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Автор	Тема: На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:44 PART X: WEB and KNOWLEDGE-BASED INFORMATION SYSTEMS 2009p [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:44 PART-X: WEB and KNOWLEDGE-BASED INFORMATION SYSTEMS [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:44 PART-X: WEB and KNOWLEDGE-BASED INFORMATION SYSTEMS: =331=> 2010p DL-liteR in the Light of Propositional Logic for Decentralized Data Management, Nada Abdallah, Francois Goasdoue, Marie-Christine Rousset, http://ijcai.org/papers09/Abstracts/331.html This paper provides a decentralized data model and associated algorithms for peer data management systems (PDMS) based on the DL-liteR description logic. Our approach relies on reducing query reformulation and consistency checking for DL-liteR into reasoning in propositional logic. This enables a straightforward deployment of DL-liteR PDMSs on top of SomeWhere, a scalable propositional peer-to-peer inference system. We also show how to use the state-of-the-art Minicon algorithm for rewriting queries using views in DL-liteR in the centralized and decentralized cases. text: http://ijcai.org/papers09/Papers/IJCAI09-331.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:45 PART-X: WEB and KNOWLEDGE-BASED INFORMATION SYSTEMS: =332=> 2016p Sketching Techniques for Collaborative Filtering, Yoram Bachrach, Ely Porat, Jeffrey S. Rosenschein, http://ijcai.org/papers09/Abstracts/332.html Recommender systems attempt to highlight items that a target user is likely to find interesting. A common technique is to use collaborative filtering (CF), where multiple users share information so as to provide each with effective recommendations. A key aspect of CF systems is finding users whose tastes accurately reflect the tastes of some target user. Typically, the system looks for other agents who have had experience with many of the items the target user has examined, and whose classification of these items has a strong correlation with the classifications of the target user. Since the universe of items may be enormous and huge data sets are involved, sophisticated methods must be used to quickly locate appropriate other agents. We present a method for quickly determining the proportional intersection between the items that each of two users has examined, by sending and maintaining extremely concise “sketches” of the list of items. These sketches enable the approximation of the proportional intersection within a distance of \epsilon, with a high probability of 1-\delta. Our sketching techniques are based on random minwise independent hash functions, and use very little space and time, so they are well-suited for use in large-scale collaborative filtering systems. text: http://ijcai.org/papers09/Papers/IJCAI09-332.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:45 PART-X: WEB and KNOWLEDGE-BASED INFORMATION SYSTEMS: =333=> 2022p Spatial Processes for Recommender Systems, Fabian Bohnert, Daniel F. Schmidt, Ingrid Zukerman, http://ijcai.org/papers09/Abstracts/333.html Spatial processes are typically used to analyse and predict geographic data. This paper adapts such models to predicting a user's interests (i.e., implicit item ratings) within a recommender system in the museum domain. We present the theoretical framework for a model based on Gaussian spatial processes, and discuss efficient algorithms for parameter estimation. Our model was evaluated with a real-world dataset collected by tracking visitors in a museum, attaining a higher predictive accuracy than state-of-the-art collaborative filters. text: http://ijcai.org/papers09/Papers/IJCAI09-333.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:45 PART-X: WEB and KNOWLEDGE-BASED INFORMATION SYSTEMS: =334=> 2028p Dynamic Selection of Ontological Alignments: A Space Reduction Mechanism, Paul Doran, Valentina Tamma, Terry R. Payne, Ignazio Palmisano, http://ijcai.org/papers09/Abstracts/334.html Effective communication in open environments relies on the ability of agents to reach a mutual understanding of the exchanged message by reconciling the vocabulary (ontology) used. Various approaches have considered how mutually acceptable mappings between corresponding concepts in the agents' own ontologies may be determined dynamically through argumentation-based negotiation (such as Meaning-based Argumentation). However, the complexity of this process is high, approaching π<sub>2</sub><sup>(p)</sup>-complete in some cases. As reducing this complexity is non-trivial, we propose the use of ontology modularization as a means of reducing the space over which possible concepts are negotiated. The suitability of different modularization approaches as filtering mechanisms for reducing the negotiation search space is investigated, and a framework that integrates modularization with Meaning-based Argumentation is proposed. We empirically demonstrate that some modularization approaches not only reduce the number of alignments required to reach consensus, but also predict those cases where a service provider is unable to satisfy a request, without the need for negotiation. text: http://ijcai.org/papers09/Papers/IJCAI09-334.