OpenRaiser/Intern-Atlas huggingface dataset:
Data management & analytics for healthcare (DARE 2013)
Authors: Ullas Nambiar, Niranjan Thirumale
Venue: CIKM
Year: 2013

Reducing healthcare costs and improving quality of outcomes is a challenge even in developed economies. Much information remains in paper form, lack common standards, sharing is uncommon and frequently hampered by the lack of foolproof de-identification for patient privacy. All of these issues impede opportunities for data mining and analysis that would enable better predictive and preventive medicine. These issues are compounded in emerging economies due to geopolitical constraints, transportation and geographic barriers, a much more limited clinical workforce, and infrastructural challenges to delivery. Thus, simple, high-impact deliverable interventions such as universal childhood immunization and maternal childcare are hampered by poor monitoring and reporting systems. This workshop is focused on identifying challenges to be overcome for effectively delivering efficient healthcare and to the masses. Specifically, we will provide a forum to discuss research directions, share experience and insights from both academia and industry. The anticipated outcome of the workshop is an assessment of the state of the art in the area, as well as identification of critical next steps to pursue in this topic.
The online revolution: education for everyone
Authors: Andrew Y. Ng
Venue: CIKM
Year: 2013

In 2011, Stanford University offered three online courses, which anyone in the world could enroll in and take for free. Together, these three courses had enrollments of around 350,000 students, making this one of the largest experiments in online education ever performed. Since the beginning of 2012, we have transitioned this effort into a new venture, Coursera, a social entrepreneurship company whose mission is to make high-quality education accessible to everyone by allowing the best universities to offer courses to everyone around the world, for free. Coursera classes provide a real course experience to students, including video content, interactive exercises with meaningful feedback, using both auto-grading and peer-grading, and a rich peer-to-peer interaction around the course materials. Currently, Coursera has over 80 university and other partners, and 4 million students enrolled in its nearly 400 courses. These courses span a range of topics including computer science, business, medicine, science, humanities, social sciences, and more. In this talk, I'll report on this far-reaching experiment in education, and why we believe this model can provide both an improved classroom experience for our on-campus students, via a flipped classroom model, as well as a meaningful learning experience for the millions of students around the world who would otherwise never have access to education of this quality.
Using micro-reviews to select an efficient set of reviews
Authors: Thanh-Son Nguyen, Hady Wirawan Lauw, Panayiotis Tsaparas
Venue: CIKM
Year: 2013

Online reviews are an invaluable resource for web users trying to make decisions regarding products or services. However, the abundance of review content, as well as the unstructured, lengthy, and verbose nature of reviews make it hard for users to locate the appropriate reviews, and distill the useful information. With the recent growth of social networking and micro-blogging services, we observe the emergence of a new type of online review content, consisting of bite-sized, 140 character-long reviews often posted reactively on the spot via mobile devices. These micro-reviews are short, concise, and focused, nicely complementing the lengthy, elaborate, and verbose nature of full-text reviews. We propose a novel methodology that brings together these two diverse types of review content, to obtain something that is more than the sum of its parts. We use micro-reviews as a crowdsourced way to extract the salient aspects of the reviewed item, and propose a new formulation of the review selection problem that aims to find a small set of reviews that efficiently cover the micro-reviews. Our approach consists of a two-step process: matching review sentences to micro-reviews and then selecting reviews such that we cover as many micro-reviews as possible, with few sentences. We perform a detailed evaluation of all the steps of our methodology using data collected from Foursquare and Yelp.
Cloud Armor: a platform for credibility-based trust management of cloud services
Authors: Talal H. Noor, Quan Z. Sheng, Anne H. H. Ngu, Abdullah Alfazi, Jeriel Law
Venue: CIKM
Year: 2013

Trust management of cloud services is emerging as an important research issue in recent years, which poses significant challenges because of the highly dynamic, distributed, and non-transparent nature of cloud services. This paper describes Cloud Armor, a platform for credibility-based trust management of cloud services. The platform provides a crawler for automatic cloud services discovery, an adaptive and robust credibility model for measuring the credibility of feedbacks, and a trust-based recommender to recommend the most trustworthy cloud services to users. This paper presents the motivation, system design, implementation, and a demonstration of the Cloud Armor platform.
Selection fusion in semi-structured retrieval
Authors: Muhammad Ali Norozi, Paavo Arvola
Venue: CIKM
Year: 2013

