Knowledge representation and reasoning
Annual review of computer science vol. 1, 1986
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Proceedings of the 11th international conference on World Wide Web
Modern Information Retrieval
Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
Personalizing search via automated analysis of interests and activities
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Google's PageRank and Beyond: The Science of Search Engine Rankings
Google's PageRank and Beyond: The Science of Search Engine Rankings
Experimental Bounds on the Usefulness of Personalized and Topic-Sensitive PageRank
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Computing information value from RDF graph properties
Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
| Hi-index | 0.00 |
Information valuation has typically been carried out implicitly in question-answering and document retrieval systems. We argue that explicit information valuation is needed to move away from the system and process-centric nature of implicit valuation which has also hindered the theoretical study of information value under a unified and explicit framework. In this paper we present a graphical-based model for explicit information valuation. Our model caters to the subjective nature of information quality by measuring the impact a candidate piece of information may have on a knowledge base representing the recipient's world view. Our model is capable of evaluating information semantically at the statement level and is in effect basing information-valuation on information-understanding. However, information value can be computed and predicted using our causal graph model without requiring full logical inference typically needed for information-understanding.