OpenRaiser/Intern-Atlas huggingface dataset:
Bridging the unstructured and structured worlds: an adaptive self learning medical form generating system
Authors: Shuai Zheng, Fusheng Wang, James J. Lu
Venue: CIKM
Year: 2012

The prevalence of medical report standards and structured reporting systems reflects the increasing demand for representing and preserving medical and clinical data with controlled vocabularies in well structured format. Strictly formatted medical reports offer high human readability and facilitate further data processing, such as querying, statistical analysis, and reasoning to support decision making. However, many medical reports, such as pathology reports, nursing notes and physician's notes, are written in free-text narration. Manually extracting free text reports by filling predefined data fields is cumbersome and error-prone. Meanwhile, information extraction tools try to automate such process, for example, through machine learning based methods. Such methods often require large volumes of training datasets annotated manually by humans, which is expensive to obtain. Furthermore, they are also limited by their accuracy (both precision and recall).
Enabling ontology based semantic queries in biomedical database systems
Authors: Shuai Zheng, Fusheng Wang, James J. Lu, Joel H. Saltz
Venue: CIKM
Year: 2012

While current biomedical ontology repositories offer primitive query capabilities, it is difficult or cumbersome to support ontology based semantic queries directly in semantically annotated biomedical databases. The problem may be largely attributed to the mismatch between the models of the ontologies and the databases, and the mismatch between the query interfaces of the two systems. To fully realize semantic query capabilities based on ontologies, we develop a system DBOntoLink to provide unified semantic query interfaces by extending database query languages. With DBOntoLink, semantic queries can be directly and naturally specified as extended functions of the database query languages without any programming needed. DBOntoLink is adaptable to different ontologies through customizations and supports major biomedical ontologies hosted at the NCBO BioPortal. We demonstrate the use of DBOntoLink in a real world biomedical database with semantically annotated medical image annotations.
Location-aware instant search
Authors: Ruicheng Zhong, Ju Fan, Guoliang Li, Kian-Lee Tan, Lizhu Zhou
Venue: CIKM
Year: 2012

Location-Based Services (LBS) have been widely accepted by mobile users recently. Existing LBS-based systems require users to type in complete keywords. However for mobile users it is rather difficult to type in complete keywords on mobile devices. To alleviate this problem, in this paper we study the location-aware instant search problem, which returns users location-aware answers as users type in queries letter by letter. The main challenge is to achieve high interactive speed. To address this challenge, in this paper we propose a novel index structure, prefix-region tree (called PR-Tree), to efficiently support location-aware instant search. PR-Tree is a tree-based index structure which seamlessly integrates the textual description and spatial information to index the spatial data. Using the PR-Tree, we develop efficient algorithms to support single prefix queries and multi-keyword queries. Experiments show that our method achieves high performance and significantly outperforms state-of-the-art methods.
Evaluating reward and risk for vertical selection
Authors: Ke Zhou, Ronan Cummins, Mounia Lalmas, Joemon M. Jose
Venue: CIKM
Year: 2012

The aggregation of search results from heterogeneous verticals (news, videos, blogs, etc) has become an important consideration in search. When aiming to select suitable verticals, from which items are selected to be shown along with the standard "ten blue links", there exists the potential to both help (selecting relevant verticals) and harm (selecting irrelevant verticals) the existing result set.
Exploring the existing category hierarchy to automatically label the newly-arising topics in cQA
Authors: Guangyou Zhou, Li Cai, Kang Liu, Jun Zhao
Venue: CIKM
Year: 2012

This work investigates selecting concise labels for the newly-arising topics in community question answer. Previous methods of generating labels do not take the information of the existing category hierarchy into consideration. The main motivation of our paper is to utilize this information into the label generation process. We propose a general framework to address this problem. Firstly, we map the questions into Wikipedia concept sets, which are more meaningful than terms. Secondly, important concepts are identified to represent the main focus of the newly-arising topics. Thirdly, candidate labels are extracted from Wikipedia category graph. Finally, candidate labels are filtered and reranked by combination of structure information of existing category hierarchy and Wikipedia category graph. The experiments show that in our test collections, about 80% "correct" labels appear in the top ten labels recommended by our system.
Topic-sensitive probabilistic model for expert finding in question answer communities
Authors: Guangyou Zhou, Siwei Lai, Kang Liu, Jun Zhao
Venue: CIKM
Year: 2012

