-
Chowdhury, G.G.: Template mining for information extraction from digital documents (1999)
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- Date
- 2. 4.2000 18:01:22
-
Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997)
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- Abstract
- Defines digital libraries and discusses the effects of new technology on librarians. Examines the different viewpoints of librarians and information technologists on digital libraries. Describes the development of a digital library at the National Drug Intelligence Center, USA, which was carried out in collaboration with information technology experts. The system is based on Web enabled search technology to find information, data visualization and data mining to visualize it and use of SGML as an information standard to store it
- Date
- 22.11.1998 18:57:22
-
Huvila, I.: Mining qualitative data on human information behaviour from the Web (2010)
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- Abstract
- This paper discusses an approach of collecting qualitative data on human information behaviour that is based on mining web data using search engines. The approach is technically the same that has been used for some time in webometric research to make statistical inferences on web data, but the present paper shows how the same tools and data collecting methods can be used to gather data for qualitative data analysis on human information behaviour.
- 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
-
Hofstede, A.H.M. ter; Proper, H.A.; Van der Weide, T.P.: Exploiting fact verbalisation in conceptual information modelling (1997)
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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
-
Amir, A.; Feldman, R.; Kashi, R.: ¬A new and versatile method for association generation (1997)
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- Source
- Information systems. 22(1997) nos.5/6, S.333-347
-
Ohly, H.P.: Bibliometric mining : added value from document analysis and retrieval (2008)
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- Abstract
- Bibliometrics is understood as statistical analysis of scientific structures and processes. The analyzed data result from information and administrative actions. The demand for quality judgments or the discovering of new structures and information means that Bibliometrics takes on the role of being exploratory and decision supporting. To the extent that it has acquired important features of Data Mining, the analysis of text and internet material can be viewed as an additional challenge. In the sense of an evaluative approach Bibliometrics can also be seen to apply inference procedures as well as navigation tools.
- Source
- Kompatibilität, Medien und Ethik in der Wissensorganisation - Compatibility, Media and Ethics in Knowledge Organization: Proceedings der 10. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation Wien, 3.-5. Juli 2006 - Proceedings of the 10th Conference of the German Section of the International Society of Knowledge Organization Vienna, 3-5 July 2006. Ed.: H.P. Ohly, S. Netscher u. K. Mitgutsch
-
Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015)
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- Abstract
- Numerous studies have explored the possibility of uncovering information from web search queries but few have examined the factors that affect web query data sources. We conducted a study that investigated this issue by comparing Google Trends and Baidu Index. Data from these two services are based on queries entered by users into Google and Baidu, two of the largest search engines in the world. We first compared the features and functions of the two services based on documents and extensive testing. We then carried out an empirical study that collected query volume data from the two sources. We found that data from both sources could be used to predict the quality of Chinese universities and companies. Despite the differences between the two services in terms of technology, such as differing methods of language processing, the search volume data from the two were highly correlated and combining the two data sources did not improve the predictive power of the data. However, there was a major difference between the two in terms of data availability. Baidu Index was able to provide more search volume data than Google Trends did. Our analysis showed that the disadvantage of Google Trends in this regard was due to Google's smaller user base in China. The implication of this finding goes beyond China. Google's user bases in many countries are smaller than that in China, so the search volume data related to those countries could result in the same issue as that related to China.
- Source
- Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
-
Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013)
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- Date
- 22. 3.2013 19:43:01
- Source
- Journal of the American Society for Information Science and Technology. 64(2013) no.3, S.574-586
-
Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019)
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- Date
- 7. 3.2019 16:32:22
- Source
- Journal of the Association for Information Science and Technology. 70(2019) no.4, S.402-411
-
Bath, P.A.: Data mining in health and medical information (2003)
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- Abstract
- Data mining (DM) is part of a process by which information can be extracted from data or databases and used to inform decision making in a variety of contexts (Benoit, 2002; Michalski, Bratka & Kubat, 1997). DM includes a range of tools and methods for extractiog information; their use in the commercial sector for knowledge extraction and discovery has been one of the main driving forces in their development (Adriaans & Zantinge, 1996; Benoit, 2002). DM has been developed and applied in numerous areas. This review describes its use in analyzing health and medical information.
- Source
- Annual review of information science and technology. 38(2004), S.331-370
-
Lam, W.; Yang, C.C.; Menczer, F.: Introduction to the special topic section on mining Web resources for enhancing information retrieval (2007)
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- Abstract
- The amount of information on the Web has been expanding at an enormous pace. There are a variety of Web documents in different genres, such as news, reports, reviews. Traditionally, the information displayed on Web sites has been static. Recently, there are many Web sites offering content that is dynamically generated and frequently updated. It is also common for Web sites to contain information in different languages since many countries adopt more than one language. Moreover, content may exist in multimedia formats including text, images, video, and audio.
