OpenRaiser/Intern-Atlas huggingface dataset:
Efficient local ligand-binding site search using landmark mds
Authors: Sungchul Kim, Lee Sael, Hwanjo Yu
Venue: CIKM
Year: 2013

In this work, we propose a new local binding site search system, called Fast Patch-Surfer, for extending previous work, Patch-Surfer. Patch-Surfer efficiently retrieves top-k similar proteins based on new representation of proteins capturing features of their local ligand-binding site and newly defined distance function. However, further speed up is needed since in practical setting of computing dissimilarity between proteins, there are possibilities for simultaneous multiple user access on the database. We address this need for further speed up in local ligand-binding site search by exploiting landmark MultiDimensional Scaling (MDS), which is an efficient version of MDS being popularly used for representing high-dimensional dataset. According to the result, using our method, the searching time is reduced up to 99%, and it retrieves almost 80% of exact top-k results.
Improving passage ranking with user behavior information
Authors: Weize Kong, Elif Aktolga, James Allan
Venue: CIKM
Year: 2013

User behavior information has proved valuable for inferring document relevance, but its role in deducing relevance at the passage/section level is not well explored. In this paper, we study how user behavior information implies section relevance, and use this information to improve section ranking. More specifically, we focus on four types of user search behavior that occur while browsing a document -- dwell time, highlighting, copying and clicks at the section level. Experimental results based on a commercial query log show that user behavior information can significantly improve section ranking. While section-level click information is a very powerful signal of relevance, it depends on an interface supporting section-level links. We find comparable levels of gain using other behavior information that does not depend upon such an interface.
The importance of being socially-savvy: quantifying the influence of social networks on microblog retrieval
Authors: Alexander Kotov, Eugene Agichtein
Venue: CIKM
Year: 2013

Social media users create virtual connections for various reasons: personal and professional. While significant research efforts have been spent on exploring the dynamics of creation of social network connections, little is known about how those connections influence the content generated by social media users. In this work, we quantitatively evaluate the influence of social networks on social media content providers. Additionally, we propose several document expansion methods, which leverage the content generated by the social networks of the authors of social media documents and compare their effectiveness. Experimental results on a large sample of Twitter data indicate that retrieval models discriminatively leveraging social network content for document expansion outperform both traditional, socially-unaware retrieval models and retrieval models that indiscriminatively utilize all social connections.
Human computing games for knowledge acquisition
Authors: Sarath Kumar Kondreddi, Peter Triantafillou, Gerhard Weikum
Venue: CIKM
Year: 2013

Automatic information extraction techniques for knowledge acquisition are known to produce noise, incomplete or incorrect facts from textual sources. Human computing offers a natural alternative to expand and complement the output of automated information extraction methods, thereby enabling us to build high-quality knowledge bases. However, relying solely on human inputs for extraction can be prohibitively expensive in practice. We demonstrate human computing games for knowledge acquisition that employ human computing to overcome the limitations in automated fact acquisition methods. We provide a combined approach that tightly integrates automated extraction techniques with human computing for effective gathering of facts. The methods we provide gather facts in the form of relationships between entities. The games we demonstrate are specifically designed to capture hard-to-extract relations between entities in narrative text -- a task that automated systems find challenging.
Is top-k sufficient for ranking?
Authors: Yanyan Lan, Shuzi Niu, Jiafeng Guo, Xueqi Cheng
Venue: CIKM
Year: 2013

Recently,`top-k learning to rank' has attracted much attention in the community of information retrieval. The motivation comes from the difficulty in obtaining a full-order ranking list for training, when employing reliable pairwise preference judgment. Inspired by the observation that users mainly care about top ranked search result, top-k learning to rank proposes to utilize top-k ground-truth for training, where only the total order of top k items are provided, instead of a full-order ranking list. However, it is not clear whether the underlying assumption holds, i.e. top-k ground-truth is sufficient for training. In this paper, we propose to study this problem from both empirical and theoretical aspects. Empirically, our experimental results on benchmark datasets LETOR4.0 show that the test performances of both pairwise and listwise ranking algorithms will quickly increase to a stable value, with the growth of k in the top-k ground-truth. Theoretically, we prove that the losses of these typical ranking algorithms in top-k setting are tighter upper bounds of (1--NDCG@k), compared with that in full-order setting. Therefore, our studies reveal that learning on top-k ground-truth is surely sufficient for ranking, which lay a foundation for the new learning to rank framework.
Using semantics to process legal document updates
Authors: Christina Leber, Dan Yang, Luis Tari, Andrew Crapo, Aravind Chandramouli
Venue: CIKM
Year: 2013

