-
Wu, I.-C.; Vakkari, P.: Effects of subject-oriented visualization tools on search by novices and intermediates (2018)
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- Date
- 9.12.2018 16:22:25
-
Lu, L.; Yuan, U.: Shall I Google it or ask the competent villain down the hall? : the moderating role of information need in information source selection (2011)
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-
Habernal, I.; Konopík, M.; Rohlík, O.: Question answering (2012)
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Torres, S.D.; Hiemstra, D.; Weber, I.; Serdyukov, P.: Query recommendation in the information domain of children (2014)
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Abacha, A.B.; Zweigenbaum, P.: MEANS: A medical question-answering system combining NLP techniques and semantic Web technologies (2015)
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- Abstract
- The Question Answering (QA) task aims to provide precise and quick answers to user questions from a collection of documents or a database. This kind of IR system is sorely needed with the dramatic growth of digital information. In this paper, we address the problem of QA in the medical domain where several specific conditions are met. We propose a semantic approach to QA based on (i) Natural Language Processing techniques, which allow a deep analysis of medical questions and documents and (ii) semantic Web technologies at both representation and interrogation levels. We present our Semantic Question-Answering System, called MEANS and our proposed method for "Answer Search" based on semantic search and query relaxation. We evaluate the overall system performance on real questions and answers extracted from MEDLINE articles. Our experiments show promising results and suggest that a query-relaxation strategy can further improve the overall performance.
-
Xie, I.; Joo, S.: Factors affecting the selection of search tactics : tasks, knowledge, process, and systems (2012)
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-
Saastamoinen, M.; Järvelin, K.: Search task features in work tasks of varying types and complexity (2017)
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- Abstract
- Information searching in practice seldom is an end in itself. In work, work task (WT) performance forms the context, which information searching should serve. Therefore, information retrieval (IR) systems development/evaluation should take the WT context into account. The present paper analyzes how WT features: task complexity and task types, affect information searching in authentic work: the types of information needs, search processes, and search media. We collected data on 22 information professionals in authentic work situations in three organization types: city administration, universities, and companies. The data comprise 286 WTs and 420 search tasks (STs). The data include transaction logs, video recordings, daily questionnaires, interviews. and observation. The data were analyzed quantitatively. Even if the participants used a range of search media, most STs were simple throughout the data, and up to 42% of WTs did not include searching. WT's effects on STs are not straightforward: different WT types react differently to WT complexity. Due to the simplicity of authentic searching, the WT/ST types in interactive IR experiments should be reconsidered.
-
Xie, I.; Joo, S.: Transitions in search tactics during the Web-based search process (2010)
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Xie, I.; Joo, S.; Bennett-Kapusniak, R.: User involvement and system support in applying search tactics (2017)
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Karanam, S.; Oostendorp, H. van; Sanchiz, M.; Chin, J.; Fu, W.-T.: Cognitive modeling of age-related differences in information search behavior (2017)
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- Abstract
- In this study, we evaluated the ability of computational cognitive models of web-navigation like CoLiDeS and CoLiDeS+ to model i) user interactions with search engines and ii) individual differences in search behavior due to variations in cognitive factors such as aging. CoLiDeS and CoLiDeS+ were extended to predict user clicks on search engine result pages. Their performance was evaluated using actual behavioral data from an experiment in which 2 types of information search tasks (simple vs. difficult), were presented to younger and older participants. The results showed that the model predictions matched significantly better with the actual user behavior on difficult tasks compared to simple tasks and with younger participants compared to older participants, especially for difficult tasks. Also, the matches were significantly better with CoLiDeS+ compared to CoLiDeS, especially for difficult tasks. We conclude that the advanced capabilities of CoLiDeS+, such as incorporating contextual information and implementing backtracking strategies enable it to predict user behavior significantly better than CoLiDeS, especially on difficult tasks. The usefulness of these modeling outcomes for the design of support systems for older adults is discussed.
