OpenRaiser/Intern-Atlas huggingface dataset:
SRGSIS: a novel framework based on social relationship graph for social image search
Authors: Bo Lu, Ye Yuan, Guoren Wang
Venue: CIKM
Year: 2012

Tag-based social image search predominately focus on using user-annotated tags to find out the results of user query. However, the performance of tag-based social image search is usually unable to satisfy the needs of users. In this paper, we propose a novel framework based on Social Relationship Graph for Social Image Search (SRGSIS), which involves two stages. In the first stage, we use heterogeneous data from multiple modalities to build a social relationship graph. Then, for the given query keywords, we execute an efficient keyword search algorithm over the social relationship graph and obtain top-k candidate results based on relevance score. We model these results as the answer trees connecting keyword nodes that match keywords in the query. In the second stage, for refining the candidate results, each image in social relationship graph is represented as a region adjacency graph by using the visual content of image. We further model these region adjacency graphs as a closure tree and compute approximate graph similarity between the candidate results and the closure tree to obtain more desirable results. Extensive experimental results demonstrate the effectiveness of the proposed approach.
Semantic context learning with large-scale weakly-labeled image set
Authors: Yao Lu, Wei Zhang, Ke Zhang, Xiangyang Xue
Venue: CIKM
Year: 2012

There are a large number of images available on the web; meanwhile, only a subset of web images can be labeled by professionals because manual annotation is time-consuming and labor-intensive. Although we can now use the collaborative image tagging system, e.g., Flickr, to get a lot of tagged images provided by Internet users, these labels may be incorrect or incomplete. Furthermore, semantics richness requires more than one label to describe one image in real applications, and multiple labels usually interact with each other in semantic space. It is of significance to learn semantic context with large-scale weakly-labeled image set in the task of multi-label annotation. In this paper, we develop a novel method to learn semantic context and predict the labels of web images in a semi-supervised framework. To address the scalability issue, a small number of exemplar images are first obtained to cover the whole data cloud; then the label vector of each image is estimated as a local combination of the exemplar label vectors. Visual context, semantic context, and neighborhood consistency in both visual and semantic spaces are sufficiently leveraged in the proposed framework. Finally, the semantic context and the label confidence vectors for exemplar images are both learned in an iterative way. Experimental results on the real-world image dataset demonstrate the effectiveness of our method.
Discovering personally semantic places from GPS trajectories
Authors: Mingqi Lv, Ling Chen, Gencai Chen
Venue: CIKM
Year: 2012

A place is a locale that is frequently visited by an individual user and carries important semantic meanings (e.g. home, work, etc.). Many location-aware applications will be greatly enhanced with the ability of the automatic discovery of personally semantic places. The discovery of a user's personally semantic places involves obtaining the physical locations and semantic meanings of these places. In this paper, we propose approaches to address both of the problems. For the physical place extraction problem, a hierarchical clustering algorithm is proposed to firstly extract visit points from the GPS trajectories, and then these visit points can be clustered to form physical places. For the semantic place recognition problem, Bayesian networks (encoding the temporal patterns in which the places are visited) are used in combination with a customized POI (i.e. place of interest) database (containing the spatial features of the places) to categorize the extracted physical places into pre-defined types. An extensive set of experiments have been conducted to demonstrate the effectiveness of the proposed approaches based on a dataset of real-world GPS trajectories.
A positional access method for relational databases
Authors: Dongzhe Ma, Jianhua Feng, Guoliang Li
Venue: CIKM
Year: 2012

Most commercial database management systems sort tuples of a relation by their primary keys for the purpose of supporting efficient insertions, deletions, and updates. However, primary keys are usually auto-generated integers, which bear little useful information about user data. Secondary indexes have to be created sometimes to help retrieve tuples by columns other than the primary key. Evidently, a better solution is to sort the data by columns that appear frequently in retrieval conditions. Unfortunately, this method does not work, at least not immediately, when the relation is vertically partitioned, which is a popular technique to reduce I/O overhead, since it is difficult to keep tuples of two partitions in exactly the same order unless the sorting columns are replicated, which again wastes storage space and disk bandwidth unnecessarily. In this paper, we introduce a positional access method that allows a partition to be sorted by another one but incurs little storage overhead and provide details about how to improve its performance.
An efficient index for massive IOT data in cloud environment
Authors: Youzhong Ma, Jia Rao, Weisong Hu, Xiaofeng Meng, Xu Han, Yu Zhang, Yunpeng Chai, Chunqiu Liu
Venue: CIKM
Year: 2012

