Search (477 results, page 1 of 24)

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Abstract

What is knowledge representation and reasoning (KR&R)? Alas, a thorough account would require a book, or at least a dedicated, full-length paper, but here we shall have to make do with something simpler. Since most readers are likely to have an intuitive grasp of the essence of KR&R, our simple account should suffice. The interesting thing is that this simple account itself makes reference to some of the foundational distinctions in the field of philosophy. These distinctions also play a central role in artificial intelligence (AI) and computer science. To begin with, the first distinction in KR&R is that we identify knowledge with knowledge that such-and-such holds (possibly to a degree), rather than knowing how. If you ask an expert tennis player how he manages to serve a ball at 130 miles per hour on his first serve, and then serve a safer, topspin serve on his second should the first be out, you may well receive a confession that, if truth be told, this athlete can't really tell you. He just does it; he does something he has been doing since his youth. Yet, there is no denying that he knows how to serve. In contrast, the knowledge in KR&R must be expressible in declarative statements. For example, our tennis player knows that if his first serve lands outside the service box, it's not in play. He thus knows a proposition, conditional in form.

Date

9. 2.2017 19:22:14

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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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Source

ABI-Technik. 39(2019) H.2, S.130-141

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Source

ABI-Technik. 39(2019) H.2, S.130-141

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Content

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

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Pages

S.124-130

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Source

Knowledge organization. 41(2014) no.2, S.123-130

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Abstract

This study explores the main determinants of social network adoption at the country level. We use the technology-organization-environment (TOE) framework to investigate factors influencing social network adoption. The authors use cross-sectional data from 130 countries. The results indicate that social network adoption, at the country level, is positively influenced by the technological maturity, public readiness, and information and communication technology law sophistication. Technological, organizational, and environmental factors altogether accounted for 67% of variance in social network adoption. These findings provide a first insight into the usage of social network sites at the country level, as well as the main factors that influence public adoption. Implications for research and practice are discussed.

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Source

Journal of the Association for Information Science and Technology. 70(2019) no.2, S.130-139

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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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Abstract

We present a new, two-stage, self-supervised algorithm for author disambiguation in large bibliographic databases. In the first "bootstrap" stage, a collection of high-precision features is used to bootstrap a training set with positive and negative examples of coreferring authors. A supervised feature-based classifier is then trained on the bootstrap clusters and used to cluster the authors in a larger unlabeled dataset. Our self-supervised approach shares the advantages of unsupervised approaches (no need for expensive hand labels) as well as supervised approaches (a rich set of features that can be discriminatively trained). The algorithm disambiguates 54,000,000 author instances in Thomson Reuters' Web of Knowledge with B3 F1 of.807. We analyze parameters and features, particularly those from citation networks, which have not been deeply investigated in author disambiguation. The most important citation feature is self-citation, which can be approximated without expensive extraction of the full network. For the supervised stage, the minor improvement due to other citation features (increasing F1 from.748 to.767) suggests they may not be worth the trouble of extracting from databases that don't already have them. A lean feature set without expensive abstract and title features performs 130 times faster with about equal F1.

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Pages

S.109-130

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Abstract

Primary health care practitioners routinely search for information within electronic knowledge resources. We proposed four levels of outcomes of information-seeking: situational relevance, cognitive impact, information use, and patient health outcomes. Our objective was to produce clinical vignettes for describing and testing these levels. We conducted a mixed methods study combining a quantitative longitudinal study and a qualitative multiple case study. Participants were 10 nurses, 10 medical residents, and 10 pharmacists. They had access to an online resource, and did 793 searches for treatment recommendations. Using the Information Assessment Method (IAM), participants rated their searches for each of the four levels. Rated searches were examined in interviews guided by log reports and a think-aloud protocol. Cases were defined as clearly described searches where clinical information was used for a specific patient. For each case, interviewees described the four levels of outcomes. Quantitative and qualitative data were merged into clinical vignettes. We produced 130 clinical vignettes. Specifically, 46 vignettes (35.4%) corresponded to clinical situations where information use was associated with one or more than one type of positive patient health outcome: increased patient knowledge (n = 28), avoidance of unnecessary or inappropriate intervention (n = 25), prevention of disease or health deterioration (n = 9), health improvement (n = 6), and increased patient satisfaction (n = 3). Results suggested information use was associated with perceived benefits for patients. This may encourage clinicians to search for information more often when they feel the need. Results supported the four proposed levels of outcomes, which can be transferable to other information-seeking contexts.

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Abstract

Democracy is represented on web interface design (Li, 2010). Wittfogel's (1957) Eastern autocracy states that 2 environmental dimensions, rainfall and sea border, influence the origin of democracy. This study examined Wittfogel's Eastern autocracy theory through statistical analysis of average annual precipitation, land boundaries, latitudes, and annual temperature of 196 countries and territories with their freedom levels defined by Freedom House, to find out the correlations between these geospatial factors and democracy. In addition, this study extended its investigation to web interface design by examining democracy represented on college/university websites in correlations with these geospatial factors. A total of 130 college/university websites selected from 65 countries were coded and examined systematically in linear and multiple regression analyses. This study concluded that democracy correlates positively with annual precipitation and latitude, but negatively with land boundaries and annual temperature. Furthermore, this study indicated that these 4 geospatial variables associate with democracy represented on web interface design, although the associations are not statistically significant. This study also suggested that it is more accurate to predict democracy if the 4 geospatial factors are considered together as dependent variables. By examining Wittfogel's theory of hydraulic civilization on web interface design, this study not only extended its sociological perspective to the information science arena, but also provided a better understanding of the functionality of the Internet in information dissemination and its cultural and sociological aspects.

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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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Date

22. 3.2013 16:18:36

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Date

27.12.2014 18:54:22

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Pages

XV, 130 S

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Date

22. 1.2012 13:17:00

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Date

26. 4.2015 19:27:22