Search (5059 results, page 2 of 253)

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Abstract

Focuses on the information modelling side of conceptual modelling. Deals with the exploitation of fact verbalisations after finishing the actual information system. Verbalisations are used as input for the design of the so-called information model. Exploits these verbalisation in 4 directions: considers their use for a conceptual query language, the verbalisation of instances, the description of the contents of a database and for the verbalisation of queries in a computer supported query environment. Provides an example session with an envisioned tool for end user query formulations that exploits the verbalisation

Source

Information systems. 22(1997) nos.5/6, S.349-385

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Abstract

Provides a glossary of Internet search terms. Outlines a number of network retrieval tools and directories: Netscape's Internet search page, W3 search engines, Lycos, WebCrawler, InfoSeek, Yahoo, and CERN's Net Directory. Gices an example of how the Internet can be used to answer a reference query and the types of materials that can be retrieved

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Abstract

The performance of 8 ranking algorithms was evaluated with respect to their effectiveness in ranking terms for query expansion. The evaluation was conducted within an investigation of interactive query expansion and relevance feedback in a real operational environment. Focuses on the identification of algorithms that most effectively take cognizance of user preferences. user choices (i.e. the terms selected by the searchers for the query expansion search) provided the yardstick for the evaluation of the 8 ranking algorithms. This methodology introduces a user oriented approach in evaluating ranking algorithms for query expansion in contrast to the standard, system oriented approaches. Similarities in the performance of the 8 algorithms and the ways these algorithms rank terms were the main focus of this evaluation. The findings demonstrate that the r-lohi, wpq, enim, and porter algorithms have similar performance in bringing good terms to the top of a ranked list of terms for query expansion. However, further evaluation of the algorithms in different (e.g. full text) environments is needed before these results can be generalized beyond the context of the present study

Date

22. 2.1996 13:14:10

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Abstract

Two probabilistic approaches to cross-lingual retrieval are in wide use today, those based on probabilistic models of relevance, as exemplified by INQUERY, and those based on language modeling. INQUERY, as a query net model, allows the easy incorporation of query operators, including a synonym operator, which has proven to be extremely useful in cross-language information retrieval (CLIR), in an approach often called structured query translation. In contrast, language models incorporate translation probabilities into a unified framework. We compare the two approaches on Arabic and Spanish data sets, using two kinds of bilingual dictionaries--one derived from a conventional dictionary, and one derived from a parallel corpus. We find that structured query processing gives slightly better results when queries are not expanded. On the other hand, when queries are expanded, language modeling gives better results, but only when using a probabilistic dictionary derived from a parallel corpus. We pursue two additional issues inherent in the comparison of structured query processing with language modeling. The first concerns query expansion, and the second is the role of translation probabilities. We compare conventional expansion techniques (pseudo-relevance feedback) with relevance modeling, a new IR approach which fits into the formal framework of language modeling. We find that relevance modeling and pseudo-relevance feedback achieve comparable levels of retrieval and that good translation probabilities confer a small but significant advantage.

Date

26.12.2007 20:22:11

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Abstract

Query suggestion is a common feature of many information search systems. While much research has been conducted about how to generate suggestions, fewer studies have been conducted about how people interact with and use suggestions. The purpose of this paper is to investigate how and when people integrate query suggestions into their searches and the outcome of this usage. The paper further investigates the relationships between search expertise, topic difficulty, and temporal segment of the search and query suggestion usage. A secondary analysis of data was conducted using data collected in a previous controlled laboratory study. In this previous study, 23 undergraduate research participants used an experimental search system with query suggestions to conduct four topic searches. Results showed that participants integrated the suggestions into their searching fairly quickly and that participants with less search expertise used more suggestions and saved more documents. Participants also used more suggestions towards the end of their searches and when searching for more difficult topics. These results show that query suggestion can provide support in situations where people have less search expertise, greater difficulty searching and at specific times during the search.

