OpenRaiser/Intern-Atlas huggingface dataset:
PruSM: a prudent schema matching approach for web forms
Authors: Thanh Hoang Nguyen, Hoa Nguyen, Juliana Freire
Venue: CIKM
Year: 2010

There has been a substantial increase in the number of Web data sources whose contents are hidden and can only be accessed through form interfaces. To leverage this data, several applications have emerged that aim to automate and simplify the access to these data sources, from hidden-Web crawlers and meta-searchers to Web information integration systems. A requirement shared by these applications is the ability to understand these forms, so that they can automatically fill them out. In this paper, we address a key problem in form understanding: how to match elements across distinct forms. Although this problem has been studied in the literature, existing approaches have important limitations. Notably, they only handle small form collections and assume that form elements are clean and normalized, often through manual pre-processing. When a large number of forms is automatically gathered, matching form schemata presents new challenges: data heterogeneity is compounded with the Web-scale and noise introduced by automated processes. We propose PruSM, a prudent schema matching strategy the determines matches for form elements in a prudent fashion, with the goal of minimizing error propagation. A experimental evaluation of PruSM using widely available data sets shows that the approach effective and able to accurately match a large number of form schemata and without requiring any manual pre-processing.
Experiences with using SVM-based learning for multi-objective ranking
Authors: Linh Thai Nguyen, Wai Gen Yee, Roger Liew, Ophir Frieder
Venue: CIKM
Year: 2010

We describe our experiences in applying learning-to-rank techniques to improving the quality of search results of an online hotel reservation system. The search result quality factors we use are average booking position and distribution of margin in top-ranked results. (We expect that total revenue will increase with these factors.) Our application of the SVMRank technique improves booking position by up to 25% and margin distribution by up to 14%.
PIKM 2010: ACM workshop for ph.d. students in information and knowledge management
Authors: Anisoara Nica, Aparna S. Varde
Venue: CIKM
Year: 2010

The PIKM workshop focuses on papers consisting mainly of the Ph.D. dissertation proposals of doctoral students. A wide range of topics on any area in databases, information retrieval and knowledge management are presented at this workshop. The areas of interest are similar to those at the CIKM main conference in the three respective tracks. Interdisciplinary work across these tracks is encouraged.
Hierarchical auto-tagging: organizing Q&A knowledge for everyone
Authors: Kyosuke Nishida, Ko Fujimura
Venue: CIKM
Year: 2010

We propose a hierarchical auto-tagging system, TagHats, to improve users' knowledge sharing. Our system assigns three different levels of tags to Q&A documents: category, theme, and keyword. Multiple category tags can organize a document according to multiple viewpoints, and multiple theme and keyword tags can identify what the document is about clearly. Moreover, these hierarchical tags will be helpful in organizing documents to support everyone because different users have different demands in terms of tag specificity. Our system consists of a hierarchical classification method for assigning category and theme tags, a new keyword extraction method that considers the structure of Q&A documents, and a new method for selecting theme tag candidates from each category. Experiments with the documents of Oshiete! goo demonstrate that our system is able to assign hierarchical tags to the documents appropriately and is capable of outperforming baseline methods significantly.
Improving taxonomies for large-scale hierarchical classifiers of web documents
Authors: Kiyoshi Nitta
Venue: CIKM
Year: 2010

We focused on taxonomy modification algorithms for gradually improving the relevance performances of large-scale hierarchical classifiers of web documents. Considering the research results of Tang et al. [5,4], who took the same approach, we investigated and implemented two heuristic taxonomy modification algorithms for performing practical classification processes for large-scale taxonomies. Although a taxonomy modification algorithm continuously improves the relevance performances of hierarchical classifiers, it increases the computational costs of those classifiers for training and predicting processes. We developed an improved taxonomy modification algorithm for reducing computational costs by preventing child node concentration. Although the relevance performances of the algorithm-modified taxonomy classifiers improved without increasing computational costs until the fourth generation by spreading the set of predicted classes, their relevance performances and behaviors went in opposite directions from the fifth generation.
Two-tier similarity model for story link detection
Authors: Tadashi Nomoto
Venue: CIKM
Year: 2010

The paper presents a novel approach to story link detection, where the goal is to determine whether a pair of news stories are linked, i.e., talk about the same event. The present work marks a departure from the prior work in that we measure similarity at two distinct levels of textual organization, the document and its collection, and combine scores at both levels to determine how well stories are linked. Experiments on the TDT-5 corpus show that the present approach, which we call a 'two-tier similarity model,' comfortably beats conventional approaches such as Clarity enhanced KL divergence, while performing robustly across diverse languages.
FD-buffer: a buffer manager for databases on flash disks
Authors: Sai Tung On, Yinan Li, Bingsheng He, Ming Wu, Qiong Luo, Jianliang Xu
Venue: CIKM
Year: 2010

