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A distributed, semiotic-inductive, and human-oriented approach to web-scale knowledge retrieval
| Content Provider | ACM Digital Library |
|---|---|
| Author | Portmann, Edy Graf, Cédric Kaufmann, Michael Alexander |
| Abstract | Web-scale knowledge retrieval can be enabled by distributed information retrieval; clustering Web clients to a large-scale computing infrastructure for knowledge discovery from Web documents. Based on this infrastructure, we propose to apply semiotic (i.e., sub-syntactical) and inductive (i.e., probabilistic) methods for inferring concept associations in human knowledge. These associations can be combined to form a fuzzy (i.e., gradual) semantic net representing a map of the knowledge in the Web. Thus, we propose to provide interactive visualizations of these cognitive concept maps to end users, who can browse and search the Web in a human-oriented, visual, and associative interface. |
| Starting Page | 1 |
| Ending Page | 8 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450317115 |
| DOI | 10.1145/2389656.2389658 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-10-29 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Inductive inference Distributed information retrieval Web-scale knowledge retrieval Human-oriented computer interaction Computational semiotics Human-computer information retrieval Explorative search Knowledge discovery from data |
| Content Type | Text |
| Resource Type | Article |