Date

25. 1.2016 18:43:22

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Abstract

Discusses the analysis of video for the classification of images in order to develop a video database. Covers compression; motion recovery in digital video; low-level motion extraction; single macroblock tracing; intermediate-level motion analysis; high-level motion analysis; spatiotemporal hierarchical representation; information filtering and digital video; content filtering opertaors; the query language; querying video contents; an architecture for video classification and retrieval; the visual query language VEVA; and implementation of macroblock tracing

Date

8. 4.1996 9:22:36

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Abstract

Describes the design and implementation of an interface generator for constructing advanced visual query WWW interfaces that allow the specification of complex queries. The generated inerfaces share a consistent look and feel. The tool accepts as input a high-level specification of the interface and produces as output its implementation

Date

1. 8.1996 22:08:06

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Abstract

In this presentation, we therefore focus on a solution for providing uniform access to Digital Libraries and other online services. In order to enable uniform query access to heterogeneous sources, we must provide metadata interoperability in a way that a query language - in this case SPARQL - can cope with the incompatibility of the metadata in various sources without changing their already existing information models.

Date

26.12.2011 13:22:46

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Abstract

In diesem Kapitel wird beschrieben, wie Suchmaschinen Suchanfragen interpretieren können, um letztendlich den Nutzern besser auf ihren Kontext zugeschnittene Ergebnisse liefern zu können. Nach einer Diskussion der Notwendigkeit und der Einsatzmöglichkeiten des Query Understanding wird aufgezeigt, auf welcher Datenbasis und an welchen Ansatzpunkten Suchanfragen interpretiert werden können. Dann erfolgt eine Erläuterung der Interpretationsmöglichkeiten anhand der Suchanfragen-Facetten von Calderon-Benavides et al. (2010), welcher sich eine Diskussion der Verfahren zur Ermittlung der Facetten anschließt.

Date

18. 9.2018 18:22:18

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Abstract

Explains how 5 Internet search engines (AltaVista, HotBot, InfoSeek, Lycos, and Yahoo) handle field searching. Includes a chart which identifies where on a search engine's page a particular field is searched and the prefix syntax used, and gives examples. Details the individual fields that can be searched: data, title, URL, images, audiovideo and other page content, links and page depth

Source

Online. 22(1998) no.3, S.18-22

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Abstract

Over the past decade, the volume of information available digitally over the Internet has grown enormously. Technical developments in the area of search, such as Google's Page Rank algorithm, have proved so good at serving relevant results that Internet search has become integrated into daily human activity. One can endlessly explore topics of interest simply by querying and reading through the resulting links. Yet, although search engines are well known for providing relevant results based on users' queries, users do not always receive the results they are looking for. Google's Director of Research describes clickstream evidence of frustrated users repeatedly reformulating queries and searching through page after page of results. Given the general quality of search engine results, one must consider the possibility that the frustrated user's query is not effective; that is, it does not describe the essence of the user's interest. Indeed, extensive research into human search behavior has found that humans are not very effective at formulating good search queries that describe what they are interested in. Ideally, the user should simply point to a portion of text that sparked the user's interest, and a system should automatically formulate a search query that captures the essence of the text. In this paper, we describe an implemented system that provides this capability. We first describe how our work differs from existing work in automatic query formulation, and propose a new method for improved quantification of the relevance of candidate search terms drawn from input text using phrase-level analysis. We then propose an implementable method designed to provide relevant queries based on a user's text input. We demonstrate the quality of our results and performance of our system through experimental studies. Our results demonstrate that our system produces relevant search terms with roughly two-thirds precision and recall compared to search terms selected by experts, and that typical users find significantly more relevant results (31% more relevant) more quickly (64% faster) using our system than self-formulated search queries. Further, we show that our implementation can scale to request loads of up to 10 requests per second within current online responsiveness expectations (<2-second response times at the highest loads tested).