We design and implement FD-Buffer, a buffer manager for database systems running on flash-based disks. Unlike magnetic disks, flash media has an inherent read-write asymmetry: writes involve expensive erase operations and as a result are usually much slower than reads. Therefore, we address this asymmetry in FD-Buffer. Specifically, we use the average I/O cost per page access as opposed to the traditional miss rate as the performance metric for a buffer. We develop a new replacement policy in which we separate clean and dirty pages into two pools. The size ratio of the two pools is automatically adapted to the read-write asymmetry and the runtime workload. We evaluate FD-Buffer with trace-driven experiments on real flash disks. Our evaluation results show that our algorithm achieves up to 33% improvement on the overall performance on commodity flash disks, in comparison with the state-of-the-art flash-aware replacement policy.
DOLAP 2010 workshop summary
Authors: Carlos Ordonez, Il-Yeol Song
Venue: CIKM
Year: 2010

The ACM DOLAP workshop presents research on data warehousing and On-Line Analytical Processing (OLAP). The program has three interesting sessions on modeling, query processing and new trends, as well as a keynote talk on OLAP query processing and a panel comparing relational and non-relational technology for data warehousing.
Expert identification in community question answering: exploring question selection bias
Authors: Aditya Pal, Joseph A. Konstan
Venue: CIKM
Year: 2010

Community Question Answering (CQA) services enables users to ask and answer questions. In these communities, there are typically a small number of experts amongst the large population of users. We study which questions a user select for answering and show that experts prefer answering questions where they have a higher chance of making a valuable contribution. We term this preferential selection as question selection bias and propose a mathematical model to estimate it. Our results show that using Gaussian classification models we can effectively distinguish experts from ordinary users over their selection biases. In order to estimate these biases, only a small amount of data per user is required, which makes an early identification of expertise a possibility. Further, our study of bias evolution reveals that they do not show significant changes over time indicating that they emanates from the intrinsic characteristics of users.
Tagging for improved semantic interpretation of XML
Authors: Vicente Palacios, Juan Lloréns, Sonia Sánchez-Cuadrado, Mónica Marrero
Venue: CIKM
Year: 2010

The development of the Semantic Web depends on agreed and unambiguous knowledge representations, on the availability and accessibility of knowledge, and on retrieval capabilities. The scarce agreement on knowledge representation and the lack of techniques to process semantic structures in web search engines makes it impossible to perform contextualized conceptual retrieval. These limitations imply that users must know beforehand the existence and location of this knowledge to be able to retrieve it. Thus, different ad-hoc knowledge representations and metadata vocabularies, scarcely formalized and agreed on, have been published, hindering the reuse and interoperability. Our proposal has as its main goal the elaboration of different views to improve the representation of semantic documents. This approach facilitates the management and retrieval of heterogeneous semantic schemas by means of a multilevel ontological structure and the alignment with a reference ontology that provides conceptual retrieval and reuse of knowledge.
Evaluating, combining and generalizing recommendations with prerequisites
Authors: Aditya G. Parameswaran, Hector Garcia-Molina, Jeffrey D. Ullman
Venue: CIKM
Year: 2010

We consider the problem of recommending the best set of k items when there is an inherent ordering between items, expressed as a set of prerequisites (e.g., the movie 'Godfather I' is a prerequisite of 'Godfather II'). Since this general problem is computationally intractable, we develop 3 approximation algorithms to solve this problem for various prerequisite structures (e.g., chain graphs, AND graphs, AND-OR graphs). We derive worst-case bounds for these algorithms for these structures, and experimentally evaluate these algorithms on synthetic data. We also develop an algorithm to combine solutions in order to generate even better solutions, and compare the performance of this algorithm with the other three.
Probabilistic ranking for relational databases based on correlations
Authors: Jaehui Park, Sang-goo Lee
Venue: CIKM
Year: 2010

This paper proposes a ranking method to exploit statistical correlations among pairs of attribute values in relational databases. For a given query, the correlations of the query are aggregated with each of the attribute values in a tuple to estimate the relevance of that tuple to the query. We extend Bayesian network models to provide a probabilistic ranking function based on a limited assumption of value independence. Experimental results show that our model improves the retrieval effectiveness on real datasets and has a reasonable query processing time compared to related work.
An efficient data-centric storage scheme considering storage and query hot-spots in sensor networks
Authors: Yonghun Park, Dongmin Seo, Jonghyeon Yun, Christopher T. Ryu, Jaesoo Yoo
Venue: CIKM
Year: 2010