In this paper, we address the problem of expert finding in community question answering (CQA). Most of the existing approaches attempt to find experts in CQA by means of link analysis techniques. However, these traditional techniques only consider the link structure while ignore the topical similarity among users (askers and answerers) and user expertise and user reputation. In this study, we propose a topic-sensitive probabilistic model, which is an extension of PageRank algorithm to find experts in CQA. Compared to the traditional link analysis techniques, our proposed method is more effective because it finds the experts by taking into account both the link structure and the topical similarity among users. We conduct experiments on real world data set from Yahoo! Answers. Experimental results show that our proposed method significantly outperforms the traditional link analysis techniques and achieves the state-of-the-art performance for expert finding in CQA.
Collaborative ranking: improving the relevance for tail queries
Authors: Ke Zhou, Xin Li, Hongyuan Zha
Venue: CIKM
Year: 2012

It is well known that tail queries contribute to a substantial fraction of distinct queries submitted to search engines and thus become a major battle field for search engines. Unfortunately, compared with popular queries, it is much more difficult to obtain good search results for tail queries due to the lack of important relevance signals, such as user clicks, phrase matches and so on. In this paper, we propose to utilize the similarities between different queries to overcome the data sparsity problem for tail queries. Specifically, we propose to jointly learn query similarities and the ranking function from data so that the relevance signals of different but related queries can be collaboratively pooled to enhance the ranking of tail queries. We emphasize that the joint optimization is critical so that the learned query similarity function can adapt to the problem of learning ranking functions. Our proposed method is evaluated on two data sets and the results show that our method improves the relevance of tail queries over several baseline alternatives.
Learning to rank duplicate bug reports
Authors: Jian Zhou, Hongyu Zhang
Venue: CIKM
Year: 2012

For a large and complex software system, the project team could receive a large number of bug reports. Some bug reports could be duplicates as they essentially report the same problem. It is often tedious and costly to manually check if a newly reported bug is a duplicate of an already reported bug. In this paper, we propose BugSim, a method that can automatically retrieve duplicate bug reports given a new bug report. BugSim is based on learning to rank concepts. We identify textual and statistical features of bug reports and propose a similarity function for bug reports based on the features. We then construct a training set by assembling pairs of duplicate and non-duplicate bug reports. We train the weights of features by applying the stochastic gradient descent algorithm over the training set. For a new bug report, we retrieve candidate duplicate reports using the trained model. We evaluate BugSim using more than 45,100 real bug reports of twelve Eclipse projects. The evaluation results show that the proposed method is effective. On average, the recall rate for the top 10 retrieved reports is 76.11%. Furthermore, BugSim outperforms the previous state-of-art methods that are implemented using SVM and BM25Fext.
More than relevance: high utility query recommendation by mining users' search behaviors
Authors: Xiaofei Zhu, Jiafeng Guo, Xueqi Cheng, Yanyan Lan
Venue: CIKM
Year: 2012

Query recommendation plays a critical role in helping users' search. Most existing approaches on query recommendation aim to recommend relevant queries. However, the ultimate goal of query recommendation is to assist users to reformulate queries so that they can accomplish their search task successfully and quickly. Only considering relevance in query recommendation is apparently not directly toward this goal. In this paper, we argue that it is more important to directly recommend queries with high utility, i.e., queries that can better satisfy users' information needs. For this purpose, we propose a novel generative model, referred to as Query Utility Model (QUM), to capture query utility by simultaneously modeling users' reformulation and click behaviors. The experimental results on a publicly released query log show that, our approach is more effective in helping users find relevant search results and thus satisfying their information needs.
Graph classification: a diversified discriminative feature selection approach
Authors: Yuanyuan Zhu, Jeffrey Xu Yu, Hong Cheng, Lu Qin
Venue: CIKM
Year: 2012

A graph models complex structural relationships among objects, and has been prevalently used in a wide range of applications. Building an automated graph classification model becomes very important for predicting unknown graphs or understanding complex structures between different classes. The graph classification framework being widely used consists of two steps, namely, feature selection and classification. The key issue is how to select important subgraph features from a graph database with a large number of graphs including positive graphs and negative graphs. Given the features selected, a generic classification approach can be used to build a classification model. In this paper, we focus on feature selection. We identify two main issues with the most widely used feature selection approach which is based on a discriminative score to select frequent subgraph features, and introduce a new diversified discriminative score to select features that have a higher diversity. We analyze the properties of the newly proposed diversified discriminative score, and conducted extensive performance studies to demonstrate that such a diversified discriminative score makes positive/negative graphs separable and leads to a higher classification accuracy.
Parallel proximal support vector machine for high-dimensional pattern classification
Authors: Zhenfeng Zhu, Xingquan Zhu, Yangdong Ye, Yue-Fei Guo, Xiangyang Xue
Venue: CIKM
Year: 2012

