Search (1674 results, page 1 of 84)

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Source

http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&ved=0CDQQFjAE&url=http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F3131107&ei=HzFWVYvGMsiNsgGTyoFI&usg=AFQjCNE2FHUeR9oQTQlNC4TPedv4Mo3DaQ&sig2=Rlzpr7a3BLZZkqZCXXN_IA&bvm=bv.93564037,d.bGg&cad=rja

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Abstract

In this thesis we propose three new word association measures for multi-word term extraction. We combine these association measures with LocalMaxs algorithm in our extraction model and compare the results of different multi-word term extraction methods. Our approach is language and domain independent and requires no training data. It can be applied to such tasks as text summarization, information retrieval, and document classification. We further explore the potential of using multi-word terms as an effective representation for general web-page summarization. We extract multi-word terms from human written summaries in a large collection of web-pages, and generate the summaries by aligning document words with these multi-word terms. Our system applies machine translation technology to learn the aligning process from a training set and focuses on selecting high quality multi-word terms from human written summaries to generate suitable results for web-page summarization.

Content

A Thesis presented to The University of Guelph In partial fulfilment of requirements for the degree of Master of Science in Computer Science. Vgl. Unter: http://www.inf.ufrgs.br%2F~ceramisch%2Fdownload_files%2Fpublications%2F2009%2Fp01.pdf.

Date

10. 1.2013 19:22:47

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Abstract

While classifications are heavily used to categorize web content, the evolution of the web foresees a more formal structure - ontology - which can serve this purpose. Ontologies are core artifacts of the Semantic Web which enable machines to use inference rules to conduct automated reasoning on data. Lightweight ontologies bridge the gap between classifications and ontologies. A lightweight ontology (LO) is an ontology representing a backbone taxonomy where the concept of the child node is more specific than the concept of the parent node. Formal lightweight ontologies can be generated from their informal ones. The key applications of formal lightweight ontologies are document classification, semantic search, and data integration. However, these applications suffer from the following problems: the disambiguation accuracy of the state of the art NLP tools used in generating formal lightweight ontologies from their informal ones; the lack of background knowledge needed for the formal lightweight ontologies; and the limitation of ontology reuse. In this dissertation, we propose a novel solution to these problems in formal lightweight ontologies; namely, faceted lightweight ontology (FLO). FLO is a lightweight ontology in which terms, present in each node label, and their concepts, are available in the background knowledge (BK), which is organized as a set of facets. A facet can be defined as a distinctive property of the groups of concepts that can help in differentiating one group from another. Background knowledge can be defined as a subset of a knowledge base, such as WordNet, and often represents a specific domain.

Content

PhD Dissertation at International Doctorate School in Information and Communication Technology. Vgl.: https%3A%2F%2Fcore.ac.uk%2Fdownload%2Fpdf%2F150083013.pdf&usg=AOvVaw2n-qisNagpyT0lli_6QbAQ.

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Content

Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.

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Content

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.

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Abstract

The realm of the digital offers both new methods of research and new objects of study. Because the digital environment for scholarship is constantly evolving, researchers must sometimes improvise, change their plans, and adapt. These details are often left out of research write-ups, leaving newcomers to the field frustrated when their approaches do not work as expected. Digital Research Confidential offers scholars a chance to learn from their fellow researchers' mistakes -- and their successes. The book -- a follow-up to Eszter Hargittai's widely read Research Confidential -- presents behind-the-scenes, nuts-and-bolts stories of digital research projects, written by established and rising scholars. They discuss such challenges as archiving, Web crawling, crowdsourcing, and confidentiality. They do not shrink from specifics, describing such research hiccups as an ethnographic interview so emotionally draining that afterward the researcher retreated to a bathroom to cry, and the seemingly simple research question about Wikipedia that mushroomed into years of work on millions of data points. Digital Research Confidential will be an essential resource for scholars in every field.

