engines (AIESE) and the implications. Lewandowski, D.: A framework for evaluating the retrieval effectiveness of search engines.

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Abstract

Diese Arbeit untersucht, wie sich eine Forschungskontext-Ontologie als Quelle für die Generierung von Query-Expansion-Termen in einem Retrievalsystem für die Domäne der Erziehungswissenschaft nutzen lässt. Durch die Kombination traditioneller, groß angelegter automatischer Retrievalexperimente und nutzerzentrierter interaktiver Retievalexperimente wird ein umfassendes Bild der Effekte ontologiebasierter Query Expansion gezeichnet. Während die automatischen Experimente die Expansionseffekte einzelner Arten ontologiebasierter Expansionsterme im Detail untersuchen, beleuchten die interaktiven Experimente, wie ontologiebasierte Expansionsmechanismen das Suchverhalten der Nutzer sowie ihren Sucherfolg beeinflussen.

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Abstract

Mit der Suchmaschine Google, den Satelitenkarten auf Google Maps, mit der Volltextsuche in Google Books, mit dem Bildprogramm Picasa, dem Mail-Programm Google Mail, Google Docs und vielen weiteren Services entwickelt sich die Firma von Larry Page und Sergej Brin zur unkontrollierten Weltmacht im Internet: Immer mehr Firmen entwickeln Dienste und Geräte, die ausschließlich auf Google abgestimmt sind. Praktisch zählt nur mehr das Wissen, das man über Google findet. Diese Monopolstellung ist für eine Wissensgesellschaft gefährlich. Sie erleichtert die politische Zensur, wie sie bei Google in China stattfindet, und sie erleichtert das Ausspionieren unserer Privatsphäre. Denn jede Suchanfrage bleibt bei Google über Jahre gespeichert: In den USA wird derzeit eine Frau des Mordes an ihrem Mann verdächtigt, weil sie einmal in Google den Suchbegriff "murder" eingetippt hat.

Date

7. 4.2008 21:22:57

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Date

22. 3.2008 14:35:21

Series

Edition PAGE

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Abstract

Describes a deductive data model based on 3 abstraction levels for representing vocabularies for information retrieval: conceptual level; expression level; and occurrence level. The proposed data model can be used for the representation and navigation of indexing and retrieval thesauri and as a vocabulary source for concept based query expansion in heterogeneous retrieval environments

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Abstract

This book constitutes the thoroughly refereed post-conference proceedings of the First COST Action IC1302 International KEYSTONE Conference on semantic Keyword-based Search on Structured Data Sources, IKC 2015, held in Coimbra, Portugal, in September 2015. The 13 revised full papers, 3 revised short papers, and 2 invited papers were carefully reviewed and selected from 22 initial submissions. The paper topics cover techniques for keyword search, semantic data management, social Web and social media, information retrieval, benchmarking for search on big data.

Content

Inhalt: Professional Collaborative Information Seeking: On Traceability and Creative Sensemaking / Nürnberger, Andreas (et al.) - Recommending Web Pages Using Item-Based Collaborative Filtering Approaches / Cadegnani, Sara (et al.) - Processing Keyword Queries Under Access Limitations / Calì, Andrea (et al.) - Balanced Large Scale Knowledge Matching Using LSH Forest / Cochez, Michael (et al.) - Improving css-KNN Classification Performance by Shifts in Training Data / Draszawka, Karol (et al.) - Classification Using Various Machine Learning Methods and Combinations of Key-Phrases and Visual Features / HaCohen-Kerner, Yaakov (et al.) - Mining Workflow Repositories for Improving Fragments Reuse / Harmassi, Mariem (et al.) - AgileDBLP: A Search-Based Mobile Application for Structured Digital Libraries / Ifrim, Claudia (et al.) - Support of Part-Whole Relations in Query Answering / Kozikowski, Piotr (et al.) - Key-Phrases as Means to Estimate Birth and Death Years of Jewish Text Authors / Mughaz, Dror (et al.) - Visualization of Uncertainty in Tag Clouds / Platis, Nikos (et al.) - Multimodal Image Retrieval Based on Keywords and Low-Level Image Features / Pobar, Miran (et al.) - Toward Optimized Multimodal Concept Indexing / Rekabsaz, Navid (et al.) - Semantic URL Analytics to Support Efficient Annotation of Large Scale Web Archives / Souza, Tarcisio (et al.) - Indexing of Textual Databases Based on Lexical Resources: A Case Study for Serbian / Stankovic, Ranka (et al.) - Domain-Specific Modeling: Towards a Food and Drink Gazetteer / Tagarev, Andrey (et al.) - Analysing Entity Context in Multilingual Wikipedia to Support Entity-Centric Retrieval Applications / Zhou, Yiwei (et al.)