In wireless sensor networks, various schemes have been proposed to efficiently store and process sensed data. Among them, the Data-Centric Storage (DCS) scheme is one of the most well-known. The DCS scheme distributes data regions and stores the data in the sensor that is responsible for the region. In this paper, we propose a new DCS based scheme, called Time-Parameterized Data-Centric Storage (TPDCS), that avoids the problems of storage hot-spots and query hotspots. To decentralize the skewed data and queries, the data regions are assigned by a time dimension as well as data dimensions in our proposed scheme. Therefore, TPDCS extends the lifetime of sensor networks. It is shown through various experiments that our scheme outperforms the existing schemes.
BagBoo: a scalable hybrid bagging-the-boosting model
Authors: Dmitry Yurievich Pavlov, Alexey Gorodilov, Cliff A. Brunk
Venue: CIKM
Year: 2010

In this paper, we introduce a novel machine learning approach for regression based on the idea of combining bagging and boosting that we call BagBoo. Our BagBoo model borrows its high accuracy potential from. Friedman's gradient boosting [2], and high efficiency and scalability through parallelism from Breiman's bagging [1]. We run empirical evaluations on large scale Web ranking data, and demonstrate that BagBoo is not only showing superior relevance than standalone bagging or boosting, but also outperforms most previously published results on these data sets. We also emphasize that BagBoo is intrinsically scalable and parallelizable, allowing us to train order of half a million trees on 200 nodes in 2 hours CPU time and beat all of the competitors in the Internet Mathematics relevance competition sponsored by Yandex and be one of the top algorithms in both tracks of Yahoo ICML-2010 challenge. We conclude the paper by stating that while impressive experimental evaluation results are presented here in the context of regression trees, the hybrid BagBoo model is applicable to other domains, such as classification, and base training models.
A decision support system for green data centers
Authors: Michael J. Pawlish, Aparna S. Varde
Venue: CIKM
Year: 2010

In this paper, we propose a decision support system for green computing in data centers on campuses. Green computing aims at the development of technologies for a greener and more sustainable planet. In this work we focus on the greening of data centers that house servers on campuses. It is known that servers consume a huge amount of power and incur other costs in cooling and related operations. It is challenging to meet the demands of efficiency and accuracy in data centers while yet promoting a greener environment along with cost cutting. Our work proposes a decision support system based on decision trees and case-based reasoning that mines existing data in order to assist decision-making for better management in data centers. We consider various aspects of the data from an environmental management perspective such as carbon footprint, thermal profiles and virtualization. Issues such as efficiency and accuracy are taken into account here, In this proposal paper, we outline the need for our work, the challenges involved, the proposed approach for building the system, preliminary evaluation and future plans. This work would be of interest to the data and knowledge management community as well as environmental scientists and researchers in related areas.
Collaborative filtering in social tagging systems based on joint item-tag recommendations
Authors: Jing Peng, Daniel Dajun Zeng, Huimin Zhao, Fei-Yue Wang
Venue: CIKM
Year: 2010

Tapping into the wisdom of the crowd, social tagging can be considered an alternative mechanism - as opposed to Web search - for organizing and discovering information on the Web. Effective tag-based recommendation of information items, such as Web resources, is a critical aspect of this social information discovery mechanism. A precise understanding of the information structure of social tagging systems lies at the core of an effective tag-based recommendation method. While most of the existing research either implicitly or explicitly assumes a simple tripartite graph structure for this purpose, we propose a comprehensive information structure to capture all types of co-occurrence information in the tagging data. Based on the proposed information structure, we further propose a unified user profiling scheme to make full use of all available information. Finally, supported by our proposed user profile, we propose a novel framework for collaborative filtering in social tagging systems. In our proposed framework, we first generate joint item-tag recommendations, with tags indicating topical interests of users in target items. These joint recommendations are then refined by the wisdom from the crowd and projected to the item space for final item recommendations. Evaluation using three real-world datasets shows that our proposed recommendation approach significantly outperformed state-of-the-art approaches.
Visual-semantic graphs: using queries to reduce the semantic gap in web image retrieval
Authors: Barbara Poblete, Benjamin Bustos, Marcelo Mendoza, Juan Manuel Barrios
Venue: CIKM
Year: 2010