The Internet of Things (IOT) has been widely applied in many fields, while the IOT data are always large volume, update frequently and inherently multi-dimensional, these characteristics bring big challenges to the traditional DBMSs. The traditional DBMSs have rich functionality and can deal with multi-attributes access efficiently, they can not scale good enough to deal with large volume data and can not support high insert throughput. The cloud-based database systems have good scalability, but they don't support multi-dimensional access natively.In order to deal with the large volume of IOT data, we propose an update and query efficient index framework (UQE-Index) based on key-value store that can support high insert throughput and provide efficient multi-dimensional query simultaneously. We implemented a prototype based on HBase and did comprehensive experiments to test our solution's scalability and efficiency.
On the usefulness of query features for learning to rank
Authors: Craig Macdonald, Rodrygo L. T. Santos, Iadh Ounis
Venue: CIKM
Year: 2012

Learning to rank studies have mostly focused on query-dependent and query-independent document features, which enable the learning of ranking models of increased effectiveness. Modern learning to rank techniques based on regression trees can support query features, which are document-independent, and hence have the same values for all documents being ranked for a query. In doing so, such techniques are able to learn sub-trees that are specific to certain types of query. However, it is unclear which classes of features are useful for learning to rank, as previous studies leveraged anonymised features. In this work, we examine the usefulness of four classes of query features, based on topic classification, the history of the query in a query log, the predicted performance of the query, and the presence of concepts such as persons and organisations in the query. Through experiments on the ClueWeb09 collection, our results using a state-of-the-art learning to rank technique based on regression trees show that all four classes of query features can significantly improve upon an effective learned model that does not use any query feature.
A summarization tool for time-sensitive social media
Authors: Walid Magdy, Ahmed M. Ali, Kareem Darwish
Venue: CIKM
Year: 2012

Searching social content in general and microblogs (aka tweets) in particular has been basic and limited, especially for time-sensitive topics. The currently implemented microblog search on sites such as Twitter is based on simple word matching and retrieves the most recent microblogs that match a given query. Furthermore, a user may obtain hundreds or perhaps thousands of microblogs in response to a given query, leading to information overload. We present a new multidimensional microblog search tool that generates a comprehensive report from microblogs instead of a flat list of recent/relevant microblogs for a given query. Reports may include tag-clouds, topic time series, and most popular and funny microblogs, etc. The tool can be configured for monitoring time-sensitive topics using a set of predefined queries. We demonstrate our system on Arabic and English microblog collections. Additionally, we show a special configuration of the system for monitoring the 2012 Egyptian presidential elections.
DUBMMSM'12: international workshop on data-driven user behavioral modeling and mining from social media
Authors: Jalal Mahmud, James Caverlee, Jeffrey Nichols, John O&apos, Donovan, Michelle X. Zhou
Venue: CIKM
Year: 2012

Massive amounts of data are being generated on social media sites, such as Twitter and Facebook. This data can be used to better understand people, such as their personality traits, perceptions, and preferences, and predict their behavior. This deeper understanding of users and their behaviors can benefit a wide range of intelligent applications, such as advertising, social recommender systems, and personalized knowledge management. These applications will also benefit individual users themselves by optimizing their experiences across a wide variety of domains, such as retail, healthcare, and education. Since mining and understanding user behavior from social media often requires interdisciplinary effort, including machine learning, text mining, human-computer interaction, and social science, our workshop aims to bring together researchers and practitioners from multiple fields to discuss the creation of deeper models of individual users by mining the content that they publish and the social networking behavior that they exhibit.
Balanced coverage of aspects for text summarization
Authors: Takuya Makino, Hiroya Takamura, Manabu Okumura
Venue: CIKM
Year: 2012

We propose a new model for the guided text summarization task. In this task, it is required that a generated summary covers all the aspects, which are predefined for the topic of the given document cluster; for example, aspects for the topic "Accidents and Natural Disasters" include WHAT, WHEN, WHERE, WHY, WHO AFFECTED, DAMAGES and COUNTERMEASURES. We use as a scorer for an aspect, the maximum entropy classifier that predicts whether each sentence reflects the aspect or not. We formalize the coverage of the aspects as a max-min problem, which enables a summary to cover aspects in a well-balanced manner. In the max-min problem, the minimum of the aspect scores is going to be maximized so that the summary contains all the aspects as much as possible. Furthermore, we integrate the model based on the max-min problem with the maximum coverage summarization model, which generates a summary containing as many conceptual units as possible. Through the experiments on benchmark datasets for the guided summarization, we show that our model outperforms other approaches in terms of ROUGE-2.
Hierarchical topic integration through semi-supervised hierarchical topic modeling
Authors: Xianling Mao, Jing He, Hongfei Yan, Xiaoming Li
Venue: CIKM
Year: 2012