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Abstract

Inquiries made by academic library users are more complex than they may appear. Successful information retrieval based on complex queries is a function of cataloguing, classification, and the librarian's interpretation. Explores aspects of complexitiy using a proposed query as an example

Source

Journal of academic librarianship. 22(1996) no.2, S.111-117

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Date

1. 2.2016 18:25:22

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Abstract

This paper talks about several schemes for improving retrieval effectiveness that can be used in the named page finding tasks of web information retrieval (Overview of the TREC-2002 web track. In: Proceedings of the Eleventh Text Retrieval Conference TREC-2002, NIST Special Publication #500-251, 2003). These methods were applied on top of the basic information retrieval model as additional mechanisms to upgrade the system. Use of the title of web pages was found to be effective. It was confirmed that anchor texts of incoming links was beneficial as suggested in other works. Sentence-query similarity is a new type of information proposed by us and was identified to be the best information to take advantage of. Stratifying and re-ranking the retrieval list based on the maximum count of index terms in common between a sentence and a query resulted in significant improvement of performance. To demonstrate these facts a large-scale web information retrieval system was developed and used for experimentation.

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Abstract

This study focuses on the impact of age, prior domain knowledge and cognitive abilities on performance, query production and navigation strategies during information searching. Twenty older adults and nineteen young adults had to answer 12 information search problems of varying nature within two domain knowledge: health and manga. In each domain, participants had to perform two simple fact-finding problems (keywords provided and answer directly accessible on the search engine results page), two difficult fact-finding problems (keywords had to be inferred) and two open-ended information search problems (multiple answers possible and navigation necessary). Results showed that prior domain knowledge helped older adults improve navigation (i.e. reduced the number of webpages visited and thus decreased the feeling of disorientation), query production and reformulation (i.e. they formulated semantically more specific queries, and they inferred a greater number of new keywords).

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Abstract

Purpose - The purpose of this paper is to improve the conceptual-based search by incorporating structural ontological information such as concepts and relations. Generally, Semantic-based information retrieval aims to identify relevant information based on the meanings of the query terms or on the context of the terms and the performance of semantic information retrieval is carried out through standard measures-precision and recall. Higher precision leads to the (meaningful) relevant documents obtained and lower recall leads to the less coverage of the concepts. Design/methodology/approach - In this paper, the authors enhance the existing ontology-based indexing proposed by Kohler et al., by incorporating sibling information to the index. The index designed by Kohler et al., contains only super and sub-concepts from the ontology. In addition, in our approach, we focus on two tasks; query expansion and ranking of the expanded queries, to improve the efficiency of the ontology-based search. The aforementioned tasks make use of ontological concepts, and relations existing between those concepts so as to obtain semantically more relevant search results for a given query. Findings - The proposed ontology-based indexing technique is investigated by analysing the coverage of concepts that are being populated in the index. Here, we introduce a new measure called index enhancement measure, to estimate the coverage of ontological concepts being indexed. We have evaluated the ontology-based search for the tourism domain with the tourism documents and tourism-specific ontology. The comparison of search results based on the use of ontology "with and without query expansion" is examined to estimate the efficiency of the proposed query expansion task. The ranking is compared with the ORank system to evaluate the performance of our ontology-based search. From these analyses, the ontology-based search results shows better recall when compared to the other concept-based search systems. The mean average precision of the ontology-based search is found to be 0.79 and the recall is found to be 0.65, the ORank system has the mean average precision of 0.62 and the recall is found to be 0.51, while the concept-based search has the mean average precision of 0.56 and the recall is found to be 0.42. Practical implications - When the concept is not present in the domain-specific ontology, the concept cannot be indexed. When the given query term is not available in the ontology then the term-based results are retrieved. Originality/value - In addition to super and sub-concepts, we incorporate the concepts present in same level (siblings) to the ontological index. The structural information from the ontology is determined for the query expansion. The ranking of the documents depends on the type of the query (single concept query, multiple concept queries and concept with relation queries) and the ontological relations that exists in the query and the documents. With this ontological structural information, the search results showed us better coverage of concepts with respect to the query.

