OpenRaiser/Intern-Atlas huggingface dataset:
Finding similar pages in a social tagging repository
Authors: Alex Penev, Raymond K. Wong
Venue: WWW
Year: 2008

Social tagging describes a community of users labeling web content with tags. It is a simple activity that enriches our knowledge about resources on the web. For a computer to help users search the tagged repository, it must know when tags are good or bad. We describe TagScore, a scoring function that rates the goodness of tags. The tags and their ratings give us a succinct synopsis for a page. We `find similar' pages in Del.icio.us by comparing synopses. Our approach gives good correlation to the full cosine similarity but is hundreds of times faster.
Learning to classify short and sparse text & web with hidden topics from large-scale data collections
Authors: Xuan Hieu Phan, Le-Minh Nguyen, Susumu Horiguchi
Venue: WWW
Year: 2008

This paper presents a general framework for building classifiers that deal with short and sparse text & segments by making the most of hidden topics discovered from large-scale data collections. The main motivation of this work is that many classification tasks working with short segments of text & Web, such as search snippets, forum & chat messages, blog & news feeds, product reviews, and book & movie summaries, fail to achieve high accuracy due to the data sparseness. We, therefore, come up with an idea of gaining external knowledge to make the data more related as well as expand the coverage of classifiers to handle future data better. The underlying idea of the framework is that for each classification task, we collect a large-scale external data collection called universal dataset, and then build a classifier on both a (small) set of labeled training data and a rich set of hidden topics discovered from that data collection. The framework is general enough to be applied to different data domains and genres ranging from search results to medical text. We did a careful evaluation on several hundred megabytes of Wikipedia (30M words) and MEDLINE (18M words) with two tasks: Web search domain disambiguation and disease categorization for medical text, and achieved significant quality enhancement.
Query-sets: using implicit feedback and query patterns to organize web documents
Authors: Barbara Poblete, Ricardo Baeza-Yates
Venue: WWW
Year: 2008

In this paper we present a new document representation based on implicit user feedback obtained from search engine queries. The main objective of this is to achieve better results in non-supervised tasks, such as clustering and labeling, through the incorporation of usage data obtained from search engine queries. This type of allows us to discover the motivations of users when visiting a certain document. The terms used in queries can provide a better choice of features, from the user's point of view, for summarizing the Web pages that were clicked from these queries. In this work we extend and formalize as model an existing but not very well known idea of view for document representation. Furthermore, we create a novel based on frequent query patterns called the model. Our evaluation shows that both query-based models outperform the vector-space when used for clustering and labeling documents in a website. In our experiments, the query-set reduces by more than 90% the number of features needed to represent a set of documents and improves by over 90% the quality of the results. We believe that this can be explained because our chooses better features and provides more accurate labels according to the user's expectations.
Enhanced hierarchical classification via isotonic smoothing
Authors: Kunal Punera, Joydeep Ghosh
Venue: WWW
Year: 2008

Hierarchical topic taxonomies have proliferated on the World Wide Web [5, 18], and exploiting the output space decompositions they induce in automated classification systems is an active area of research. In many domains, classifiers learned on a hierarchy of classes have been shown to outperform those learned on a flat set of classes. In this paper we argue that the hierarchical arrangement of classes leads to intuitive relationships between the corresponding classifiers' output scores, and that enforcing these relationships as a post-processing step after classification can improve its accuracy. We formulate the task of smoothing classifier outputs as a regularized isotonic tree regression problem, and present a dynamic programming based method that solves it optimally. This new problem generalizes the classic isotonic tree regression problem, and both, the new formulation and algorithm, might be of independent interest. In our empirical analysis of two real-world text classification scenarios, we show that our approach to smoothing classifier outputs results in improved classification accuracy.
Learning to rank relational objects and its application to web search
Authors: Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang, Wen-Ying Xiong, Hang Li
Venue: WWW
Year: 2008

Learning to rank is a new statistical learning technology on creating a ranking model for sorting objects. The technology has been successfully applied to web search, and is becoming one of the key machineries for building search engines. Existing approaches to learning to rank, however, did not consider the cases in which there exists relationship between the objects to be ranked, despite of the fact that such situations are very common in practice. For example, in web search, given a query certain relationships usually exist among the the retrieved documents, e.g., URL hierarchy, similarity, etc., and sometimes it is necessary to utilize the information in ranking of the documents. This paper addresses the issue and formulates it as a novel learning problem, referred to as, 'learning to rank relational objects'. In the new learning task, the ranking model is defined as a function of not only the contents (features) of objects but also the relations between objects. The paper further focuses on one setting of the learning problem in which the way of using relation information is predetermined. It formalizes the learning task as an optimization problem in the setting. The paper then proposes a new method to perform the optimization task, particularly an implementation based on SVM. Experimental results show that the proposed method outperforms the baseline methods for two ranking tasks (Pseudo Relevance Feedback and Topic Distillation) in web search, indicating that the proposed method can indeed make effective use of relation information and content information in ranking.
Which "Apple" are you talking about ?
Authors: Mandar Rahurkar, Dan Roth, Thomas S. Huang
Venue: WWW
Year: 2008