Lots of document collections are well organized in hierarchical structure, and such structure can help users browse and understand these collections. Meanwhile, there are a large number of plain document collections loosely organized, and it is difficult for users to understand them effectively. In this paper we study how to automatically integrate latent topics in a plain collection with the topics in a hierarchical structured collection. We propose to use semi-supervised topic modeling to solve the problem in a principled way. The experiments show that the proposed method can generate both meaningful latent topics and expand high quality hierarchical topic structures.
Predicting query performance for fusion-based retrieval
Authors: Gad Markovits, Anna Shtok, Oren Kurland, David Carmel
Venue: CIKM
Year: 2012

Estimating the effectiveness of a search performed in response to a query in the absence of relevance judgments is the goal of query-performance prediction methods. Post-retrieval predictors analyze the result list of the most highly ranked documents. We address the prediction challenge for retrieval approaches wherein the final result list is produced by fusing document lists that were retrieved in response to a query. To that end, we present a novel fundamental prediction framework that accounts for this special characteristics of the fusion setting; i.e., the use of intermediate retrieved lists. The framework is based on integrating prediction performed upon the final result list with that performed upon the lists that were fused to create it; prediction integration is controlled based on inter-list similarities. We empirically demonstrate the merits of various predictors instantiated from the framework. A case in point, their prediction quality substantially transcends that of applying state-of-the-art predictors upon the final result list.
Extraction of topic evolutions from references in scientific articles and its GPU acceleration
Authors: Tomonari Masada, Atsuhiro Takasu
Venue: CIKM
Year: 2012

This paper provides a topic model for extracting topic evolutions as a corpus-wide transition matrix among latent topics. Recent trends in text mining point to a high demand for exploiting metadata. Especially, exploitation of reference relationships among documents induced by hyperlinking Web pages, citing scientific articles, tumblring blog posts, retweeting tweets, etc., is put in the foreground of the effort for an effective mining. We focus on scholarly activities and propose a topic model for obtaining a corpus-wide view on how research topics evolve along citation relationships. Our model, called TERESA, extends latent Dirichlet allocation (LDA) by introducing a corpus-wide topic transition probability matrix, which models reference relationships as transitions among topics. Our approximated variational inference updates LDA posteriors and topic transition posteriors alternately. The main issue is execution time amounting to O(MK2), where K is the number of topics and M is that of links in citation network. Therefore, we accelerate the inference with Nvidia CUDA compatible GPUs. We compare the effectiveness of TERESA with that of LDA by introducing a new measure called diversity plus focusedness (D+F). We also present topic evolution examples our method gives.
An examination of content farms in web search using crowdsourcing
Authors: Richard McCreadie, Craig Macdonald, Iadh Ounis, Jim Giles, Ferris Jabr
Venue: CIKM
Year: 2012

On the Web, content farms produce articles engineered such that search engines rank them highly, in order to turn a profit from online advertising. Recently, content farms have increasingly been the target of demotion strategies by Web search engines, since content farm articles are often considered to be of suspect quality. In this paper, we study the prevalence of content farms in the results returned by three major Web search engines over time. In particular, we develop a crowdsourced approach to identify content farm articles from the results returned by these search engines. Our results show that between the period of March and August 2011, the number of content farm articles observed on a number of indicative queries was reduced by up to 55% in the top ranks.
What is the IQ of your data transformation system?
Authors: Giansalvatore Mecca, Paolo Papotti, Salvatore Raunich, Donatello Santoro
Venue: CIKM
Year: 2012

Mapping and translating data across different representations is a crucial problem in information systems. Many formalisms and tools are currently used for this purpose, to the point that developers typically face a difficult question: "what is the right tool for my translation task?" In this paper, we introduce several techniques that contribute to answer this question. Among these, a fairly general definition of a data transformation system, a new and very efficient similarity measure to evaluate the outputs produced by such a system, and a metric to estimate user efforts. Based on these techniques, we are able to compare a wide range of systems on many translation tasks, to gain interesting insights about their effectiveness, and, ultimately, about their "intelligence".
When big data leads to lost data
Authors: V. M. Megler, David Maier
Venue: CIKM
Year: 2012