BK

05.20 Kommunikation und Gesellschaft

Classification

05.20 Kommunikation und Gesellschaft

Content

Preface How to think about digital research / Christian Sandvig and Eszter Hargittai -- "How local is user-generated content" : a 9,000+ word essay on answering a five-word research question" : or how we learned to stop worrying (or worry less) and love the diverse challenges of our fast-moving, geographically-flavored interdisciplinary research area / Darren Gergle and Brent Hecht -- Flash mobs and the social life of public spaces : analyzing online visual data to study new forms of sociability / Virag Molnar and Aron Hsiao -- Social software as social science / Eric Gilbert and Karrie Karahalios -- Hired hands and dubious guesses : adventures in crowdsourced data collection / Aaron Shaw -- Making sense of teen life : strategies for capturing ethnographic data in a networked era / Danah Boyd -- When should we use real names in published accounts of internet research? / Amy Bruckman, Kurt Luther, and Casey Fiesler -- The art of web crawling for social science research / Michelle Shumate and Matthew Weber -- The ethnographic study of visual culture in the age of digitization / Paul Leonardi -- Read/write the digital archive: strategies for historical web research / Megan Sapnar Ankerson -- Big data, big problems, big opportunities : using internet log data to conduct social network analysis research / Brooke Foucault Welles -- Contributors -- References -- Index.

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Abstract

Modern search engines have been moving away from simplistic interfaces that aimed at satisfying a user's need with a single-shot query. Interactive features are now integral parts of web search engines. However, generating good query modification suggestions remains a challenging issue. Query log analysis is one of the major strands of work in this direction. Although much research has been performed on query logs collected on the web as a whole, query log analysis to enhance search on smaller and more focused collections has attracted less attention, despite its increasing practical importance. In this article, we report on a systematic study of different query modification methods applied to a substantial query log collected on a local website that already uses an interactive search engine. We conducted experiments in which we asked users to assess the relevance of potential query modification suggestions that have been constructed using a range of log analysis methods and different baseline approaches. The experimental results demonstrate the usefulness of log analysis to extract query modification suggestions. Furthermore, our experiments demonstrate that a more fine-grained approach than grouping search requests into sessions allows for extraction of better refinement terms from query log files.

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Abstract

Feature-rich data, such as audio-video recordings, digital images, and results of scientific experiments, nowadays constitute the largest fraction of the massive data sets produced daily in the e-society. Content-based similarity search systems working on such data collections are rapidly growing in importance. Unfortunately, similarity search is in general very expensive and hardly scalable. In this paper we study the case of content-based image retrieval (CBIR) systems, and focus on the problem of increasing the throughput of a large-scale CBIR system that indexes a very large collection of digital images. By analyzing the query log of a real CBIR system available on the Web, we characterize the behavior of users who experience a novel search paradigm, where content-based similarity queries and text-based ones can easily be interleaved. We show that locality and self-similarity is present even in the stream of queries submitted to such a CBIR system. According to these results, we propose an effective way to exploit this locality, by means of a similarity caching system, which stores the results of recently/frequently submitted queries and associated results. Unlike traditional caching, the proposed cache can manage not only exact hits, but also approximate ones that are solved by similarity with respect to the result sets of past queries present in the cache. We evaluate extensively the proposed solution by using the real query stream recorded in the log and a collection of 100 millions of digital photographs. The high hit ratios and small average approximation error figures obtained demonstrate the effectiveness of the approach.

Date

27. 1.2016 18:30:29

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Abstract

The behavior of modern web robots varies widely when they crawl for different purposes. In this article, we present a framework to classify these web robots from two orthogonal perspectives, namely, their functionality and the types of resources they consume. Applying the classification framework to a year-long access log from the UConn SoE web server, we present trends that point to significant differences in their crawling behavior.