In most information retrieval systems, updates to indexed documents are handled by either adding the new document to the index and incrementing a version field, or by completely replacing the old version by the new version. These approaches work well when the entire document text is updated, but are insufficient to appropriately handle partial updates in amendments that explicitly state what changes are made to legal contracts. We propose here an approach that parses contract amendments to understand how the amendment is altering the original contract and determine the new effective legal contract. This approach uses semantic modeling and rules to indicate the effective contract to information retrieval systems.
Objectionable content filtering by click-through data
Authors: Lung-Hao Lee, Yen-Cheng Juan, Hsin-Hsi Chen, Yuen-Hsien Tseng
Venue: CIKM
Year: 2013

This paper explores users' browsing intents to predict the category of a user's next access during web surfing, and applies the results to objectionable content filtering. A user's access trail represented as a sequence of URLs reveals the contextual information of web browsing behaviors. We extract behavioral features of each clicked URL, i.e., hostname, bag-of-words, gTLD, IP, and port, to develop a linear chain CRF model for context-aware category prediction. Large-scale experiments show that our method achieves a promising accuracy of 0.9396 for objectionable access identification without requesting their corresponding page content. Error analysis indicates that our proposed model results in a low false positive rate of 0.0571. In real-life filtering simulations, our proposed model accomplishes macro-averaging blocking rate 0.9271, while maintaining a favorably low macro-averaging over-blocking rate 0.0575 for collaboratively filtering objectionable content with time change on the dynamic web.
Pathway-based classification of brain activities for alzheimer's disease analysis
Authors: Jongan Lee, Younghoon Kim, Yong Jeong, Duk L. Na, Kwang Hyung Lee, Doheon Lee
Venue: CIKM
Year: 2013

The advent of resting-state (RS) functional magnetic resonance imaging (fMRI) technology has made it possible to classify Alzheimer's disease (AD) states based on the quantitative activity indices of brain regions. Current connectivity-based classification techniques suffer from limited reproducibility due to the need for prior knowledge on discriminative brain regions and intrinsic heterogeneity in the course of AD progression. Actually, similar challenges have been already addressed in molecular bioinformatics communities. They have achieved higher and reproducible classification accuracy and have identified interpretable markers by incorporating molecular pathway information in their classification. We have adopted a similar strategy to the RS-fMRI-based AD classification problem. After collecting various functional brain pathways from literature, we have quantified which pathways show significantly different activity levels between AD patients and healthy subjects. Moreover, discriminatory pathways between AD patients and healthy subjects may facilitate the interpretation of functional alterations in the course of AD progression.
Online multitasking and user engagement
Authors: Janette Lehmann, Mounia Lalmas, Georges Dupret, Ricardo Baeza-Yates
Venue: CIKM
Year: 2013

Users often access and re-access more than one site during an online session, effectively engaging in multitasking. In this paper, we study the effect of online multitasking on two widely used engagement metrics designed to capture users browsing behavior with a site. Our study is based on browsing data of 2.5M users across 760 sites encompassing diverse types of services such as social media, news and mail. To account for multitasking we need to redefine how user sessions are represented and we need to adapt the metrics under study. We introduce a new representation of user sessions: tree-streams -- as opposed to the commonly used click-streams -- present a more accurate picture of the browsing behavior of a user that includes how users switch between sites (e.g., hyperlinking, teleporting, backpaging). We then discuss a number of insights on multitasking patterns, and show how these help to better understand how users engage with sites. Finally, we define metrics that characterize multitasking during online sessions and show how they provide additional insights to standard engagement metrics.
A multimodal framework for unsupervised feature fusion
Authors: Xiaoyi Li, Jing Gao, Hui Li, Le Yang, Rohini K. Srihari
Venue: CIKM
Year: 2013

With the overwhelming amounts of visual contents on the Internet nowadays, it is very important to generate meaningful and succinct descriptions of multimedia contents including images and videos. Although human taggings and annotations can partially label some of the images or videos, it is impossible to exhaustively describe all the multimedia data due to its huge scale. Therefore, the key to this important task is to develop an effective algorithm that can automatically generate a description of an image or a frame. In this paper, we propose a multimodal feature fusion framework which can model any given image-description pair using semantically meaningful features. This framework is trained as a combination of multi-modal deep networks having two integral components: An ensemble of image descriptors and a recursive bigram encoder with fixed length output feature vector. These two components are then integrated into a joint model characterizing the correlations between images and texts. The proposed framework can not only model the unique characteristics of images or texts, but also take into account their correlations at the semantic level. Experiments on real image-text data sets show that the proposed framework is effective and efficient in indexing and retrieving semantically similar pairs, which will be very useful to help people locate interesting images or videos in large-scale databases.
LCMKL: latent-community and multi-kernel learning based image annotation
Authors: Qing Li, Yun Gu, Xueming Qian
Venue: CIKM
Year: 2013