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:45 PART-X: WEB and KNOWLEDGE-BASED INFORMATION SYSTEMS: =335=> 2034p Improving Search In Social Networks by Agent Based Mining, Anil Gursel, Sandip Sen, http://ijcai.org/papers09/Abstracts/335.html Users share and access large volumes of information on social networking sites like Facebook, Flickr, del.icio.us, etc. Whereas a few of these sites have generic, impersonal searching mechanisms, we have developed an agent-based framework that mines the social network of a user to improve search results. Our Social Network-based Item Search (SNIS) system uses agents that utilize the connections of a user in the social network to facilitate the search for items of interest. Our approach generates targeted search results that can improve the precision of the result returned from a user's query. We have implemented the SNIS agent-based framework in Flickr, a photo-sharing social network, for searching for photos by using tag lists as search queries. We discuss the architecture of SNIS, motivate the searching scheme used, and demonstrate the effectiveness of the SNIS approach by presenting results. We also show how SNIS can be utilized for expertise location. text: http://ijcai.org/papers09/Papers/IJCAI09-335.pdf ===================================================> IJCAI Distinguished Paper Award Winner! PART-X: WEB and KNOWLEDGE-BASED INFORMATION SYSTEMS: =336=> 2040p Consequence-Driven Reasoning for Horn SHIQ Ontologies, Yevgeny Kazakov, http://ijcai.org/papers09/Abstracts/336.html We present a novel reasoning procedure for Horn SHIQ ontologies—SHIQ ontologies that can be translated to the Horn fragment of first-order logic. In contrast to traditional reasoning procedures for ontologies, our procedure does not build models or model representations, but works by deriving new consequent axioms. The procedure is closely related to the so-called completion-based procedure for EL++ ontologies, and can be regarded as an extension thereof. In fact, our procedure is theoretically optimal for Horn SHIQ ontologies as well as for the common fragment of EL++ and SHIQ. A preliminary empirical evaluation of our procedure on large medical ontologies demonstrates a dramatic improvement over existing ontology reasoners. Specifically, our implementation allows the classification of the largest available OWL version of Galen. To the best of our knowledge no other reasoner is able to classify this ontology. text: http://ijcai.org/papers09/Papers/IJCAI09-336.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:46 PART-X: WEB and KNOWLEDGE-BASED INFORMATION SYSTEMS: =337=> 2046p Efficient Estimation of Influence Functions for SIS Model on Social Networks, Masahiro Kimura, Kazumi Saito, Hiroshi Motoda, http://ijcai.org/papers09/Abstracts/337.html We address the problem of efficiently estimating the influence function of initially activated nodes in a social network under the susceptible / infected / susceptible (SIS) model, a diffusion model where nodes are allowed to be activated multiple times. The computational complexity drastically increases because of this multiple activation property. We solve this problem by constructing a layered graph from the original social network with each layer added on top as the time proceeds, and applying the bond percolation with a pruning strategy. We show that the computational complexity of the proposed method is much smaller than the conventional naive probabilistic simulation method by a theoretical analysis and confirm this by applying the proposed method to two real world networks. text: http://ijcai.org/papers09/Papers/IJCAI09-337.pdf ====================================================== PART-X: WEB and KNOWLEDGE-BASED INFORMATION SYSTEMS: =338=> 2051p Can Movies and Books Collaborate? Cross-Domain Collaborative Filtering for Sparsity Reduction, Bin Li, Qiang Yang, Xiangyang Xue, http://ijcai.org/papers09/Abstracts/338.html The sparsity problem in collaborative filtering (CF) is a major bottleneck for most CF methods. In this paper, we consider a novel approach for alleviating the sparsity problem in CF by transferring user-item rating patterns from a dense auxiliary rating matrix in other domains (e.g., a popular movie rating website) to a sparse rating matrix in a target domain (e.g., a new book rating website). We do not require that the users and items in the two domains be identical or even overlap. Based on the limited ratings in the target matrix, we establish a bridge between the two rating matrices at a cluster-level of user-item rating patterns in order to transfer more useful knowledge from the auxiliary task domain. We first compress the ratings in the auxiliary rating matrix into an informative and yet compact cluster-level rating pattern representation referred to as a codebook. Then, we propose an efficient algorithm for reconstructing the target rating matrix by expanding the codebook. We perform extensive empirical tests to show that our method is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary tasks, as compared to many state-of-the-art CF methods. text: http://ijcai.org/papers09/Papers/IJCAI09-338.pdf ====================================================== PART-X: WEB and KNOWLEDGE-BASED INFORMATION SYSTEMS: =339=> 2058p Using Web Photos for Measuring Video Frame Interestingness, Feng