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Abstract

Purpose Provenance information is crucial for consistent maintenance of metadata schemas over time. The purpose of this paper is to propose a provenance model named DSP-PROV to keep track of structural changes of metadata schemas. Design/methodology/approach The DSP-PROV model is developed through applying the general provenance description standard PROV of the World Wide Web Consortium to the Dublin Core Application Profile. Metadata Application Profile of Digital Public Library of America is selected as a case study to apply the DSP-PROV model. Finally, this paper evaluates the proposed model by comparison between formal provenance description in DSP-PROV and semi-formal change log description in English. Findings Formal provenance description in the DSP-PROV model has advantages over semi-formal provenance description in English to keep metadata schemas consistent over time. Research limitations/implications The DSP-PROV model is applicable to keep track of the structural changes of metadata schema over time. Provenance description of other features of metadata schema such as vocabulary and encoding syntax are not covered. Originality/value This study proposes a simple model for provenance description of structural features of metadata schemas based on a few standards widely accepted on the Web and shows the advantage of the proposed model to conventional semi-formal provenance description.

Date

15. 1.2018 19:13:29

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Abstract

Process mining is an emerging area that synergically combines model-based and data-oriented analysis techniques to obtain useful insights on how business processes are executed within an organization. Through process mining, decision makers can discover process models from data, compare expected and actual behaviors, and enrich models with key information about their actual execution. To be applicable, process mining techniques require the input data to be explicitly structured in the form of an event log, which lists when and by whom different case objects (i.e., process instances) have been subject to the execution of tasks. Unfortunately, in many real world set-ups, such event logs are not explicitly given, but are instead implicitly represented in legacy information systems. To apply process mining in this widespread setting, there is a pressing need for techniques able to support various process stakeholders in data preparation and log extraction from legacy information systems. The purpose of this paper is to single out this challenging, open issue, and didactically introduce how techniques from intelligent data management, and in particular ontology-based data access, provide a viable solution with a solid theoretical basis.

Series

Lecture Notes in Computer Scienc;10370) (Information Systems and Applications, incl. Internet/Web, and HCI

Source

Reasoning Web: Semantic Interoperability on the Web, 13th International Summer School 2017, London, UK, July 7-11, 2017, Tutorial Lectures. Eds.: Ianni, G. et al

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Abstract

In this paper we present results from an investigation of religious information searching based on analyzing log files from a large general-purpose search engine. From approximately 15 million queries, we identified 124,422 that were part of 60,759 user sessions. We present a method for categorizing queries based on related terms and show differences in search patterns between religious searches and web searching more generally. We also investigate the search patterns found in queries related to 5 religions: Christianity, Hinduism, Islam, Buddhism, and Judaism. Different search patterns are found to emerge. Results from this study complement existing studies of religious information searching and provide a level of detailed analysis not reported to date. We show, for example, that sessions involving religion-related queries tend to last longer, that the lengths of religion-related queries are greater, and that the number of unique URLs clicked is higher when compared to all queries. The results of the study can serve to provide information on what this large population of users is actually searching for.

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Abstract

"Web Search Engine Research", edited by Dirk Lewandowski, provides an understanding of Web search engines from the unique perspective of Library and Information Science. The book explores a range of topics including retrieval effectiveness, user satisfaction, the evaluation of search interfaces, the impact of search on society, reliability of search results, query log analysis, user guidance in the search process, and the influence of search engine optimization (SEO) on results quality. While research in computer science has mainly focused on technical aspects of search engines, LIS research is centred on users' behaviour when using search engines and how this interaction can be evaluated. LIS research provides a unique perspective in intermediating between the technical aspects, user aspects and their impact on their role in knowledge acquisition. This book is directly relevant to researchers and practitioners in library and information science, computer science, including Web researchers.

LCSH

Web search engines

Subject

Web search engines

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Abstract

This volume contains the lecture notes of the 13th Reasoning Web Summer School, RW 2017, held in London, UK, in July 2017. In 2017, the theme of the school was "Semantic Interoperability on the Web", which encompasses subjects such as data integration, open data management, reasoning over linked data, database to ontology mapping, query answering over ontologies, hybrid reasoning with rules and ontologies, and ontology-based dynamic systems. The papers of this volume focus on these topics and also address foundational reasoning techniques used in answer set programming and ontologies.