Date

1. 2.2016 18:25:22

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Abstract

Documents usually have a content and a structure. The content refers to the text of the document, whereas the structure refers to how a document is logically organized. An increasingly common way to encode the structure is through the use of a mark-up language. Nowadays, the most widely used mark-up language for representing structure is the eXtensible Mark-up Language (XML). XML can be used to provide a focused access to documents, i.e. returning XML elements, such as sections and paragraphs, instead of whole documents in response to a query. Such focused strategies are of particular benefit for information repositories containing long documents, or documents covering a wide variety of topics, where users are directed to the most relevant content within a document. The increased adoption of XML to represent a document structure requires the development of tools to effectively access documents marked-up in XML. This book provides a detailed description of query languages, indexing strategies, ranking algorithms, presentation scenarios developed to access XML documents. Major advances in XML retrieval were seen from 2002 as a result of INEX, the Initiative for Evaluation of XML Retrieval. INEX, also described in this book, provided test sets for evaluating XML retrieval effectiveness. Many of the developments and results described in this book were investigated within INEX.

Content

Table of Contents: Introduction / Basic XML Concepts / Historical Perspectives / Query Languages / Indexing Strategies / Ranking Strategies / Presentation Strategies / Evaluating XML Retrieval Effectiveness / Conclusions

LCSH

Query languages (Computer science)

Subject

Query languages (Computer science)

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Abstract

With the advance of the semantic Web, increasing amounts of data are available in a structured and machine-understandable form. This opens opportunities for users to employ semantic queries instead of simple keyword-based ones to accurately express the information need. However, constructing semantic queries is a demanding task for human users [11]. To compose a valid semantic query, a user has to (1) master a query language (e.g., SPARQL) and (2) acquire sufficient knowledge about the ontology or the schema of the data source. While there are systems which support this task with visual tools [21, 26] or natural language interfaces [3, 13, 14, 18], the process of query construction can still be complex and time consuming. According to [24], users prefer keyword search, and struggle with the construction of semantic queries although being supported with a natural language interface. Several keyword search approaches have already been proposed to ease information seeking on semantic data [16, 32, 35] or databases [1, 31]. However, keyword queries lack the expressivity to precisely describe the user's intent. As a result, ranking can at best put query intentions of the majority on top, making it impossible to take the intentions of all users into consideration.