Liu, Y. Niu, M. Gleicher, http://ijcai.org/papers09/Abstracts/339.html In this paper, we present a method that uses web photos for measuring frame interestingness of a travel video. Web photo collections, such as those on Flickr, tend to contain interesting images because their images are more carefully taken, composed, and selected. Because these photos have already been chosen as subjectively interesting, they serve as evidence that similar images are also interesting. Our idea is to leverage these web photos to measure the interestingness of video frames. Specifically, we measure the interestingness of each video frame according to its similarity to web photos. The similarity is defined based on the scene content and composition. We characterize the scene content using scale invariant local features, specifically SIFT keypoints. We characterize composition by feature distribution. Accordingly, we measure the similarity between a web photo and a video frame based on the co-occurrence of the SIFT features, and the similarity between their spatial distribution. Interestingness of a video frame is measured by considering how many photos it is similar to, and how similar it is to them. Our experiments on measuring frame interestingness of videos from YouTube using photos from Flickr show the initial success of our method. http://ijcai.org/papers09/Papers/IJCAI09-339.pdf ====================================================== PART-X: WEB and KNOWLEDGE-BASED INFORMATION SYSTEMS: =340=> 2064p A Content-Based Method to Enhance Tag Recommendation, Yu-Ta Lu, Shoou-I Yu, Tsung-Chieh Chang, Jane Yung-jen Hsu, http://ijcai.org/papers09/Abstracts/340.html Tagging has become a primary tool for users to organize and share digital content on many social media sites. In addition, tag information has been shown to enhance capabilities of existing search engines. However, many resources on the web still lack tag information. This paper proposes a content-based approach to tag recommendation which can be applied to webpages with or without prior tag information. While social bookmarking service such as Delicious enables users to share annotated bookmarks, tag recommendation is available only for pages with tags specified by other users. Our proposed approach is motivated by the observation that similar webpages tend to have the same tags. Each webpage can therefore share the tags they own with similar webpages. The propagation of a tag depends on its weight in the originating webpage and the similarity between the sending and receiving webpages. The similarity metric between two webpages is defined as a linear combination of four cosine similarities, taking into account both tag information and page content. Experiments using data crawled from Delicious show that the proposed method is effective in populating untagged webpages with the correct tags. text: http://ijcai.org/papers09/Papers/IJCAI09-340.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:46 PART-X: WEB and KNOWLEDGE-BASED INFORMATION SYSTEMS: =341=> 2070p Conjunctive Query Answering in the Description Logic EL using a Relational Database System, Carsten Lutz, David Toman, David Toman, Frank Wolter, Frank Wolter, http://ijcai.org/papers09/Abstracts/341.html Conjunctive queries (CQ) are fundamental for accessing description logic (DL) knowledge bases. We study CQ answering in (extensions of) the DL EL, which is popular for large-scale ontologies and underlies the designated OWL2-EL profile of OWL2. Our main contribution is a novel approach to CQ answering that enables the use of standard relational database systems as the basis for query execution. We evaluate our approach using the IBM DB2 system, with encouraging results. text: http://ijcai.org/papers09/Papers/IJCAI09-341.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:46 PART-X: WEB and KNOWLEDGE-BASED INFORMATION SYSTEMS: =342=> 2076p Exploiting Background Knowledge to Build Reference Sets for Information Extraction, Matthew Michelson, Craig A. Knoblock, http://ijcai.org/papers09/Abstracts/342.html Previous work on information extraction from unstructured, ungrammatical text (e.g. classified ads) showed that exploiting a set of background knowledge, called a "reference set," greatly improves the precision and recall of the extractions. However, finding a source for this reference set is often difficult, if not impossible. Further, even if a source is found, it might not overlap well with the text for extraction. In this paper we present an approach to building the reference set directly from the text itself. Our approach eliminates the need to find the source for the reference set, and ensures better overlap between the text and reference set. Starting with a small amount of background knowledge, our technique constructs tuples representing the entities in the text to form a reference set. Our results show that our method outperforms manually constructed reference sets, since hand built reference sets may not overlap with the entities in the unstructured, ungrammatical text. We also ran experiments comparing our method to the supervised approach of Conditional Random Fields (CRFs) using simple, generic features. These results show our method achieves an improvement in F1-measure for 6/9 attributes and is competitive in performance on the others, and this is without training data. text: http://ijcai.org/papers09/Papers/IJCAI09-342.