-
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
-
Habernal, I.; Konopík, M.; Rohlík, O.: Question answering (2012)
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- Source
- Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a
-
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
-
Mayr, P.; Mutschke, P.; Petras, V.; Schaer, P.; Sure, Y.: Applying science models for search (2010)
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- Source
- Information und Wissen: global, sozial und frei? Proceedings des 12. Internationalen Symposiums für Informationswissenschaft (ISI 2011) ; Hildesheim, 9. - 11. März 2011. Hrsg.: J. Griesbaum, T. Mandl u. C. Womser-Hacker
-
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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-
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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-
González-Ibáñez, R.; Shah, C.; White, R.W.: Capturing 'Collabportunities' : a method to evaluate collaboration opportunities in information search using pseudocollaboration (2015)
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- Abstract
- In explicit collaborative search, two or more individuals coordinate their efforts toward a shared goal. Every day, Internet users with similar information needs have the potential to collaborate. However, online search is typically performed in solitude. Existing search systems do not promote explicit collaborations, and collaboration opportunities (collabportunities) are missed. In this article, we describe a method to evaluate the feasibility of transforming these collabportunities into recommendations for explicit collaboration. We developed a technique called pseudocollaboration to evaluate the benefits and costs of collabportunities through simulations. We evaluate the performance of our method using three data sets: (a) data from single users' search sessions, (b) data with collaborative search sessions between pairs of searchers, and (c) logs from a large-scale search engine with search sessions of thousands of searchers. Our results establish when and how collabportunities would significantly help or hinder the search process versus searches conducted individually. The method that we describe has implications for the design and implementation of recommendation systems for explicit collaboration. It also connects system-mediated and user-mediated collaborative search, whereby the system evaluates the likely benefits of collaborating for a search task and helps searchers make more informed decisions on initiating and executing such a collaboration.
-
Teixera Lopes, C.; Paiva, D.; Ribeiro, C.: Effects of language and terminology of query suggestions on medical accuracy considering different user characteristics (2017)
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-
Carstens, C.: Ontology based query expansion : retrieval support for the domain of educational research (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.
-
Shah, C.; Marchionini, G.: Awareness in collaborative information seeking (2010)
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-
Hoeber, O.: Human-centred Web search (2012)
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- Source
- Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a
-
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
-
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