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Abstract

Social phenomena happen, and the historians follow. So it goes with Google, the latest star shooting through the universe of trend-setting businesses. This company has even entered our popular lexicon: as many note, "Google" has moved beyond noun to verb, becoming an action which most tech-savvy citizens at the turn of the twenty-first century recognize and in fact do, on a daily basis. It's this wide societal impact that fascinated authors David Vise and Mark Malseed, who came to the book with well-established reputations in investigative reporting. Vise authored the bestselling The Bureau and the Mole, and Malseed contributed significantly to two Bob Woodward books, Bush at War and Plan of Attack. The kind of voluminous research and behind-the-scenes insight in which both writers specialize, and on which their earlier books rested, comes through in The Google Story. The strength of the book comes from its command of many small details, and its focus on the human side of the Google story, as opposed to the merely academic one. Some may prefer a dryer, more analytic approach to Google's impact on the Internet, like The Search or books that tilt more heavily towards bits and bytes on the spectrum between technology and business, like The Singularity is Near. Those wanting to understand the motivations and personal growth of founders Larry Page and Sergey Brin and CEO Eric Schmidt, however, will enjoy this book. Vise and Malseed interviewed over 150 people, including numerous Google employees, Wall Street analysts, Stanford professors, venture capitalists, even Larry Page's Cub Scout leader, and their comprehensiveness shows. As the narrative unfolds, readers learn how Google grew out of the intellectually fertile and not particularly directed friendship between Page and Brin; how the founders attempted to peddle early versions of their search technology to different Silicon Valley firms for $1 million; how Larry and Sergey celebrated their first investor's check with breakfast at Burger King; how the pair initially housed their company in a Palo Alto office, then eventually moved to a futuristic campus dubbed the "Googleplex"; how the company found its financial footing through keyword-targeted Web ads; how various products like Google News, Froogle, and others were cooked up by an inventive staff; how Brin and Page proved their mettle as tough businessmen through negotiations with AOL Europe and their controversial IPO process, among other instances; and how the company's vision for itself continues to grow, such as geographic expansion to China and cooperation with Craig Venter on the Human Genome Project. Like the company it profiles, The Google Story is a bit of a wild ride, and fun, too. Its first appendix lists 23 "tips" which readers can use to get more utility out of Google. The second contains the intelligence test which Google Research offers to prospective job applicants, and shows the sometimes zany methods of this most unusual business. Through it all, Vise and Malseed synthesize a variety of fascinating anecdotes and speculation about Google, and readers seeking a first draft of the history of the company will enjoy an easy read.

Date

3. 5.1997 8:44:22

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Abstract

Wenn Larry Page und Sergey Brin irgendwo auf der Welt auf eine Bühne klettern, ist Ausnahmezustand. Längst sind die Erfinder von Google, die erfolgreichste Suchmaschine im Internet, Popstars geworden, scheinbar unzerstörbare späte Kinder der New Economy, deren Geist sie mit dem grandiosen Siegeszug ihres Produkts retteten über den Niedergang der zahllosen Internet- und Start-up-Träume, die zu Beginn des Jahrtausends zerplatzten. Eigentlich wollten die beiden netten Jungs nur eine etwas funktionstüchtigere Suchmaschine für ihre Universität basteln. Doch was 1997 in Stanford Studenten, Lehrern und Verwaltungsangestellten unter Google zur Verfügung stand (Google, eigentlich Googol, ist der mathematische Begriff für eine Riesenzahl, Brin und Page hatten ihn versehentlich falsch geschrieben), wuchs nach der Firmenausgründung in kürzester Zeit in den virtuellen Himmel. Mitte 2000 bereits erledigte Google 15 Millionen Suchanfragen pro Tag, nur 10.000 waren es noch Mitte 1998 gewesen. Anfang 2001 kletterte die Zahl auf 100 Millionen Suchanfragen pro Tag, 10.000 pro Sekunde. Nach dem Börsengang im August 2004 wurde Google milliardenschwer. Im Sommer 2005 besaß jeder der Firmengründer ein Reinvermögen von mehr als zehn Milliarden Dollar. Heute ist "googeln" als Verb im Duden aufgenommen, ebenso wie sich "to google" im angelsächsischen Sprachraum etabliert hat. In der Google-Story zeichnen der Pulitzer-Preisträger David A. Vise und Mark Malseed den ebenso spannenden wie wechselhaften Aufstieg der Google-Erfinder nach. Eine wahre Heldenstory -- und an manchen Stellen zu dick aufgetragen, insbesondere dann, wenn der Suchmaschine "menschliche Eigenschaften" zugesprochen werden. Aber es ist durchaus spannend zu verfolgen, wie zwei amerikanische Jungs innerhalb von nur fünf Jahren mit einem kalifornischen Studentenprojekt die globale Informationskultur umzustülpen vermochten. Zumal Vise und Malseed die Schwierigkeiten des Unternehmens mit Datenschutz und Copyright, mit Wettbewerbern und Konkurrenten nicht verschweigen. Und wer dazu noch die 23 Google-Suchtipps mitstudiert, wird nach der Lektüre noch leichter durchs Internet navigieren.