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:46 PART-X: WEB and KNOWLEDGE-BASED INFORMATION SYSTEMS: =343=> 2083p Large-Scale Taxonomy Mapping for Restructuring and Integrating Wikipedia, Simone Paolo Ponzetto, Roberto Navigli, http://ijcai.org/papers09/Abstracts/343.html We present a knowledge-rich methodology for disambiguating Wikipedia categories with WordNet synsets and using this semantic information to restructure a taxonomy automatically generated from the Wikipedia system of categories. We evaluate against a manual gold standard and show that both category disambiguation and taxonomy restructuring perform with high accuracy. Besides, we assess these methods on automatically generated datasets and show that we are able to effectively enrich WordNet with a large number of instances from Wikipedia. Our approach produces an integrated resource, thus bringing together the fine-grained classification of instances in Wikipedia and a well-structured top-level taxonomy from WordNet. text: http://ijcai.org/papers09/Papers/IJCAI09-343.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:46 PART-X: WEB and KNOWLEDGE-BASED INFORMATION SYSTEMS: =344=> 2089p Towards Ontology Learning from Folksonomies, Jie Tang, Ho-fung Leung, Qiong Luo, Dewei Chen, Jibin Gong, http://ijcai.org/papers09/Abstracts/344.html A folksonomy refers to a collection of user-defined tags with which users describe contents published on the Web. With the flourish of Web 2.0, folksonomies have become an important mean to develop the Semantic Web. Because tags in folksonomies are authored freely, there is a need to understand the structure and semantics of these tags in various applications. In this paper, we propose a learning approach to create an ontology that captures the hierarchical semantic structure of folksonomies. Our experimental results on two different genres of real world data sets show that our method can effectively learn the ontology structure from the folksonomies. text: http://ijcai.org/papers09/Papers/IJCAI09-344.pdf ====================================================== PART-X: WEB and KNOWLEDGE-BASED INFORMATION SYSTEMS: =345=> 2095p Streamlining Attacks on CAPTCHAs with a Computer Game, Jeff Yan, Su-Yang Yu, http://ijcai.org/papers09/Abstracts/345.html CAPTCHA has been widely deployed by commercial web sites as a security technology for purposes such as anti-spam. A common approach to evaluating the robustness of CAPTCHA is the use of machine learning techniques. Critical to this approach is the acquisition of an adequate set of labeled samples, on which the learning techniques are trained. However, such a sample labeling task is difficult for computers, since the strength of CAPTCHAs stems exactly from the difficulty computers have in recognizing either distorted texts or image contents. Therefore, until now, researchers have to manually label their samples, which is tedious and expensive. In this paper, we present Magic Bullet, a computer game that for the first time turns such sample labeling into a fun experience, and that achieves a labeling accuracy of as high as 98% for free. The game leverages human computation to address a task that cannot be easily automated, and it effectively streamlines the evaluation of CAPTCHAs. The game can also be used for other constructive purposes such as 1) developing better machine learning algorithms for handwriting recognition, and 2) training people’s typing skills. text: http://ijcai.org/papers09/Papers/IJCAI09-345.pdf ====================================================== PART-X: WEB and KNOWLEDGE-BASED INFORMATION SYSTEMS: =346=> 2101p Incorporating User Behaviors in New Word Detection, Yabin Zheng, Zhiyuan Liu, Maosong Sun, Liyun Ru, Yang Zhang, http://ijcai.org/papers09/Abstracts/346.html In this paper, we proposed a novel method to detect new words in domain-specific fields based on user behaviors. First, we select the most representative words from domain-specific lexicon. Then combining with user behaviors, we try to discover the potential experts in this field who use those terminologies frequently. Finally, we make further efforts to identify new words from behaviors of those experts. Words used much more frequently in this community than others are most probably new words. In brief, our method follows a collaborative filtering way: first from words to find professional experts, then from experts to discover new words, which is different from the traditional new word detection methods. Our method achieves up to 0.86 in accuracy on a computer science related data set. Moreover, the proposed method can be easily extended to related words retrieval task. We compare our method with Google Sets and Bayesian Sets. Experiments show that our method and Bayesian Sets gives better results than Google Sets. text: http://ijcai.org/papers09/Papers/IJCAI09-346.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:46 PART-X: WEB and KNOWLEDGE-BASED INFORMATION SYSTEMS [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:47 PART-X: WEB and KNOWLEDGE-BASED INFORMATION SYSTEMS... [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:47 [Ответ][Цитата]
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