For decades, scientists bemoaned the scarcity of observational data to analyze and against which to test their models. Exponential growth in data volumes from ever-cheaper environmental sensors has provided scientists with the answer to their prayers: "big data". Now, scientists face a new challenge: with terabytes, petabytes or exabytes of data at hand, stored in thousands of heterogeneous datasets, how can scientists find the datasets most relevant to their research interests? If they cannot find the data, then they may as well never have collected it; that data is lost to them. Our research addresses this challenge, using an existing scientific archive as our test-bed. We approach this problem in a new way: by adapting Information Retrieval techniques, developed for searching text documents, into the world of (primarily numeric) scientific data. We propose an approach that uses a blend of automated and "semi-curated" methods to extract metadata from large archives of scientific data. We then perform searches over the extracted metadata, returning results ranked by similarity to the query terms. We briefly describe an implementation performed at an ocean observatory to validate the proposed approach. We propose performance and scalability research to explore how continued archive growth will affect our goal of interactive response, no matter the scale.
Dictionary based sparse representation for domain adaptation
Authors: Rishabh Mehrotra, Rushabh Agrawal, Syed Aqueel Haider
Venue: CIKM
Year: 2012

Machine Learning algorithms are often as good as the data they can learn from. Enormous amount of unlabeled data is readily available and the ability to efficiently use such amount of unlabeled data holds a significant promise in terms of increasing the performance of various learning tasks. We consider the task of supervised Domain Adaptation and present a Self-Taught learning based framework which makes use of the K-SVD algorithm for learning sparse representation of data in an unsupervised manner. To the best of our knowledge this is the first work that integrates K-SVD algorithm into the self-taught learning framework. The K-SVD algorithm iteratively alternates between sparse coding of the instances based on the current dictionary and a process of updating/adapting the dictionary to better fit the data so as to achieve a sparse representation under strict sparsity constraints. Using the learnt dictionary, a rich feature representation of the few labeled instances is obtained which is fed to a classifier along with class labels to build the model. We evaluate our framework on the task of domain adaptation for sentiment classification. Both self-domain (requiring very few domain-specific training instances) and cross-domain classification (requiring 0 labeled instances of target domain and very few labeled instances of source domain) are performed. Empirical comparisons of self-domain and cross-domain results establish the efficacy of the proposed framework.
Measuring website similarity using an entity-aware click graph
Authors: Pablo N. Mendes, Peter Mika, Hugo Zaragoza, Roi Blanco
Venue: CIKM
Year: 2012

Query logs record the actual usage of search systems and their analysis has proven critical to improving search engine functionality. Yet, despite the deluge of information, query log analysis often suffers from the sparsity of the query space. Based on the observation that most queries pivot around a single entity that represents the main focus of the user's need, we propose a new model for query log data called the entity-aware click graph. In this representation, we decompose queries into entities and modifiers, and measure their association with clicked pages. We demonstrate the benefits of this approach on the crucial task of understanding which websites fulfill similar user needs, showing that using this representation we can achieve a higher precision than other query log-based approaches.
CloudDB 2012: fourth international workshop on cloud data management
Authors: Xiaofeng Meng, Adam Silberstein, Fusheng Wang
Venue: CIKM
Year: 2012

The fourth ACM international workshop on cloud data management is held in Maui, Hawaii, USA on October 29, 2012 and co-located with the ACM 21th Conference on Information and Knowledge Management (CIKM). The main objective of the workshop is to address the challenges of large scale data management based on the cloud computing infrastructure. The workshop brings together researchers and practitioners from cloud computing, distributed storage, query processing, parallel algorithms, data mining, and system analysis, all attendees share common research interests in maximizing performance, reducing cost of cloud data management and enlarging the scale of their endeavors. We have constructed an exciting program of seven refereed papers and four invited keynote talks that will give participants a full dose of emerging research.
LUKe and MIKe: learning from user knowledge and managing interactive knowledge extraction
Authors: Steffen Metzger, Michael Stoll, Katja Hose, Ralf Schenkel
Venue: CIKM
Year: 2012

Semantic recognition and annotation of unqiue enities and their relations is a key in understanding the essence contained in large text corpora. It typically requires a combination of efficient automatic methods and manual verification. Usually, both parts are seen as consecutive steps. In this demo we present MIKE, a user interface enabling the integration of user feedback into an iterative extraction process. We show how an extraction system can directly learn from such integrated user supervision. In general, this setup allows for stepwise training of the extraction system to a particular domain, while using user feedback early in the iterative extraction process improves extraction quality and reduces the overall human effort needed.
Recommendations using linked data
Authors: Rouzbeh Meymandpour, Joseph G. Davis
Venue: CIKM
Year: 2012

Linked Data offers new opportunities for Semantic Web-based application development by connecting structured information from various domains. These technologies allow machines and software agents to automatically interpret and consume Linked Data and provide users with intelligent query answering services. In order to enable advanced and innovative semantic applications of Linked Data such as recommendation, social network analysis, and information clustering, a fundamental requirement is systematic metrics that allow comparison between resources. In this research, we develop a hybrid similarity metric based on the characteristics of Linked Data. In particular, we develop and demonstrate metrics for providing recommendations of closely related resources. The results of our preliminary experiments and future directions are also presented.