In a higher level task such as clustering of web results or
Graph theoretical framework for simultaneously integrating visual and textual features for efficient web image clustering
Authors: Manjeet Rege, Ming Dong, Jing Hua
Venue: WWW
Year: 2008

With the explosive growth of Web and the recent development in digital media technology, the number of images on the Web has grown tremendously. Consequently, Web image clustering has emerged as an important application. Some of the initial efforts along this direction revolved around clustering Web images based on the visual features of images or textual features by making use of the text surrounding the images. However, not much work has been done in using multimodal information for clustering Web images. In this paper, we propose a graph theoretical framework for simultaneously integrating visual and textual features for efficient Web image clustering. Specifically, we model visual features, images and words from surrounding text using a tripartite graph. Partitioning this graph leads to clustering of the Web images. Although, graph partitioning approach has been adopted before, the main contribution of this work lies in a new algorithm that we propose- Consistent Isoperimetric High-order Co-clustering (CIHC), for partitioning the tripartite graph. Computationally, CIHC is very quick as it requires a simple solution to a sparse system of linear equations. Our theoretical analysis and extensive experiments performed on real Web images demonstrate the performance of CIHC in terms of the quality, efficiency and scalability in partitioning the visual feature-image-word tripartite graph.
Wishful search: interactive composition of data mashups
Authors: Anton Riabov, Eric Bouillet, Mark Feblowitz, Zhen Liu, Anand Ranganathan
Venue: WWW
Year: 2008

With the emergence of Yahoo Pipes and several similar services, data mashup tools have started to gain interest of business users. Making these tools simple and accessible ton users with no or little programming experience has become a pressing issue. In this paper we introduce MARIO (Mashup Automation with Runtime Orchestration and Invocation), a new tool that radically simplifies data mashup composition. We have developed an intelligent automatic composition engine in MARIO together with a simple user interface using an intuitive "wishful search" abstraction. It thus allows users to explore the space of potentially composable data mashups and preview composition results as they iteratively refine their "wishes", i.e. mashup composition goals. It also lets users discover and make use of system capabilities without having to understand the capabilities of individual components, and instantly reflects changes made to the components by presenting an aggregate view of changed capabilities of the entire system. We describe our experience with using MARIO to compose flows of Yahoo Pipes components.
Characterizing typical and atypical user sessions in clickstreams
Authors: Narayanan Sadagopan, Jie Li
Venue: WWW
Year: 2008

Millions of users retrieve information from the Internet using search engines. Mining these user sessions can provide valuable information about the quality of user experience and the perceived quality of search results. Often search engines rely on accurate estimates of Click Through Rate (CTR) to evaluate the quality of user experience. The vast heterogeneity in the user population and presence of automated software programs (bots) can result in high variance in the estimates of CTR. To improve the estimation accuracy of user experience metrics like CTR, we argue that it is important to identify typical and atypical user sessions in clickstreams. Our approach to identify these sessions is based on detecting outliers using Mahalanobis distance in the user session space. Our user session model incorporates several key clickstream characteristics including a novel conformance score obtained by Markov Chain analysis. Editorial results show that our approach of identifying typical and atypical sessions has a precision of about 89%. Filtering out these atypical sessions reduces the uncertainty (95% confidence interval) of the mean CTR by about 40%. These results demonstrate that our approach of identifying typical and atypical user sessions is extremely valuable for cleaning "noisy" user session data for increased accuracy in evaluating user experience.
Gsp-exr: gsp protocol with an exclusive right for keyword auctions
Authors: Yuko Sakurai, Atsushi Iwasaki, Yasumasa Saito, Makoto Yokoo
Venue: WWW
Year: 2008

We propose a keyword auction protocol called the Generalized Second Price with an Exclusive Right (GSP-ExR). In existing keyword auctions, the number of displayed advertisements is determined in advance. Thus, we consider adjusting the number of advertisements dynamically based on bids. In the GSP-ExR, the number of slots can be either 1 or K. When K slots are displayed, the protocol is identical to the GSP. If the value per click of the highest ranked bidder is large enough, then this bidder can exclusively display her advertisement by paying a premium. Thus, this pricing scheme is relatively simple and seller revenue is at least as good as the GSP. Also, in the GSP-ExR, the highest ranked bidder has no incentive to change the number of slots by over/under-bidding as long as she retains the top position.
Determining user's interest in real time
Authors: Sanasam Ranbir Singh, Hema A. Murthy, Timothy A. Gonsalves
Venue: WWW
Year: 2008