We explore the application of a graph representation to model similarity relationships that exist among images found on the Web. The resulting similarity-induced graph allows us to model in a unified way different types of content-based similarities, as well as semantic relationships. Content-based similarities include different image descriptors, and semantic similarities can include relevance user feedback from search engines. The goal of our representation is to provide an experimental framework for combining apparently unrelated metrics into a unique graph structure, which allows us to enhance the results of Web image retrieval. We evaluate our approach by re-ranking Web image search results.
Online annotation of text streams with structured entities
Authors: Ken Q. Pu, Oktie Hassanzadeh, Richard Drake, Renée J. Miller
Venue: CIKM
Year: 2010

We propose a framework and algorithm for annotating unbounded text streams with entities of a structured database. The algorithm allows one to correlate unstructured and dirty text streams from sources such as emails, chats and blogs, to entities stored in structured databases. In contrast to previous work on entity extraction, our emphasis is on performing entity annotation in a completely online fashion. The algorithm continuously extracts important phrases and assigns to them top-k relevant entities. Our algorithm does so with a guarantee of constant time and space complexity for each additional word in the text stream, thus infinite text streams can be annotated. Our framework allows the online annotation algorithm to adapt to changing stream rate by self-adjusting multiple run-time parameters to reduce or improve the quality of annotation for fast or slow streams, respectively. The framework also allows the online annotation algorithm to incorporate query feedback to learn the user preference and personalize the annotation for individual users.
The anatomy of a click: modeling user behavior on web information systems
Authors: Kunal Punera, Srujana Merugu
Venue: CIKM
Year: 2010

The ultimate goal of information retrieval science continues to be providing relevant information to users while placing minimal cognitive load on them. The retrieval and presentation of relevant information (say, search results) as well as any dynamic system behavior (e.g., search engine re-ranking) depends acutely on estimating user intent. Hence, it is critical to use all the available information about user behavior at any stage of a search-session to accurately infer the user intent. However, the simplistic interfaces provided by search engines in order to minimize the user cognitive effort, and intrinsic limits imposed by privacy concerns, latency requirements, and other web instrumentation challenges, result in only a subset of user actions that are predictive of the search intent being captured. In this paper, we present a dynamic Bayesian network (DBN) that models user interaction with general web information systems, taking into account both observed (clicks etc.) as well as hidden (result examinations etc.) user actions. Our model goes beyond the ranked list information access paradigm and gives a solution where arbitrary context information can be incorporated in a principled fashion. To account for heterogeneity in user behavior as well as information access tasks, we further propose a bi-clustering algorithm that partitions users and tasks, and learns separate models for each bicluster. We instantiate this general DBN model for a typical static search interface comprising of a single query box and a ranked list of search results using a set of seven common user actions and various predictive state attributes. Experimental results on real-world web search log data indicate that one can obtain superior predictive performance on various session properties (such as click positions and reformulations) compared to simpler instantiations of the DBN.
Choosing your own adventure: automatic taxonomy generation to permit many paths
Authors: Xiaoguang Qi, Dawei Yin, Zhenzhen Xue, Brian D. Davison
Venue: CIKM
Year: 2010

A taxonomy organizes concepts or topics in a hierarchical structure and can be created manually or via automated systems. A major drawback of taxonomies is that they require users to have the same view of the topics as the taxonomy creator. Users who do not share that mental taxonomy are likely to have difficulty in finding the desired topic. In this paper, we propose a new approach to taxonomy expansion which is able to provide more flexible views. Based on an existing taxonomy, our algorithm finds possible alternative paths and generates an expanded taxonomy with flexibility in user browsing choices. In experiments on the dmoz Open Directory Project, the rebuilt taxonomies provide more alternative paths and shorter paths to information. User studies show that our expanded taxonomies are preferred compared to the original
How about utilizing ordinal information from the distribution of unlabeled data
Authors: Mingjie Qian, Bo Chen, Hongzhi Xu, Hongwei Qi
Venue: CIKM
Year: 2010

Problems of ordinal regression arise in many fields such as information retrieval, data mining and knowledge management. In this paper, we consider ordinal regression in a semi-supervised scenario, i.e., we try to utilize the ordinal information from the distribution of unlabeled data. Semi-supervised ordinal regression is more applicable than traditional supervised ordinal regression, because nowadays labeled data is expensive and time-consuming as it needs human labor, whereas a large amount of unlabeled data are far accessible with the development of internet technology. We construct a general semi-supervised ordinal regression framework to formulate this problem. Based on the framework, we then propose a semi-supervised ordinal regression method called Semi-supervised Ordinal SVM (SOSVM). Additionally, in order to make our proposed method more applicable to problems with large scaled labeled data, we put forward a kernel based dual coordinate descent algorithm to efficiently solve SOSVM. Both rigorous theoretical analysis and promising experimental evaluations on real world datasets show the great performance and remarkable efficiency of SOSVM.