Automatic image annotation is an important function for online photo sharing service. The concurrence of labels is pretty common in multi-label annotation. In this paper, we propose a novel approach called latent-community and multi-kernel learning (LCMKL). The established graph of labels is regarded as a semantic network. Community detection method is introduced that treats the label set as communities. Multi-kernel learning SVM is adopted for specifying communities and settling difficulty of extracting semantically meaningful entities with some simple features. Experiments on NUS-WIDE database demonstrate that LCMKL outperforms other state-of-the-art approaches.
Unsupervised identification of synonymous query intent templates for attribute intents
Authors: Yanen Li, Bo-June Paul Hsu, ChengXiang Zhai
Venue: CIKM
Year: 2013

Among all web search queries there is an important subset of queries containing entity mentions. In these queries, it is observed that users are most interested in requesting some attribute of an entity, such as "Obama age" for the intent of age, which we refer to as the attribute intent. In this work we address the problem of identifying synonymous query intent templates for the attribute intent. For example, "how old is [Person]" and "[Person]'s age" are both synonymous templates for the age intent. Successful identification of the synonymous query intent templates not only can improve the performance of all existing query annotation approaches, but also could benefit applications such as instant answers and intent-based query suggestion. In this work we propose a clustering framework with multiple kernel functions to identify synonymous query intent templates for a set of canonical templates jointly. Furthermore, signals from multiple sources of information are integrated into a kernel function between templates, where the weights of these signals are tuned in an unsupervised manner. We have conducted extensive experiments across multiple domains in FreeBase, and results demonstrate the effectiveness of our clustering framework for finding synonymous query intent templates for attribute intents.
Mining entity attribute synonyms via compact clustering
Authors: Yanen Li, Bo-June Paul Hsu, ChengXiang Zhai, Kuansan Wang
Venue: CIKM
Year: 2013

Entity attribute values, such as "lord of the rings" for movie.title or "infant" for shoe.gender, are atomic components of entity expressions. Discovering alternative surface forms of attribute values is important for improving entity recognition and retrieval. In this work, we propose a novel compact clustering framework to jointly identify synonyms for a set of attribute values. The framework can integrate signals from multiple information sources into a similarity function between attribute values. And the weights of these signals are optimized in an unsupervised manner. Extensive experiments across multiple domains demonstrate the effectiveness of our clustering framework for mining entity attribute synonyms.
CloudDB 2013: fifth international workshop on cloud data management
Authors: Feifei Li, Xiaofeng Meng, Fusheng Wang, Cong Yu
Venue: CIKM
Year: 2013

The fifth ACM international workshop on cloud data management is held in San Francisco, California, USA on October 28, 2013 and co-located with the ACM 22nd 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 four refereed papers and an invited keynote talk that will give participants a full dose of emerging research.
FRec: a novel framework of recommending users and communities in social media
Authors: Lei Li, Wei Peng, Saurabh Kataria, Tong Sun, Tao Li
Venue: CIKM
Year: 2013

In this paper, we propose a framework of recommending users and communities in social media. Given a user's profile, our framework is capable of recommending influential users and topic-cohesive interactive communities that are most relevant to the given user. In our framework, we present a generative topic model to discover user-oriented and community-oriented topics simultaneously, which enables us to capture the exact topic interests of users, as well as the focuses of communities. Extensive evaluation on a data set obtained from Twitter has demonstrated the effectiveness of our proposed framework compared with other probabilistic topic model based recommendation methods.
Assessing sparse information extraction using semantic contexts
Authors: Pei-Pei Li, Haixun Wang, Hongsong Li, Xindong Wu
Venue: CIKM
Year: 2013

One important assumption of information extraction is that extractions occurring more frequently are more likely to be correct. Sparse information extraction is challenging because no matter how big a corpus is, there are extractions supported by only a small amount of evidence in the corpus. A pioneering work known as REALM learns HMMs to model the context of a semantic relationship for assessing the extractions. This is quite costly and the semantics revealed for the context are not explicit. In this work, we introduce a lightweight, explicit semantic approach for sparse information extraction. We use a large semantic network consisting of millions of concepts, entities, and attributes to explicitly model the context of semantic relationships. Experiments show that our approach improves the F-score of extraction by at least 11.2% over state-of-the-art, HMM based approaches while maintaining more efficiency.
Modeling information diffusion over social networks for temporal dynamic prediction
Authors: Dong Li, Zhiming Xu, Yishu Luo, Sheng Li, Anika Gupta, Katia P. Sycara, Shengmei Luo, Lei Hu, Hong Chen
Venue: CIKM
Year: 2013