Date

3. 5.1997 8:44:22

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Content

Inhalt: A. Grundsätzliche Fragen (aus dem Umfeld) der Wissensorganisation Markus Gottwald, Matthias Klemm und Jan Weyand: Warum ist es schwierig, Wissen zu managen? Ein soziologischer Deutungsversuch anhand eines Wissensmanagementprojekts in einem Großunternehmen H. Peter Ohly: Wissenskommunikation und -organisation. Quo vadis? Helmut F. Spinner: Wissenspartizipation und Wissenschaftskommunikation in drei Wissensräumen: Entwurf einer integrierten Theorie B. Dokumentationssprachen in der Anwendung Felix Boteram: Semantische Relationen in Dokumentationssprachen vom Thesaurus zum semantischen Netz Jessica Hubrich: Multilinguale Wissensorganisation im Zeitalter der Globalisierung: das Projekt CrissCross Vivien Petras: Heterogenitätsbehandlung und Terminology Mapping durch Crosskonkordanzen - eine Fallstudie Manfred Hauer, Uwe Leissing und Karl Rädler: Query-Expansion durch Fachthesauri Erfahrungsbericht zu dandelon.com, Vorarlberger Parlamentsinformationssystem und vorarlberg.at

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Series

Edition Page

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Footnote

The next chapter introduces resource description framework (RDF) and RDF schema (RDFS). Unlike XML, RDF provides a foundation for expressing the semantics of dada: it is a standard dada model for machine-processable semantics. Resource description framework schema offers a number of modeling primitives for organizing RDF vocabularies in typed hierarchies. In addition to RDF and RDFS, a query language for RDF, i.e. RQL. is introduced. This chapter and the next chapter are two of the most important chapters in the book. Chapter 4 presents another language called Web Ontology Language (OWL). Because RDFS is quite primitive as a modeling language for the Web, more powerful languages are needed. A richer language. DAML+OIL, is thus proposed as a joint endeavor of the United States and Europe. OWL takes DAML+OIL as the starting point, and aims to be the standardized and broadly accepted ontology language. At the beginning of the chapter, the nontrivial relation with RDF/RDFS is discussed. Then the authors describe the various language elements of OWL in some detail. Moreover, Appendix A contains an abstract OWL syntax. which compresses OWL and makes OWL much easier to read. Chapter 5 covers both monotonic and nonmonotonic rules. Whereas the previous chapter's mainly concentrate on specializations of knowledge representation, this chapter depicts the foundation of knowledge representation and inference. Two examples are also givwn to explain monotonic and non-monotonic rules, respectively. "To get the most out of the chapter. readers had better gain a thorough understanding of predicate logic first. Chapter 6 presents several realistic application scenarios to which the Semantic Web technology can be applied. including horizontal information products at Elsevier, data integration at Audi, skill finding at Swiss Life, a think tank portal at EnerSearch, e-learning. Web services, multimedia collection indexing, online procurement, raid device interoperability. These case studies give us some real feelings about the Semantic Web.
The chapter on ontology engineering describes the development of ontology-based systems for the Web using manual and semiautomatic methods. Ontology is a concept similar to taxonomy. As stated in the introduction, ontology engineering deals with some of the methodological issues that arise when building ontologies, in particular, con-structing ontologies manually, reusing existing ontologies. and using semiautomatic methods. A medium-scale project is included at the end of the chapter. Overall the book is a nice introduction to the key components of the Semantic Web. The reading is quite pleasant, in part due to the concise layout that allows just enough content per page to facilitate readers' comprehension. Furthermore, the book provides a large number of examples, code snippets, exercises, and annotated online materials. Thus, it is very suitable for use as a textbook for undergraduates and low-grade graduates, as the authors say in the preface. However, I believe that not only students but also professionals in both academia and iudustry will benefit from the book. The authors also built an accompanying Web site for the book at http://www.semanticwebprimer.org. On the main page, there are eight tabs for each of the eight chapters. For each tabm the following sections are included: overview, example, presentations, problems and quizzes, errata, and links. These contents will greatly facilitate readers: for example, readers can open the listed links to further their readings. The vacancy of the errata sections also proves the quality of the book."