- Footnote
- Einführung in einen Themenschwerpunkt "Mining Web resources for enhancing information retrieval"
- Source
- Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1791-1792
-
Chen, S.Y.; Liu, X.: ¬The contribution of data mining to information science : making sense of it all (2005)
0.01
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- Source
- Journal of information science. 30(2005) no.6, S.550-
-
Cardie, C.: Empirical methods in information extraction (1997)
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- Abstract
- Surveys the use of empirical, machine-learning methods for information extraction. Presents a generic architecture for information extraction systems and surveys the learning algorithms that have been developed to address the problems of accuracy, portability, and knowledge acquisition for each component of the architecture
- Footnote
- Contribution to a special section reviewing recent research in empirical methods in speech recognition, syntactic parsing, semantic processing, information extraction and machine translation
-
Budzik, J.; Hammond, K.J.; Birnbaum, L.: Information access in context (2001)
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-
Blake, C.: Text mining (2011)
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- Source
- Annual review of information science and technology. 45(2011) no.1, S.121-155
-
Chen, H.; Chau, M.: Web mining : machine learning for Web applications (2003)
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- Abstract
- With more than two billion pages created by millions of Web page authors and organizations, the World Wide Web is a tremendously rich knowledge base. The knowledge comes not only from the content of the pages themselves, but also from the unique characteristics of the Web, such as its hyperlink structure and its diversity of content and languages. Analysis of these characteristics often reveals interesting patterns and new knowledge. Such knowledge can be used to improve users' efficiency and effectiveness in searching for information an the Web, and also for applications unrelated to the Web, such as support for decision making or business management. The Web's size and its unstructured and dynamic content, as well as its multilingual nature, make the extraction of useful knowledge a challenging research problem. Furthermore, the Web generates a large amount of data in other formats that contain valuable information. For example, Web server logs' information about user access patterns can be used for information personalization or improving Web page design.
- Source
- Annual review of information science and technology. 38(2004), S.289-330
-
Ku, L.-W.; Chen, H.-H.: Mining opinions from the Web : beyond relevance retrieval (2007)
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- Abstract
- Documents discussing public affairs, common themes, interesting products, and so on, are reported and distributed on the Web. Positive and negative opinions embedded in documents are useful references and feedbacks for governments to improve their services, for companies to market their products, and for customers to purchase their objects. Web opinion mining aims to extract, summarize, and track various aspects of subjective information on the Web. Mining subjective information enables traditional information retrieval (IR) systems to retrieve more data from human viewpoints and provide information with finer granularity. Opinion extraction identifies opinion holders, extracts the relevant opinion sentences, and decides their polarities. Opinion summarization recognizes the major events embedded in documents and summarizes the supportive and the nonsupportive evidence. Opinion tracking captures subjective information from various genres and monitors the developments of opinions from spatial and temporal dimensions. To demonstrate and evaluate the proposed opinion mining algorithms, news and bloggers' articles are adopted. Documents in the evaluation corpora are tagged in different granularities from words, sentences to documents. In the experiments, positive and negative sentiment words and their weights are mined on the basis of Chinese word structures. The f-measure is 73.18% and 63.75% for verbs and nouns, respectively. Utilizing the sentiment words mined together with topical words, we achieve f-measure 62.16% at the sentence level and 74.37% at the document level.
- Footnote
- Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"
- Source
- Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1838-1850
-
Wu, T.; Pottenger, W.M.: ¬A semi-supervised active learning algorithm for information extraction from textual data (2005)
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- Abstract
- In this article we present a semi-supervised active learning algorithm for pattern discovery in information extraction from textual data. The patterns are reduced regular expressions composed of various characteristics of features useful in information extraction. Our major contribution is a semi-supervised learning algorithm that extracts information from a set of examples labeled as relevant or irrelevant to a given attribute. The approach is semi-supervised because it does not require precise labeling of the exact location of features in the training data. This significantly reduces the effort needed to develop a training set. An active learning algorithm is used to assist the semi-supervised learning algorithm to further reduce the training set development effort. The active learning algorithm is seeded with a Single positive example of a given attribute. The context of the seed is used to automatically identify candidates for additional positive examples of the given attribute. Candidate examples are manually pruned during the active learning phase, and our semi-supervised learning algorithm automatically discovers reduced regular expressions for each attribute. We have successfully applied this learning technique in the extraction of textual features from police incident reports, university crime reports, and patents. The performance of our algorithm compares favorably with competitive extraction systems being used in criminal justice information systems.
- Source
- Journal of the American Society for Information Science and Technology. 56(2005) no.3, S.258-271
-
O'Brien, H.L.; Lebow, M.: Mixed-methods approach to measuring user experience in online news interactions (2013)
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- Abstract
- When it comes to evaluating online information experiences, what metrics matter? We conducted a study in which 30 people browsed and selected content within an online news website. Data collected included psychometric scales (User Engagement, Cognitive Absorption, System Usability Scales), self-reported interest in news content, and performance metrics (i.e., reading time, browsing time, total time, number of pages visited, and use of recommended links); a subset of the participants had their physiological responses recorded during the interaction (i.e., heart rate, electrodermal activity, electrocmytogram). Findings demonstrated the concurrent validity of the psychometric scales and interest ratings and revealed that increased time on tasks, number of pages visited, and use of recommended links were not necessarily indicative of greater self-reported engagement, cognitive absorption, or perceived usability. Positive ratings of news content were associated with lower physiological activity. The implications of this research are twofold. First, we propose that user experience is a useful framework for studying online information interactions and will result in a broader conceptualization of information interaction and its evaluation. Second, we advocate a mixed-methods approach to measurement that employs a suite of metrics capable of capturing the pragmatic (e.g., usability) and hedonic (e.g., fun, engagement) aspects of information interactions. We underscore the importance of using multiple measures in information research, because our results emphasize that performance and physiological data must be interpreted in the context of users' subjective experiences.
- Source
- Journal of the American Society for Information Science and Technology. 64(2013) no.8, S.1543-1556
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Baeza-Yates, R.; Hurtado, C.; Mendoza, M.: Improving search engines by query clustering (2007)
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- Abstract
- In this paper, we present a framework for clustering Web search engine queries whose aim is to identify groups of queries used to search for similar information on the Web. The framework is based on a novel term vector model of queries that integrates user selections and the content of selected documents extracted from the logs of a search engine. The query representation obtained allows us to treat query clustering similarly to standard document clustering. We study the application of the clustering framework to two problems: relevance ranking boosting and query recommendation. Finally, we evaluate with experiments the effectiveness of our approach.
- Footnote
- Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"
- Source
- Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1793-1804