Semi-structured retrieval aims at providing focused answers to the users queries. A successful retrieval experience in semi-structured environment would mean a satisfactory combination of (a) matching or scoring and (b) selection of appropriate and focused fragments of the text. The need to retrieve items of different sizes arises today with users querying the retrieval systems with varied use case, user interface and screen-size requirements. Which means that different selection scenario serve different requirements and constraints. Hence we propose, a novel type of fusion; the \textit{selection fusion} -- a fusion methodology which fuses an all-purpose and comprehensive ranking of elements with a specific selection scheme, and also enables evaluation of the ranking in many selection perspectives. With the standard Wikipedia XML test collection, we are able to demonstrate that a strong and competitive baseline ranking system improves retrieval quality irrespective of the selection criteria. Our baseline ranking system is based on data fusion over the official submitted runs at INEX 2009.
Motif discovery in spatial trajectories using grammar inference
Authors: Tim Oates, Arnold P. Boedihardjo, Jessica Lin, Crystal Chen, Susan Frankenstein, Sunil Gandhi
Venue: CIKM
Year: 2013

Spatial trajectory analysis is crucial to uncovering insights into the motives and nature of human behavior. In this work, we study the problem of discovering motifs in trajectories based on symbolically transformed representations and context free grammars. We propose a fast and robust grammar induction algorithm called mSEQUITUR to infer a grammar rule set from a trajectory for motif generation. Second, we designed the Symbolic Trajectory Analysis and VIsualization System (STAVIS), the first of its kind trajectory analytical system that applies grammar inference to derive trajectory signatures and enable mining tasks on the signatures. Third, an empirical evaluation is performed to demonstrate the efficiency and effectiveness of mSEQUITUR for generating trajectory signatures and discovering motifs.
Trustable aggregation of online ratings
Authors: Hyun-Kyo Oh, Sang-Wook Kim, Sunju Park, Ming Zhou
Venue: CIKM
Year: 2013

The average of the customer ratings on the product, which we call reputation, is one of the key factors in online purchasing decision of a product. There is, however, no guarantee in the trustworthiness of the reputation since it can be manipulated rather easily. In this paper, we define false reputation as the problem of the reputation to be manipulated by unfair ratings, and design a general framework that provides trustable reputation. For this purpose, we propose TRUEREPUTATION, an algorithm that iteratively adjusts the reputation based on the confidence of customer ratings.
CV-PCR: a context-guided value-driven framework for patent citation recommendation
Authors: Sooyoung Oh, Zhen Lei, Wang-Chien Lee, Prasenjit Mitra, John Yen
Venue: CIKM
Year: 2013

Patent citation recommendation and prior patent search, critical for patent filing and patent examination, have become increasingly difficult due to the rapidly growing number of patents. Unlike paper citations that focus on reference comprehensiveness, patent citations tend to be more parsimonious and refer only to those prior patents bearing significant technological and/or economic value, as they define the scope of the citing patent and thus have significant legal and economic implications. Based on the insight that patent citations are important information reflecting the value of cited patents to the citing patent, we propose a heterogeneous patent citation-bibliographic network that combines patent citations (reflecting value relation) and bibliographic information (reflecting similarity relation) together. From this network, we extract various features that reflect the value of a prior patent to a query patent with regard to the context of the query patent such as its assignee, classifications, etc. We then propose a two-stage framework for patent citation recommendation. Our idea is that by exploiting those context-specific value measures of candidate patents to the query patent, the proposed framework is able to make effective patent citation recommendations. We evaluate the proposed context-guided value-driven framework using a collection of 1.8M U.S. patents. Experimental results validate our ideas and show that those value-driven features are very effective and significantly outperform two state-of-the-art methods in terms of both the precision and recall rates.
RCached-tree: an index structure for efficiently answering popular queries
Authors: Manash Pal, Arnab Bhattacharya, Debjyoti Paul
Venue: CIKM
Year: 2013

In many applications of similarity searching in databases, a set of similar queries appear more frequently. Since it is rare that a query point with its associated parameters (range or number of nearest neighbors) will repeat exactly, intelligent caching mechanisms are required to efficiently answer such queries. In addition, the performance of non-repeating and non-cached queries should not suffer too much either. In this paper, we propose RCached-tree, belonging to the family of R-trees, that aims to solve this problem. In every internal node of the tree up to a certain level, a portion of the space is reserved for storing popular queries and their solutions. For a new query that is encompassed by a cached query, this enables bypassing the traversal of lower levels of the subtree corresponding to the node as the answers can be obtained directly from the result set of the cached query. The structure adapts itself to varying query patterns; new popular queries replace the old cached ones that are not popular any more. Queries that are not popular as well as insertions, deletions and updates are handled in the same manner as in a general R-tree. Experiments show that the RCached-tree can outperform R-tree and other such structures by a significant margin when the proportion of popular queries is 20% or more by reserving 30-40% of the internal nodes as cache.
ROU: advanced keyword search on graph
Authors: Yifan Pan, Yuqing Wu
Venue: CIKM
Year: 2013

