In social bookmark tools users are setting up lightweight conceptual structures called folksonomies. Currently, the information retrieval support is limited. We present a formal model and a new search algorithm for fo...
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In social bookmark tools users are setting up lightweight conceptual structures called folksonomies. Currently, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset. A long version of this paper has been published at the European Semantic Web Conference 2006 [3].
This book constitutes the refereed proceedings of the 7th International Conference on knowledgescience, engineering and Management, KSEM 2014, held in Sibiu, Romania, in October 2014. The 30 revised full papers prese...
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ISBN:
(数字)9783319120966
ISBN:
(纸本)9783319120959
This book constitutes the refereed proceedings of the 7th International Conference on knowledgescience, engineering and Management, KSEM 2014, held in Sibiu, Romania, in October 2014. The 30 revised full papers presented together with 5 short papers and 3 keynotes were carefully selected and reviewed from 77 submissions. The papers are organized in topical sections on formal semantics; content and document analysis; concept and lexical analysis; clustering and classification; metamodeling and conceptual modeling; enterprise knowledge; knowledge discovery and retrieval; formal knowledge processing; ontology engineering and management; knowledge management; and hybrid knowledge systems.
Web users use search engine to find useful information on the Internet. However current web search engines return answer to a query independent of specific user information need. Since web users with similar web behav...
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Sometimes it is not possible for a user to state a retrieval goal explicitly a priori. One common way to support such exploratory retrieval scenarios is to give an overview using a neighborhood-preserving projection o...
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Sometimes it is not possible for a user to state a retrieval goal explicitly a priori. One common way to support such exploratory retrieval scenarios is to give an overview using a neighborhood-preserving projection of the collection onto two dimensions. However, neighborhood cannot always be preserved in the projection because of the dimensionality reduction. Further, there is usually more than one way to look at a collection of images - and diversity grows with the number of features that can be extracted. We describe an adaptive zoomable interface for exploration that addresses both problems: It makes use of a complex non-linear multi-focal zoom lens that exploits the distorted neighborhood relations introduced by the projection. We further introduce the concept of facet distances representing different aspects of image similarity. Given user-specific weightings of these aspects, the system can adapt to the user's way of exploring the collection by manipulation of the neighborhoods as well as the projection.
Proof-Number Search (PNS) and Monte-Carlo Tree Search (MCTS) have been successfully applied for decision making in a range of games. This paper proposes a new approach called PN-MCTS that combines these two tree-searc...
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The ability to understand the expertise of users in Social Networking Sites (SNSs) is a key component for delivering effective information services such as talent seeking and user recommendation. However, users are of...
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ISBN:
(纸本)9781509044719
The ability to understand the expertise of users in Social Networking Sites (SNSs) is a key component for delivering effective information services such as talent seeking and user recommendation. However, users are often unwilling to make the effort to explicitly provide this information, so existing methods aimed at user expertise discovery in SNSs primarily rely on implicit inference. This work aims to infer a user's expertise based on their posts on the popular micro-blogging site Twitter. The work proposes a sentiment-weighted and topic relation-regularized learning model to address this problem. It first uses the sentiment intensity of a tweet to evaluate its importance in inferring a user's expertise. The intuition is that if a person can forcefully and subjectively express their opinion on a topic, it is more likely that the person has strong knowledge of that topic. Secondly, the relatedness between expertise topics is exploited to model the inference problem. The experiments reported in this paper were conducted on a large-scale dataset with over 10,000 Twitter users and 149 expertise topics. The results demonstrate the success of our proposed approach in user expertise inference and show that the proposed approach outperforms several alternative methods.
Consider a problem where 4k given vectors need to be partitioned into k clusters of four vectors each. A cluster of four vectors is called a quad, and the cost of a quad is the sum of the component-wise maxima of the ...
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Sensor fusion is the combining of sensory data from disparate sources such that the resulting information is in some sense better than would be possible when these sources were used individually. The natural uncertain...
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The core of scientific theories are laws. These laws often make use of theoretical terms, linguistic entities which do not directly refer to observables. There is therefore no direct way of determining which theoretic...
The core of scientific theories are laws. These laws often make use of theoretical terms, linguistic entities which do not directly refer to observables. There is therefore no direct way of determining which theoretical assertions are true. This suggests that multiple theories may exist which are incompatible with each other but compatible with all possible observations. Since such theories make the same empirical claims, empirical tests cannot be used to differentiate or rank such theories. One property that has been suggested for evaluating rival theories is coherence. This was only understood qualitatively until we [Kwok, ***. 98] introduced a coherence measure based on the average use of formulas in support sets for observations. The idea was to identify highly coherent theories with those whose formulas that are tightly coupled to account for observations, while low coherence theories contain many disjointed and isolated statements. Our current approach generalizes that insight to accommodate fundamental ideas from the philosophy of science and better mirrors scientific practice. Moreover, this new approach is neutral with respect to the philosophy and practice of science, and is able to explain notions like modularization using coherence.
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