Date

20. 1.2015 18:30:22

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Abstract

The role of a modern librarian has become more and more demanding in the information age. One of the new challenges for the information specialists is what's usually called "the teaching librarian", meaning that the librarian or information specialist should be able to teach at least basic practical searching skills to the patrons in need for relevant information. Query Performance Analyser (QPA) is a tool for analysing and comparing the performance of individual queries. It has been developed in the department of information studies at the University of Tampere. It can be used in user training to demonstrate the characteristics of IR systems and different searching strategies. Usually users can't get any feedback about the effectiveness of their queries and therefore may have difficulties to perceive the actual fectiveness of a query formulated, or the effect changes between queries. QPA provides a instant visual feedback about the performance of a given query and gives the user a possibility to compare the effectiveness of multiple queries and the performance of different query formulation strategies. QPA is based on predefined search topics. They all contain a corpus of documents that are relevant to the given topic. The purpose of this paper is to give a brief insight to the infrastructure of QPA, the basic :Functionality of the QPA-based game, and to its implementation in IR education.

Date

22. 7.2009 11:03:43

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Abstract

Search engine users typically engage in multiquery sessions in their quest to fulfill their information needs. Despite a plethora of research findings suggesting that a significant group of users look for information within a specific geographical scope, existing reformulation studies lack a focused analysis of how users reformulate geographic queries. This study comprehensively investigates the ways in which users reformulate such needs in an attempt to fill this gap in the literature. Reformulated sessions were sampled from a query log of a major search engine to extract 2,400 entries that were manually inspected to filter geo sessions. This filter identified 471 search sessions that included geographical intent, and these sessions were analyzed quantitatively and qualitatively. The results revealed that one in five of the users who reformulated their queries were looking for geographically related information. They reformulated their queries by changing the content of the query rather than the structure. Users were not following a unified sequence of modifications and instead performed a single reformulation action. However, in some cases it was possible to anticipate their next move. A number of tasks in geo modifications were identified, including standard, multi-needs, multi-places, and hybrid approaches. The research concludes that it is important to specialize query reformulation studies to focus on particular query types rather than generically analyzing them, as it is apparent that geographic queries have their special reformulation characteristics.

Date

26. 1.2014 18:48:22

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Abstract

A substantial fraction of web search queries contain references to entities, such as persons, organizations, and locations. Recently, methods that exploit named entities have been shown to be more effective for query expansion than traditional pseudorelevance feedback methods. In this article, we introduce a supervised learning approach that exploits named entities for query expansion using Wikipedia as a repository of high-quality feedback documents. In contrast with existing entity-oriented pseudorelevance feedback approaches, we tackle query expansion as a learning-to-rank problem. As a result, not only do we select effective expansion terms but we also weigh these terms according to their predicted effectiveness. To this end, we exploit the rich structure of Wikipedia articles to devise discriminative term features, including each candidate term's proximity to the original query terms, as well as its frequency across multiple article fields and in category and infobox descriptors. Experiments on three Text REtrieval Conference web test collections attest the effectiveness of our approach, with gains of up to 23.32% in terms of mean average precision, 19.49% in terms of precision at 10, and 7.86% in terms of normalized discounted cumulative gain compared with a state-of-the-art approach for entity-oriented query expansion.

Date

22. 8.2014 17:07:50

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Abstract

Understanding what kinds of Web pages are the most useful for Web search engine users is a critical task in Web information retrieval (IR). Most previous works used hyperlink analysis algorithms to solve this problem. However, little research has been focused on query-independent Web data cleansing for Web IR. In this paper, we first provide analysis of the differences between retrieval target pages and ordinary ones based on more than 30 million Web pages obtained from both the Text Retrieval Conference (TREC) and a widely used Chinese search engine, SOGOU (www.sogou.com). We further propose a learning-based data cleansing algorithm for reducing Web pages that are unlikely to be useful for user requests. We found that there exists a large proportion of low-quality Web pages in both the English and the Chinese Web page corpus, and retrieval target pages can be identified using query-independent features and cleansing algorithms. The experimental results showed that our algorithm is effective in reducing a large portion of Web pages with a small loss in retrieval target pages. It makes it possible for Web IR tools to meet a large fraction of users' needs with only a small part of pages on the Web. These results may help Web search engines make better use of their limited storage and computation resources to improve search performance.