Keyword search, the major means for Internet search engines, has recently been explored in structured and semi-structured data. What is yet to be explored thoroughly is how optional and negative keywords can be expressed, what the results should be and how such search queries can be evaluated efficiently. In this paper, we formally define a new type of keyword search query, ROU-query, which takes as input keywords in three categories: required, optional and unwanted, and returns as output sets of nodes in the data graph whose neighborhood satisfies the keyword requirements. We define multiple semantics, including maximal coverage and minimal footprint, to ensure the meaningfulness of results. We propose query induced partite graph (QuIP), that can capture the constraints on neighborhood size and unwanted keywords, and propose a family of algorithms for evaluation of ROU-queries. We conducted extensive experimental evaluations to show our approaches are able to generate results for ROU-queries efficiently.
Efficient parsing-based search over structured data
Authors: Aditya G. Parameswaran, Raghav Kaushik, Arvind Arasu
Venue: CIKM
Year: 2013

Parsing-based search, i.e., parsing keyword search queries using grammars, is often used to override the traditional "bag-of-words'" semantics in web search and enterprise search scenarios. Compared to the "bag-of-words" semantics, the parsing-based semantics is richer and more customizable. While a formalism for parsing-based semantics for keyword search has been proposed in prior work and ad-hoc implementations exist, the problem of designing efficient algorithms to support the semantics is largely unstudied. In this paper, we present a suite of efficient algorithms and auxiliary indexes for this problem. Our algorithms work for a broad classes of grammars used in practice, and cover a variety of database matching functions (set- and substring-containment, approximate and exact equality) and scoring functions (to filter and rank different parses). We formally analyze the time complexity of our algorithms and provide an empirical evaluation over real-world data to show that our algorithms scale well with the size of the database and grammar.
On segmentation of eCommerce queries
Authors: Nish Parikh, Prasad Sriram, Mohammad Al Hasan
Venue: CIKM
Year: 2013

In this paper, we present QSEGMENT, a real-life query segmentation system for eCommerce queries. QSEGMENT uses frequency data from the query log which we call buyers' data and also frequency data from product titles what we call sellers' data. We exploit the taxonomical structure of the marketplace to build domain specific frequency models. Using such an approach, QSEGMENT performs better than previously described baselines for query segmentation. Also, we perform a large scale evaluation by using an unsupervised IR metric which we refer to as user-intent-score. We discuss the overall architecture of QSEGMENT as well as various use cases and interesting observations around segmenting eCommerce queries.
An efficient MapReduce algorithm for counting triangles in a very large graph
Authors: Ha-Myung Park, Chin-Wan Chung
Venue: CIKM
Year: 2013

Triangle counting problem is one of the fundamental problem in various domains. The problem can be utilized for computation of clustering coefficient, transitivity, trianglular connectivity, trusses, etc. The problem have been extensively studied in internal memory but the algorithms are not scalable for enormous graphs. In recent years, the MapReduce has emerged as a de facto standard framework for processing large data through parallel computing. A MapReduce algorithm was proposed for the problem based on graph partitioning. However, the algorithm redundantly generates a large number of intermediate data that cause network overload and prolong the processing time. In this paper, we propose a new algorithm based on graph partitioning with a novel idea of triangle classification to count the number of triangles in a graph. The algorithm substantially reduces the duplication by classifying triangles into three types and processing each triangle differently according to its type. In the experiments, we compare the proposed algorithm with recent existing algorithms using both synthetic datasets and real-world datasets that are composed of millions of nodes and billions of edges. The proposed algorithm outperforms other algorithms in most cases. Especially, for a twitter dataset, the proposed algorithm is more than twice as fast as existing MapReduce algorithms. Moreover, the performance gap increases as the graph becomes larger and denser.
Dynamic query intent mining from a search log stream
Authors: Ya-nan Qian, Tetsuya Sakai, Junting Ye, Qinghua Zheng, Cong Li
Venue: CIKM
Year: 2013

It has long been recognized that search queries are often broad and ambiguous. Even when submitting the same query, different users may have different search intents. Moreover, the intents are dynamically evolving. Some intents are constantly popular with users, others are more bursty. We propose a method for mining dynamic query intents from search query logs. By regarding the query logs as a data stream, we identify constant intents while quickly capturing new bursty intents. To evaluate the accuracy and efficiency of our method, we conducted experiments using 50 topics from the NTCIR INTENT-9 data and additional five popular topics, all supplemented with six-month query logs from a commercial search engine. Our results show that our method can accurately capture new intents with short response time.
Functional dirichlet process
Authors: Lijing Qin, Xiaoyan Zhu
Venue: CIKM
Year: 2013