-
Vuong, T.; Saastamoinen, M.; Jacucci, G.; Ruotsalo, T.: Understanding user behavior in naturalistic information search tasks (2019)
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- Abstract
- Understanding users' search behavior has largely relied on the information available from search engine logs, which provide limited information about the contextual factors affecting users' behavior. Consequently, questions such as how users' intentions, task goals, and substances of the users' tasks affect search behavior, as well as what triggers information needs, remain largely unanswered. We report an experiment in which naturalistic information search behavior was captured by analyzing 24/7 continuous recordings of information on participants' computer screens. Written task diaries describing the participants' tasks were collected and used as real-life task contexts for further categorization. All search tasks were extracted and classified under various task categories according to users' intentions, task goals, and substances of the tasks. We investigated the effect of different task categories on three behavioral factors: search efforts, content-triggers, and application context. Our results suggest four findings: (i) Search activity is integrally associated with the users' creative processes. The content users have seen prior to searching more often triggers search, and is used as a query, within creative tasks. (ii) Searching within intellectual and creative tasks is more time-intensive, while search activity occurring as a part of daily routine tasks is associated with more frequent searching within a search task. (iii) Searching is more often induced from utility applications in tasks demanding a degree of intellectual effort. (iv) Users' leisure information-seeking activity is occurring inherently within social media services or comes from social communication platforms. The implications of our findings for information access and management systems are discussed.
-
Aloteibi, S.; Sanderson, M.: Analyzing geographic query reformulation : an exploratory study (2014)
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- Date
- 26. 1.2014 18:48:22
-
Monchaux, S.; Amadieu, F.; Chevalier, A.; Mariné, C.: Query strategies during information searching : effects of prior domain knowledge and complexity of the information problems to be solved (2015)
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- Date
- 25. 1.2016 18:46:22
-
Pontis, S.; Blandford, A.; Greifeneder, E.; Attalla, H.; Neal, D.: Keeping up to date : an academic researcher's information journey (2017)
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- Source
- Journal of the Association for Information Science and Technology. 68(2017) no.1, S.22-35
-
Sachse, J.: ¬The influence of snippet length on user behavior in mobile web search (2019)
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- Date
- 20. 1.2015 18:30:22
-
Hopkins, M.E.; Zavalina, O.L.: Evaluating physicians' serendipitous knowledge discovery in online discovery systems : a new approach (2019)
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- Date
- 20. 1.2015 18:30:22
-
Barrio, P.; Gravano, L.: Sampling strategies for information extraction over the deep web (2017)
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- Abstract
- Information extraction systems discover structured information in natural language text. Having information in structured form enables much richer querying and data mining than possible over the natural language text. However, information extraction is a computationally expensive task, and hence improving the efficiency of the extraction process over large text collections is of critical interest. In this paper, we focus on an especially valuable family of text collections, namely, the so-called deep-web text collections, whose contents are not crawlable and are only available via querying. Important steps for efficient information extraction over deep-web text collections (e.g., selecting the collections on which to focus the extraction effort, based on their contents; or learning which documents within these collections-and in which order-to process, based on their words and phrases) require having a representative document sample from each collection. These document samples have to be collected by querying the deep-web text collections, an expensive process that renders impractical the existing sampling approaches developed for other data scenarios. In this paper, we systematically study the space of query-based document sampling techniques for information extraction over the deep web. Specifically, we consider (i) alternative query execution schedules, which vary on how they account for the query effectiveness, and (ii) alternative document retrieval and processing schedules, which vary on how they distribute the extraction effort over documents. We report the results of the first large-scale experimental evaluation of sampling techniques for information extraction over the deep web. Our results show the merits and limitations of the alternative query execution and document retrieval and processing strategies, and provide a roadmap for addressing this critically important building block for efficient, scalable information extraction.
-
Renugadevi, S.; Geetha, T.V.; Gayathiri, R.L.; Prathyusha, S.; Kaviya, T.: Collaborative search using an implicitly formed academic network (2014)
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- Date
- 20. 1.2015 18:30:22
-
Waschatz, B.: Schmökern ist schwierig : Viele Uni-Bibliotheken ordnen ihre Bücher nicht - Tipps für eine erfolgreiche Suche (2010)
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- Date
- 3. 5.1997 8:44:22