Content

Neumaier, Sebastian (et al.): Data Integration for Open Data on the Web - Stamou, Giorgos (et al.): Ontological Query Answering over Semantic Data - Calì, Andrea: Ontology Querying: Datalog Strikes Back - Sequeda, Juan F.: Integrating Relational Databases with the Semantic Web: A Reflection - Rousset, Marie-Christine (et al.): Datalog Revisited for Reasoning in Linked Data - Kaminski, Roland (et al.): A Tutorial on Hybrid Answer Set Solving with clingo - Eiter, Thomas (et al.): Answer Set Programming with External Source Access - Lukasiewicz, Thomas: Uncertainty Reasoning for the Semantic Web - Calvanese, Diego (et al.): OBDA for Log Extraction in Process Mining

RSWK

Ontologie <Wissensverarbeitung> / Semantic Web

Series

Lecture Notes in Computer Scienc;10370 )(Information Systems and Applications, incl. Internet/Web, and HCI

Subject

Ontologie <Wissensverarbeitung> / Semantic Web

Theme

Semantic Web

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Abstract

Die schnelle Entwicklung der Internet- und Web-Technologie verändert den Stand der Technik in der Kommunikation von Wissen oder Forschungsergebnissen. Insbesondere werden semantische Technologien, verknüpfte und offene Daten zu entscheidenden Faktoren für einen erfolgreichen und effizienten Forschungsfortschritt. Zuerst definiere ich den Research Data Service (RDS) und diskutiere typische aktuelle und mögliche zukünftige Nutzungsszenarien mit RDS. Darüber hinaus bespreche ich den Stand der Technik in den Bereichen semantische Dienstleistung und Datenanmerkung und API-Konstruktion sowie infrastrukturelle Lösungen, die für die RDS-Realisierung anwendbar sind. Zum Schluss werden noch innovative Methoden der Online-Verbreitung, Förderung und effizienten Kommunikation der Forschung diskutiert.

Theme

Semantic Web

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Abstract

Children represent an increasing group of web users. Some of the key problems that hamper their search experience is their limited vocabulary, their difficulty in using the right keywords, and the inappropriateness of their general-purpose query suggestions. In this work, we propose a method that uses tags from social media to suggest queries related to children's topics. Concretely, we propose a simple yet effective approach to bias a random walk defined on a bipartite graph of web resources and tags through keywords that are more commonly used to describe resources for children. We evaluate our method using a large query log sample of queries submitted by children. We show that our method outperforms by a large margin the query suggestions of modern search engines and state-of-the art query suggestions based on random walks. We improve further the quality of the ranking by combining the score of the random walk with topical and language modeling features to emphasize even more the child-related aspects of the query suggestions.

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Abstract

One of the problems faced by users of databases containing textual documents is the difficulty in retrieving relevant results due to the diverse vocabulary used in queries and contained in relevant documents, especially when there are only a small number of relevant documents. This problem is known as the Vocabulary Gap. The PIKES team have constructed a small test collection of 331 articles extracted from a blog and a Gold Standard for 35 queries selected from the blog's search log so the results of different approaches to semantic search can be compared. So far, prior approaches include recognising Named Entities in documents and queries, and relations including temporal relations, and represent them as `semantic layers' in a retrieval system index. In this work, we take two different approaches that do not involve Named Entity Recognition. In the first approach, we process an unannotated version of the PIKES document collection using Latent Semantic Analysis and use a combination of query coordination and automatic relevance feedback with which we outperform prior work. However, this approach is highly dependent on the underlying collection, and is not necessarily scalable to massive collections. In our second approach, we use an LSA Model generated by SEMILAR from a Wikipedia dump to generate a Term Similarity Matrix (TSM). We automatically expand the queries in the PIKES test collection with related terms from the TSM and submit them to a term-by-document matrix derived by indexing the PIKES collection using the Vector Space Model. Coupled with a combination of query coordination and automatic relevance feedback we also outperform prior work with this approach. The advantage of the second approach is that it is independent of the underlying document collection.