Dirichlet process mixture (DPM) model is one of the most important Bayesian nonparametric models owing to its efficiency of inference and flexibility for various applications. A fundamental assumption made by DPM model is that all data items are generated from a single, shared DP. This assumption, however, is restrictive in many practical settings where samples are generated from a collection of dependent DPs, each associated with a point in some covariate space. For example, documents in the proceedings of a conference are organized by year, or photos may be tagged and recorded with GPS locations. We present a general method for constructing dependent Dirichlet processes (DP) on arbitrary covariate space. The approach is based on restricting and projecting a DP defined on a space of continuous functions with different domains, which results in a collection of dependent random measures, each associated with a point in covariate space and is marginally DP distributed. The constructed collection of dependent DPs can be used as a nonparametric prior of infinite dynamic mixture models, which allow each mixture component to appear/disappear and vary in a subspace of covariate space. Furthermore, we discuss choices of base distributions of functions in a variety of settings as a flexible method to control dependencies. In addition, we develop an efficient Gibbs sampler for model inference where all underlying random measures are integrated out. Finally, experiment results on temporal modeling and spatial modeling datasets demonstrate the effectiveness of the method in modeling dynamic mixture models on different types of covariates.
Modeling interaction features for debate side clustering
Authors: Minghui Qiu, Liu Yang, Jing Jiang
Venue: CIKM
Year: 2013

Online discussion forums are popular social media platforms for users to express their opinions and discuss controversial issues with each other. To automatically identify the sides/stances of posts or users from textual content in forums is an important task to help mine online opinions. To tackle the task, it is important to exploit user posts that implicitly contain support and dispute (interaction) information. The challenge we face is how to mine such interaction information from the content of posts and how to use them to help identify stances. This paper proposes a two-stage solution based on latent variable models: an interaction feature identification stage to mine interaction features from structured debate posts with known sides and reply intentions; and a clustering stage to incorporate interaction features and model the interplay between interactions and sides for debate side clustering. Empirical evaluation shows that the learned interaction features provide good insights into user interactions and that with these features our debate side model shows significant improvement over other baseline methods.
A tool for assisting provenance search in social media
Authors: Suhas Ranganath, Pritam Gundecha, Huan Liu
Venue: CIKM
Year: 2013

In recent years, social media sites are witnessing an information explosion. Determining the reliability of such a large amount of information is a major area of research. Information provenance (aka, sources or origin) provides a way to measure the reliability of information in social networks. The main challenge in seeking provenance is the availability of suitable data consisting of sufficient unique propagation paths. Knowledge of the actual propagation paths for a piece of information will be a valuable asset in provenance search. This paper presents a tool for capturing the propagation network of a given tweet or URL (Uniform Resource Locator) in the Twitter network. Researchers can use this tool to collect information propagation data, design effective strategies for determining the provenance, and gain information about the tweet such as impact, growth rate and users influencing the spread. Two case studies are presented to demonstrate the effectiveness of the system for seeking provenance information.
RAProp: ranking tweets by exploiting the tweet/user/web ecosystem and inter-tweet agreement
Authors: Srijith Ravikumar, Kartik Talamadupula, Raju Balakrishnan, Subbarao Kambhampati
Venue: CIKM
Year: 2013

The increasing popularity of Twitter renders improved trust- worthiness and relevance assessment of tweets much more important for search. However, given the limitations on the size of tweets, it is hard to extract measures for ranking from the tweets? content alone. We present a novel ranking method called RAProp, which combines two orthogonal measures of relevance and trustworthiness of a tweet. The first, called Feature Score, measures the trustworthiness of the source of the tweet by extracting features from a 3-layer Twitter ecosystem consisting of users, tweets and webpages. The second measure, called agreement analysis, estimates the trustworthiness of the content of a tweet by analyzing whether the content is independently corroborated by other tweets. We view the candidate result set of tweets as the vertices of a graph, with the edges measuring the estimated agreement between each pair of tweets. The feature score is propagated over this agreement graph to compute the top-k tweets that have both trustworthy sources and independent corroboration. The evaluation of our method on 16 million tweets from the TREC 2011 Microblog Dataset shows that for top-30 precision, we achieve 53% better precision than the current best performing method on the data set, and an improvement of 300% over current Twitter Search.
Mapping adaptation actions for the automatic reconciliation of dynamic ontologies
Authors: Júlio Cesar dos Reis, Duy Dinh, Cédric Pruski, Marcos Da Silveira, Chantal Reynaud-Delaître
Venue: CIKM
Year: 2013

The highly dynamic nature of domain ontologies has a direct impact on semantic mappings established between concepts from different ontologies. Mappings must therefore be maintained according to ongoing ontology changes. Since many software applications exploit mappings for managing information and knowledge, it is important to define appropriate adaptation strategies to apply to existing mappings in order to keep their validity over time. In this article, we propose a set of mapping adaptation actions and present how they are used to maintain mappings up-to-date based on ontology change operations of different nature. We conduct an experimental evaluation using life sciences ontologies and mappings. We measure the evolution of mappings based on the proposed approach to mapping adaptation. The results confirm that mappings must be individually adapted according to the different types of ontology change.