How to model the process of information diffusion in social networks is a critical research task. Although numerous attempts have been made for this study, few of them can simulate and predict the temporal dynamics of the diffusion process. To address this problem, we propose a novel information diffusion model (GT model), which considers the users in network as intelligent agents. The agent jointly considers all his interacting neighbors and calculates the payoffs for his different choices to make strategic decision. We introduce the time factor into the user payoff, enabling the GT model to not only predict the behavior of a user but also to predict when he will perform the behavior. Both the global influence and social influence are explored in the time-dependent payoff calculation, where a new social influence representation method is designed to fully capture the temporal dynamic properties of social influence between users. Experimental results on Sina Weibo and Flickr validate the effectiveness of our methods.
Scientific articles recommendation
Authors: Yingming Li, Ming Yang, Zhongfei (Mark) Zhang
Venue: CIKM
Year: 2013

We study the problem of recommending scientific articles to users in an online community and present a novel matrix factorization model, the topic regression Matrix Factorization (tr-MF), to solve the problem. The main idea of tr-MF lies in extending the matrix factorization with a probabilistic topic modeling. Instead of regularizing item factors through the probabilistic topic modeling as in the framework of the CTR model, tr-MF introduces a regression model to regularize user factors through the probabilistic topic modeling under the basic hypothesis that users share the similar preferences if they rate similar sets of items. Consequently, tr-MF provides interpretable latent factors for users and items, and makes accurate predictions for community users. Specifically, it is effective in making predictions for users with only few ratings or even no ratings, and supports tasks that are specific to a certain field, neither of which is addressed in the existing literature. Further, we demonstrate the efficacy of tr-MF on a large subset of the data from CiteULike, a bibliography sharing service dataset. The proposed model outperforms the state-of-the-art matrix factorization models with a significant margin.
Causality and responsibility: probabilistic queries revisited in uncertain databases
Authors: Xiang Lian, Lei Chen
Venue: CIKM
Year: 2013

Recently, due to ubiquitous data uncertainty in many real-life applications, it has become increasingly important to study efficient and effective processing of various probabilistic queries over uncertain data, which usually retrieve uncertain objects that satisfy query predicates with high probabilities. However, one annoying, yet challenging, problem is that, some probabilistic queries are very sensitive to low-quality objects in uncertain databases, and the returned query answers might miss some important results (due to low data quality). To identify both accurate query answers and those potentially low-quality objects, in this paper, we investigate the causes of query answers/non-answers from a novel angle of causality and responsibility (CR), and propose a new interpretation of probabilistic queries. Particularly, we focus on the problem of CR-based probabilistic nearest neighbor (CR-PNN) query, and design a general framework for answering CR-based queries (including CR-PNN), which can return both query answers with high confidences and low-quality objects that may potentially affect query results (for data cleaning purposes). To efficiently process CR-PNN queries, we propose effective pruning strategies to quickly filter out false alarms, and design efficient algorithms to obtain CR-PNN answers. Extensive experiments have been conducted to verify the efficiency and effectiveness of our proposed approaches.
An effective latent networks fusion based model for event recommendation in offline ephemeral social networks
Authors: Guoqiong Liao, Yuchen Zhao, Sihong Xie, Philip S. Yu
Venue: CIKM
Year: 2013

Offline ephemeral social networks (OffESNs) are the networks created ad-hoc at a specific location for a specific purpose and lasting for short period of time, relying on mobile social media such as Radio Frequency Identification (RFID) and Bluetooth devices. The primary purpose of people in the OffESNs is to acquire and share information via attending prescheduled events. Event Recommendation over this kind of networks can facilitate attendees on selecting the prescheduled events and organizers on making resource planning. However, because of lack of users' preference and rating information, as well as explicit social relations, the existing recommendation methods can no longer work well to recommend the events in the OffESNs. To address the challenges such as how to derive latent preferences and social relations and how to fuse the latent information in a unified model, we first construct two heterogeneous interaction social networks, an event participation network and a physical proximity network. Then, we use them to derive users' latent preferences and latent networks on social relations, including like-minded peers, co-attendees and friends. Finally, we propose an LNF (Latent Networks Fusion) model under a pairwise factor graph to infer event attendance probabilities for recommendation. Experiments on an RFID-based real conference dataset have demonstrated the effectiveness of the proposed model compared with typical solutions.