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Abstract

The birth of the Web has brought an exponential growth to the amount of the information that is freely available to the Internet population, overloading users and entangling their efforts to satisfy their information needs. Web search engines such Google, Yahoo, or Bing have become popular mainly due to the fact that they offer an easy-to-use query interface (i.e., based on keywords) and an effective and efficient query execution mechanism. The majority of these search engines do not consider information stored on the deep or hidden Web [9,28], despite the fact that the size of the deep Web is estimated to be much bigger than the surface Web [9,47]. There have been a number of systems that record interactions with the deep Web sources or automatically submit queries them (mainly through their Web form interfaces) in order to index their context. Unfortunately, this technique is only partially indexing the data instance. Moreover, it is not possible to take advantage of the query capabilities of data sources, for example, of the relational query features, because their interface is often restricted from the Web form. Besides, Web search engines focus on retrieving documents and not on querying structured sources, so they are unable to access information based on concepts.

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Abstract

The Entity-Relationship (ER) model is a fundamental formalism for conceptual modeling in database design; it was introduced by Chen in his milestone paper, and it is now widely used, being flexible and easily understood by practitioners. With the rise of the Semantic Web, conceptual modeling formalisms have gained importance again as ontology formalisms, in the Semantic Web parlance. Ontologies and conceptual models are aimed at representing, rather than the structure of data, the domain of interest, that is, the fragment of the real world that is being represented by the data and the schema. A prominent formalism for modeling ontologies are Description Logics (DLs), which are decidable fragments of first-order logic, particularly suitable for ontological modeling and querying. In particular, DL ontologies are sets of assertions describing sets of objects and (usually binary) relations among such sets, exactly in the same fashion as the ER model. Recently, research on DLs has been focusing on the problem of answering queries under ontologies, that is, given a query q, an instance B, and an ontology X, answering q under B and amounts to compute the answers that are logically entailed from B by using the assertions of X. In this context, where data size is usually large, a central issue the data complexity of query answering, i.e., the computational complexity with respect to the data set B only, while the ontology X and the query q are fixed.

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Abstract

The Web has become the world's largest database, with search being the main tool that allows organizations and individuals to exploit its huge amount of information. Search on the Web has been traditionally based on textual and structural similarities, ignoring to a large degree the semantic dimension, i.e., understanding the meaning of the query and of the document content. Combining search and semantics gives birth to the idea of semantic search. Traditional search engines have already advertised some semantic dimensions. Some of them, for instance, can enhance their generated result sets with documents that are semantically related to the query terms even though they may not include these terms. Nevertheless, the exploitation of the semantic search has not yet reached its full potential. In this book, Roberto De Virgilio, Francesco Guerra and Yannis Velegrakis present an extensive overview of the work done in Semantic Search and other related areas. They explore different technologies and solutions in depth, making their collection a valuable and stimulating reading for both academic and industrial researchers. The book is divided into three parts. The first introduces the readers to the basic notions of the Web of Data. It describes the different kinds of data that exist, their topology, and their storing and indexing techniques. The second part is dedicated to Web Search. It presents different types of search, like the exploratory or the path-oriented, alongside methods for their efficient and effective implementation. Other related topics included in this part are the use of uncertainty in query answering, the exploitation of ontologies, and the use of semantics in mashup design and operation. The focus of the third part is on linked data, and more specifically, on applying ideas originating in recommender systems on linked data management, and on techniques for the efficiently querying answering on linked data.

Content

Inhalt: Introduction.- Part I Introduction to Web of Data.- Topology of the Web of Data.- Storing and Indexing Massive RDF Data Sets.- Designing Exploratory Search Applications upon Web Data Sources.- Part II Search over the Web.- Path-oriented Keyword Search query over RDF.- Interactive Query Construction for Keyword Search on the SemanticWeb.- Understanding the Semantics of Keyword Queries on Relational DataWithout Accessing the Instance.- Keyword-Based Search over Semantic Data.- Semantic Link Discovery over Relational Data.- Embracing Uncertainty in Entity Linking.- The Return of the Entity-Relationship Model: Ontological Query Answering.- Linked Data Services and Semantics-enabled Mashup.- Part III Linked Data Search engines.- A Recommender System for Linked Data.- Flint: from Web Pages to Probabilistic Semantic Data.- Searching and Browsing Linked Data with SWSE.