Most of the search engine optimization techniques attempt to predict users interest by learning from the past information collected from different sources. But, a user's current interest often depends on many factors which are not captured in the past information. In this paper, we attempt to identify user's current interest in real time from the information provided by the user in the current query session. By identifying user's interest in real time, the engine could adapt differently to different users in real time. Experimental verification indicates that our approach is encouraging for short queries
Networked graphs: a declarative mechanism for SPARQL rules, SPARQL views and RDF data integration on the web
Authors: Simon Schenk, Steffen Staab
Venue: WWW
Year: 2008

Easy reuse and integration of declaratively described information in a distributed setting is one of the main motivations for building the Semantic Web. Despite of this claim, reuse and recombination of RDF data today is mostly done using data replication and procedural code. A simple declarative mechanism for reusing and combining RDF data would help users to generate content for the semantic web. Having such a mechanism, the Semantic Web could better benefit from user generated content, as it is broadly present in the so called Web 2.0, but also from better linkage of existing content.We propose Networked Graphs, which allow users to define RDF graphs both, by extensionally listing content, but also by using views on other graphs. These views can be used to include parts of other graphs, to transform data before including it and to denote rules. The relationships between graphs are described declaratively using SPARQL queries and an extension of the SPARQL semantics. Networked Graphs are easily exchangeable between and interpretable on different computers. Using existing protocols, Networked Graphss can be evaluated in a distributed setting.
Querying for meta knowledge
Authors: Bernhard Schueler, Sergej Sizov, Steffen Staab, Duc Thanh Tran
Venue: WWW
Year: 2008

The Semantic Web is based on accessing and reusing RDF data from many different sources, which one may assign different levels of authority and credibility. Existing Semantic Web query languages, like SPARQL, have targeted the retrieval, combination and reuse of facts, but have so far ignored all aspects of meta knowledge, such as origins, authorship, recency or certainty of data, to name but a few. In this paper, we present an original, generic, formalized and implemented approach for managing many dimensions of meta knowledge, like source, authorship, certainty and others. The approach re-uses existing RDF modeling possibilities in order to represent meta knowledge. Then, it extends SPARQL query processing in such a way that given a SPARQL query for data, one may request meta knowledge without modifying the query proper. Thus, our approach achieves highly flexible and automatically coordinated querying for data and meta knowledge, while completely separating the two areas of concern. 1.
Circular context-based semantic matching to identify web service composition
Authors: Aviv Segev
Venue: WWW
Year: 2008

This paper provides initial analysis in identifying possible Web services composition using context-based semantic matching. Context-based semantic matching allows service composition to be expanded beyond simple term matching and reduces erroneous compositions. A common method of context extraction is used to compare two types of service description, textual and WSDL. The approach is unique since it provides the Web service designer with an explicit numeric estimation of the extent to which a composition "makes sense." Motivation for the work is displayed with examples from Web services in the field of business.
International Workshop on Context Enabled Source and Service Selection, Integration and Adaptation: workshop summary
Authors: Quan Z. Sheng, Ullas Nambiar, Amit P. Sheth, Biplav Srivastava, Zakaria Maamar, Said Elnaffar
Venue: WWW
Year: 2008

This write-up provides a summary of the International Workshop on Context enabled Source and Service Selection, Integration and Adaptation (CSSSIA 2008), organized in conjunction with WWW 2008, at Beijing, China on April 22nd 2008. We outline the motivation for organizing the workshop, briefly describe the organizational details and program of the workshop, and summarize each of the papers accepted by the workshop. More information about the workshop can be found at http://www.cs.adelaide.edu.au/~csssia08/.
WS3: international workshop on context-enabled source and service selection, integration and adaptation (CSSSIA 2008)
Authors: Quan Z. Sheng, Ullas Nambiar, Amit P. Sheth, Biplav Srivastava, Zakaria Maamar, Said Elnaffar
Venue: WWW
Year: 2008

This write-up provides a summary of the International Workshop on Context enabled Source and Service Selection, Integration and Adaptation (CSSSIA 2008), organized in conjunction with WWW 2008, at Beijing, China on April 22nd 2008. We outline the motivation for organizing the workshop, briefly describe the organizational details and program of the workshop, and summarize each of the papers accepted by the workshop. More information about the workshop can be found at http://www.cs.adelaide.edu.au/~csssia08/.
Collaborative knowledge semantic graph image search
Authors: Jyh-Ren Shieh, Yang-Ting Yeh, Chih-Hung Lin, Ching-Yung Lin, Ja-Ling Wu
Venue: WWW
Year: 2008