Proximal support vector machine (PSVM) is a simple but effective classifier, especially for solving large-scale data classification problems. An inherent deficiency of PSVM lies on its inefficiency for dealing with high-dimensional data. In this paper, we propose a parallel version of PSVM (PPSVM). Based on random dimensionality partitioning, PPSVM can obtain partitioned local model parameters in parallel, with combined parameters to form the final global solution. In fact, PPSVM enjoys two properties: 1) It can calculate model parameters in parallel and is therefore a fast learning method with theoretically proved convergence; and 2) It can avoid the inversion of large matrix, which makes it suitable for high-dimensional data. In the paper, we also propose a random PPSVM with randomly partitioned data in each iteration to improve the performance of PSVM. Experimental results on real-world data demonstrate that the proposed methods can obtain similar or even better prediction accuracy than PSVM with much better runtime efficiency.
Recency-sensitive model of web page authority
Authors: Maxim Zhukovskiy, Dmitry Vinogradov, Gleb Gusev, Pavel Serdyukov, Andrei M. Raigorodskii
Venue: CIKM
Year: 2012

Traditional link-based web ranking algorithms run on a single web snapshot without concern of the dynamics of web pages and links. In particular, the correlation of web pages freshness and their classic PageRank is negative (see [11]). For this reason, in recent years a number of authors introduce some algorithms of PageRank actualization. We introduce our new algorithm called Actual PageRank, which generalizes some previous approaches and therefore provides better capability for capturing the dynamics of the Web. To the best of our knowledge we are the first to conduct ranking evaluations of a fresh-aware variation of PageRank on a large data set. The results demonstrate that our method achieves more relevant and fresh results than both classic PageRank and its "fresh" modifications.
Empirical validation of the buckley-osthus model for the web host graph: degree and edge distributions
Authors: Maxim Zhukovskiy, Dmitry Vinogradov, Yuri Pritykin, Liudmila Ostroumova, Evgeny Grechnikov, Gleb Gusev, Pavel Serdyukov, Andrei M. Raigorodskii
Venue: CIKM
Year: 2012

We consider the Buckley-Osthus implementation of preferential attachment and its ability to model the web host graph in two aspects. One is the degree distribution that we observe to follow the power law, as often being the case for real-world graphs. Another one is the two-dimensional edge distribution, the number of edges between vertices of given degrees. We fit a single "initial attractiveness" parameter a of the model, first with respect to the degree distribution of the web host graph, and then, absolutely independently, with respect to the edge distribution. Surprisingly, the values of a we obtain turn out to be nearly the same. Therefore the same model with the same value of the parameter a fits very well the two independent and basic aspects of the web host graph. In addition, we demonstrate that other models completely lack the asymptotic behavior of the edge distribution of the web host graph, even when accurately capturing the degree distribution. To the best of our knowledge, this is the first study confirming the ability of preferential attachment models to reflect the distribution of edges between vertices with respect to their degrees in a real graph of Internet.
Computational advertising: the linkedin way
Authors: Deepak Agarwal
Venue: CIKM
Year: 2013

LinkedIn is the largest professional social network in the world with more than 238M members. It provides a platform for advertisers to reach out to professionals and target them using rich profile and behavioral data. Thus, online advertising is an important business for LinkedIn. In this talk, I will give an overview of machine learning and optimization components that power LinkedIn self-serve display advertising systems. The talk will not only focus on machine learning and optimization methods, but various practical challenges that arise when running such components in a real production environment. I will describe how we overcome some of these challenges to bridge the gap between theory and practice.
Extraction and integration of web data by end-users
Authors: Sudhir Agarwal, Michael R. Genesereth
Venue: CIKM
Year: 2013

For increasingly sophisticated use cases end users often need to extract, combine, and aggregate information from various (often dynamically generated) web pages from multiple websites. Current search engines do not focus on combining information from various web pages in order to answer the overall information need of the user. Semantic Web and Linked Data usually take a static view on the data and rely on providers' cooperation. In this paper, we present a novel approach that enables end users to easily extract data from web pages while they browse, store it locally in their browser as well as structure, integrate and search such data. We propose Datalog rules for integrating and searching the extracted data. We show how cleaning steps and integration rules can be reused to accelerate the cleaning and integration of extracted data. The proposed approach is implemented as a browser plugin. We present its implementation details and report on our evaluation of the plugin concerning user experience and browsing time saving.
Linear-time enumeration of maximal K-edge-connected subgraphs in large networks by random contraction
Authors: Takuya Akiba, Yoichi Iwata, Yuichi Yoshida
Venue: CIKM
Year: 2013