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Content

Inhalt: Acknowledgments; Abstract; Zusammenfassung; Contents; List of Figures; List of Tables; List of Acronyms; Chapter 1 Introduction ; 1.1 Research Questions; 1.2 Thesis Outline; Part I Fundamentals ; Chapter 2 Information Retrieval for Young Users ; 2.1 Basics of Information Retrieval; 2.1.1 Architecture of an IR System; 2.1.2 Relevance Ranking; 2.1.3 Search User Interfaces; 2.1.4 Targeted Search Engines; 2.2 Aspects of Child Development Relevant for Information Retrieval Tasks; 2.2.1 Human Cognitive Development; 2.2.2 Information Processing Theory; 2.2.3 Psychosocial Development 2.3 User Studies and Evaluation2.3.1 Methods in User Studies; 2.3.2 Types of Evaluation; 2.3.3 Evaluation with Children; 2.4 Discussion; Chapter 3 State of the Art ; 3.1 Children's Information-Seeking Behaviour; 3.1.1 Querying Behaviour; 3.1.2 Search Strategy; 3.1.3 Navigation Style; 3.1.4 User Interface; 3.1.5 Relevance Judgement; 3.2 Existing Algorithms and User Interface Concepts for Children; 3.2.1 Query; 3.2.2 Content; 3.2.3 Ranking; 3.2.4 Search Result Visualisation; 3.3 Existing Information Retrieval Systems for Children; 3.3.1 Digital Book Libraries; 3.3.2 Web Search Engines 3.4 Summary and DiscussionPart II Studying Open Issues ; Chapter 4 Usability of Existing Search Engines for Young Users ; 4.1 Assessment Criteria; 4.1.1 Criteria for Matching the Motor Skills; 4.1.2 Criteria for Matching the Cognitive Skills; 4.2 Results; 4.2.1 Conformance with Motor Skills; 4.2.2 Conformance with the Cognitive Skills; 4.2.3 Presentation of Search Results; 4.2.4 Browsing versus Searching; 4.2.5 Navigational Style; 4.3 Summary and Discussion; Chapter 5 Large-scale Analysis of Children's Queries and Search Interactions; 5.1 Dataset; 5.2 Results; 5.3 Summary and Discussion Chapter 6 Differences in Usability and Perception of Targeted Web Search Engines between Children and Adults 6.1 Related Work; 6.2 User Study; 6.3 Study Results; 6.4 Summary and Discussion; Part III Tackling the Challenges ; Chapter 7 Search User Interface Design for Children ; 7.1 Conceptual Challenges and Possible Solutions; 7.2 Knowledge Journey Design; 7.3 Evaluation; 7.3.1 Study Design; 7.3.2 Study Results; 7.4 Voice-Controlled Search: Initial Study; 7.4.1 User Study; 7.5 Summary and Discussion; Chapter 8 Addressing User Diversity ; 8.1 Evolving Search User Interface 8.1.1 Mapping Function8.1.2 Evolving Skills; 8.1.3 Detection of User Abilities; 8.1.4 Design Concepts; 8.2 Adaptation of a Search User Interface towards User Needs; 8.2.1 Design & Implementation; 8.2.2 Search Input; 8.2.3 Result Output; 8.2.4 General Properties; 8.2.5 Configuration and Further Details; 8.3 Evaluation; 8.3.1 Study Design; 8.3.2 Study Results; 8.3.3 Preferred UI Settings; 8.3.4 User satisfaction; 8.4 Knowledge Journey Exhibit; 8.4.1 Hardware; 8.4.2 Frontend; 8.4.3 Backend; 8.5 Summary and Discussion; Chapter 9 Supporting Visual Searchers in Processing Search Results 9.1 Related Work

Date

1. 2.2016 18:25:22