In this paper, we propose a Collaborative Knowledge Semantic Graphs Image Search (CKSGIS) system. It provides a novel way to conduct image search by utilizing the collaborative nature in Wikipedia and by performing network analysis to form semantic graphs for search-term expansion. The collaborative article editing process used by Wikipedia's contributors is formalized as bipartite graphs that are folded into networks between terms. When a user types in a search term, CKSGIS automatically retrieves an interactive semantic graph of related terms that allow users to easily find related images not limited to a specific search term. Interactive semantic graph then serve as an interface to retrieve images through existing commercial search engines. This method significantly saves users' time by avoiding multiple search keywords that are usually required in generic search engines. It benefits both naïve users who do not possess a large vocabulary and professionals who look for images on a regular basis. In our experiments, 85% of the participants favored CKSGIS system rather than commercial search engines.
Keysurf: a character controlled browser for people with physical disabilities
Authors: Leo Spalteholz, Kin Fun Li, Nigel Livingston, Foad Hamidi
Venue: WWW
Year: 2008

For many users with a physical or motor disability, using a computer mouse or other pointing device to navigate the web is cumbersome or impossible due to problems with pointing accuracy. At the same time, web accessibility using a keyboard in major browsers is rudimentary, requiring many key presses to select links or other elements. We introduce KeySurf, a character controlled web navigation system which addresses this situation by presenting an interface which allows a user to activate any web page element with only two or three keystrokes. Through an implementation of a user-centric incremental search algorithm, elements are matched according to user expectation as characters are entered into the interface. We show how our interface can be integrated with a speech recognition input, as well as with specialized on-screen keyboards for people with disabilities. Using the user's browsing history, we improve the efficiency of the selection process and find potentially interesting page links for the user within the current web page. We present the results from a pilot study evaluating the performance of various components of our system.
Mining, indexing, and searching for textual chemical molecule information on the web
Authors: Bingjun Sun, Prasenjit Mitra, C. Lee Giles
Venue: WWW
Year: 2008

Current search engines do not support user searches for chemical entities (chemical names and formulae) beyond simple keyword searches. Usually a chemical molecule can be represented in multiple textual ways. A simple keyword search would retrieve only the exact match and not the others. We show how to build a search engine that enables searches for chemical entities and demonstrate empirically that it improves the relevance of returned documents. Our search engine first extracts chemical entities from text, performs novel indexing suitable for chemical names and formulae, and supports different query models that a scientist may require. We propose a model of hierarchical conditional random fields for chemical formula tagging that considers long-term dependencies at the sentence level. To substring searches of chemical names, a search engine must index substrings of chemical names. Indexing all possible sub-sequences is not feasible in practice. We propose an algorithm for independent frequent subsequence mining to discover sub-terms of chemical names with their probabilities. We then propose an unsupervised hierarchical text segmentation (HTS) method to represent a sequence with a tree structure based on discovered independent frequent subsequences, so that sub-terms on the HTS tree should be indexed. Query models with corresponding ranking functions are introduced for chemical name searches. Experiments show that our approaches to chemical entity tagging perform well. Furthermore, we show that index pruning can reduce the index size and query time without changing the returned ranked results significantly. Finally, experiments show that our approaches out-perform traditional methods for document search with ambiguous chemical terms.
Behavioral classification on the click graph
Authors: Martin Szummer, Nick Craswell
Venue: WWW
Year: 2008

A bipartite query-URL graph, where an edge indicates that a document was clicked for a query, is a useful construct for finding groups of related queries and URLs. Here we use this behavior graph for classification. We choose a click graph sampled from two weeks of image search activity, and the task of "adult" filtering: identifying content in the graph that is inappropriate for minors. We show how to perform classification using random walks on this graph, and two methods for estimating classifier parameters.
Using CEP technology to adapt messages exchanged by web services
Authors: Yehia Taher, Marie-Christine Fauvet, Marlon Dumas, Djamal Benslimane
Venue: WWW
Year: 2008

Web service may be unable to interact with each other because of incompatibilities between their interfaces. In this paper, we present an event driven approach which aims at adapting messages exchanged during service interactions. The proposed framework relies on Complex Event Processing (CEP) technology, which provides an environment for the development of applications that need to continuously process, analyse and respond to event streams. Our main contribution is a system that enables developers to design and implement CEP-based adapters. These latter are deployed in a CEP engine which is responsible for continuously receiving messages and processing them according to rules implemented by the adapters. Resulting transformed messages are thus forwarded to their original service recipient.