Capturing sets of closely related vertices from large networks is an essential task in many applications such as social network analysis, bioinformatics, and web link research. Decomposing a graph into k-core components is a standard and efficient method for this task, but obtained clusters might not be well-connected. The idea of using maximal k-edge-connected subgraphs was recently proposed to address this issue. Although we can obtain better clusters with this idea, the state-of-the-art method is not efficient enough to process large networks with millions of vertices.
On sparsity and drift for effective real-time filtering in microblogs
Authors: M-Dyaa Albakour, Craig Macdonald, Iadh Ounis
Venue: CIKM
Year: 2013

In this paper, we approach the problem of real-time filtering in the Twitter Microblogging platform. We adapt an effective traditional news filtering technique, which uses a text classifier inspired by Rocchio's relevance feedback algorithm, to build and dynamically update a profile of the user's interests in real-time. In our adaptation, we tackle two challenges that are particularly prevalent in Twitter: sparsity and drift. In particular, sparsity stems from the brevity of tweets, while drift occurs as events related to the topic develop or the interests of the user change. First, to tackle the acute sparsity problem, we apply query expansion to derive terms or related tweets for a richer initialisation of the user interests within the profile. Second, to deal with drift, we modify the user profile to balance between the importance of the short-term interests, i.e. emerging subtopics, and the long-term interests in the overall topic. Moreover, we investigate an event detection method from Twitter and newswire streams to predict times at which drift may happen. Through experiments using the TREC Microblog track 2012, we show that our approach is effective for a number of common filtering metrics such as the user's utility, and that it compares favourably with state-of-the-art news filtering baselines. Our results also uncover the impact of different factors on handling topic drifting.
Wikipedia-based semantic query enrichment
Authors: Mohannad Almasri, Catherine Berrut, Jean-Pierre Chevallet
Venue: CIKM
Year: 2013

We deal, in this paper, with the short queries (containing one or two words) problem. Short queries have no sufficient information to express their semantics in a non ambiguous way. Pseudo-relevance feedback (PRF) approach for query expansion is useful in many Information Retrieval (IR) tasks. However, this approach does not work well in the case of very short queries. Therefore, we present instead of PRF a semantic query enrichment method based on Wikipedia. This method expands short queries by semantically related terms extracted from Wikipedia. Our experiments on cultural heritage corpora show significant improvement in the retrieval performance.
Exploiting entities in social media
Authors: Omar Alonso, Qifa Ke, Kartikay Khandelwal, Srinivas Vadrevu
Venue: CIKM
Year: 2013

Over the past couple of years micro blogging platforms, such as Twitter, have become extremely popular for information generation and dissemination. Each day hundreds of millions of tweets are being published, containing fresh and trending information that is highly valuable for online users. However, discovering relevant information from such sources is becoming harder due to their rapid growth and the fact that social fragments are often short and noisy. Aggregation techniques such as clustering are often used for extracting this relevant information, since interesting signals begin to emerge when these fragments are grouped together. Clustering large amount of short tweets with limited features is however a challenging task in itself. In this paper, we propose to aggregate tweets by pivoting on entities and mapping them to topics that are already defined in websites such as Wikipedia and Freebase. This allows us to aggregate tweets in a more reliable and feasible way while providing interesting aggregated information about entities present in these fragments. Our analysis using large amounts of tweets shows that such an approach indeed works well. We present encouraging results and various interesting applications centered on entities.
Beyond data: from user information to business value through personalized recommendations and consumer science
Authors: Xavier Amatriain
Venue: CIKM
Year: 2013

Since the Netflix $1 million Prize, announced in 2006, Netflix has been known for having personalization at the core of our product. Our current product offering is nowadays focused around instant video streaming, and our data is now many orders of magnitude larger. Not only do we have many more users in many more countries, but we also receive many more streams of data. Besides the ratings, we now also use information such as what our members play, browse, or search. In this invited talk I will discuss the different approaches we follow to deal with these large streams of user data in order to extract information for personalizing our service. I will describe some of the machine learning models used, and their application in the service. I will also describe our data-driven approach to innovation that combines rapid offline explorations as well as online A/B testing. This approach enables us to convert user information into real and measurable business value.
Beyond data: from user information to business value through personalized recommendations and consumer science
Authors: Xavier Amatriain
Venue: CIKM
Year: 2013

Since the Netflix $1 million Prize, announced in 2006, Netflix has been known for having personalization at the core of our product. Our current product offering is nowadays focused around instant video streaming, and our data is now many orders of magnitude larger. Not only do we have many more users in many more countries, but we also receive many more streams of data. Besides the ratings, we now also use information such as what our members play, browse, or search. In this paper I will discuss the different approaches we follow to deal with these large streams of user data in order to extract information for personalizing our service. I will describe some of the machine learning models used, and their application in the service. I will also describe our data-driven approach to innovation that combines rapid offline explorations as well as online A/B testing. This approach enables us to convert user information into real and measurable business value.