Date

10. 3.2017 13:29:57

Series

Information Systems and Applications, incl. Internet/Web, and HCI; 10151

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Abstract

This collection of research papers provides extensive information on deploying services, concepts, and approaches for using open linked data from libraries and other cultural heritage institutions. With a special emphasis on how libraries and other cultural heritage institutions can create effective end user interfaces using open, linked data or other datasets. These papers are essential reading for any one interesting in user interface design or the semantic web.

Content

H. Frank Cervone: Linked data and user interaction : an introduction -- Paola Di Maio: Linked Data Beyond Libraries Towards Universal Interfaces and Knowledge Unification -- Emmanuelle Bermes: Following the user's flow in the Digital Pompidou -- Patrick Le Bceuf: Customized OPACs on the Semantic Web : the OpenCat prototype -- Ryan Shaw, Patrick Golden and Michael Buckland: Using linked library data in working research notes -- Timm Heuss, Bernhard Humm.Tilman Deuschel, Torsten Frohlich, Thomas Herth and Oliver Mitesser: Semantically guided, situation-aware literature research -- Niklas Lindstrom and Martin Malmsten: Building interfaces on a networked graph -- Natasha Simons, Arve Solland and Jan Hettenhausen: Griffith Research Hub. Vgl.: http://d-nb.info/1032799889.

LCSH

Semantic Web

RSWK

Bibliothek / Linked Data / Benutzer / Mensch-Maschine-Kommunikation / Recherche / Suchverfahren / Aufsatzsammlung
Linked Data / Online-Katalog / Semantic Web / Benutzeroberfläche / Kongress / Singapur <2013>

Subject

Bibliothek / Linked Data / Benutzer / Mensch-Maschine-Kommunikation / Recherche / Suchverfahren / Aufsatzsammlung
Linked Data / Online-Katalog / Semantic Web / Benutzeroberfläche / Kongress / Singapur <2013>
Semantic Web

Theme

Semantic Web

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Abstract

This study examined the task type as an important factor in collaborative information seeking activities, devoting special attention to its impacts on collaborative information seeking behavior, awareness and sentiment. Collaborative information search experiments were conducted on a collaborative search system-Coagmento-for three different types of task (informational, transactional and navigational). System log, surveys and semi-structured interviews were used to collect data, with quantitative and qualitative analyses carried out on the data which related to 12 participants in four groups. Quantitative analysis employed SPSS 20, while qualitative analysis was carried out using ATLAS.ti. Through our research, we found that the task types have impact on users' collaborative information seeking behavior in terms of web page browsing, search and image using, as well as interact with task awareness. A collaborative team approach is more suitable for completing the informational task than transactional and navigational tasks, while the task type also influences the sentiment. Concretely speaking, the transactional task causes more negative emotions.

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Abstract

More and more cultural heritage institutions publish their collections, vocabularies and metadata on the Web. The resulting Web of linked cultural data opens up exciting new possibilities for searching and browsing through these cultural heritage collections. We report on ongoing work in which we investigate the estimation of relevance in this Web of Culture. We study existing measures of semantic distance and how they apply to two use cases. The use cases relate to the structured, multilingual and multimodal nature of the Culture Web. We distinguish between measures using the Web, such as Google distance and PMI, and measures using the Linked Data Web, i.e. the semantic structure of metadata vocabularies. We perform a small study in which we compare these semantic distance measures to human judgements of relevance. Although it is too early to draw any definitive conclusions, the study provides new insights into the applicability of semantic distance measures to the Web of Culture, and clear starting points for further research.

Date

29. 7.2011 14:44:56
26.12.2011 13:40:22

Theme

Semantic Web