OpenRaiser/Intern-Atlas huggingface dataset:
Clinical data analysis using ontology-guided rule learning
Authors: Hua Min, Janusz Wojtusiak
Venue: CIKM
Year: 2012

Currently, research in Machine Learning (ML) mainly focuses on the ability to process very large amounts of data and build accurate models. Problems related to complexity, heterogeneity, and semantics of healthcare data are often out of the main focus. Healthcare is particularly rich in background knowledge. Surprisingly, few ML methods used in healthcare can handle these sources of background knowledge, and instead treat healthcare data as a set of numbers without particular meaning. This paper explores an approach that can fill in this gap. A medical ontology (i.e., UMLS) is proposed to provide background knowledge for the ML method to understand healthcare data. The ontology-guided ML-based rule induction method is described and illustrated to analyze the clinical data supplemented with an ontology-based background knowledge.
Design and evaluation of an ir-benchmark for sparql queries with fulltext conditions
Authors: Arunav Mishra, Sairam Gurajada, Martin Theobald
Venue: CIKM
Year: 2012

In this paper, we describe our goals in introducing a new, annotated benchmark collection, with which we aim to bridge the gap between the fundamentally different aspects that are involved in querying both structured and unstructured data. This semantically rich collection, captured in a unified XML format, combines components (unstructured text, semistructured infoboxes, and category structure) from 3.1 Million Wikipedia articles with highly structured RDF properties from both DBpedia and YAGO2. The new collection serves as the basis of the INEX 2012 Ad-hoc, Faceted Search, and Jeopardy retrieval tasks. With a focus on the new Jeopardy task, we particularly motivate the usage of the collection for question-answering (QA) style retrieval settings, which we also exemplify by introducing a set of 90 QA-style benchmark queries which come shipped in a SPARQL-based query format that has been extended by fulltext filter conditions.
Twitter user behavior understanding with mood transition prediction
Authors: Aditya Mogadala, Vasudeva Varma
Venue: CIKM
Year: 2012

Human moods continuously change over time. Tracking moods can provide important information about psychological and health behavior of an individual. Also, history of mood information can be used to predict the future moods of individuals. In this paper, we try to predict the mood transition of a Twitter user by regression analysis on the tweets posted over twitter time line. Initially, user tweets are automatically labeled with mood labels from time 0 to t-1. It is then used to predict user mood transition information at time t. Experiments show that SVM regression attained less root-mean-square error compared to other regression approaches for mood transition prediction.
On the design of LDA models for aspect-based opinion mining
Authors: Samaneh Moghaddam, Martin Ester
Venue: CIKM
Year: 2012

Aspect-based opinion mining, which aims to extract aspects and their corresponding ratings from customers reviews, provides very useful information for customers to make purchase decisions. In the past few years several probabilistic graphical models have been proposed to address this problem, most of them based on Latent Dirichlet Allocation (LDA). While these models have a lot in common, there are some characteristics that distinguish them from each other. These fundamental differences correspond to major decisions that have been made in the design of the LDA models. While research papers typically claim that a new model outperforms the existing ones, there is normally no "one-size-fits-all" model. In this paper, we present a set of design guidelines for aspect-based opinion mining by discussing a series of increasingly sophisticated LDA models. We argue that these models represent the essence of the major published methods and allow us to distinguish the impact of various design decisions. We conduct extensive experiments on a very large real life dataset from Epinions.com (500K reviews) and compare the performance of different models in terms of the likelihood of the held-out test set and in terms of the accuracy of aspect identification and rating prediction.
TALMUD: transfer learning for multiple domains
Authors: Orly Moreno, Bracha Shapira, Lior Rokach, Guy Shani
Venue: CIKM
Year: 2012

Most collaborative Recommender Systems (RS) operate in a single domain (such as movies, books, etc.) and are capable of providing recommendations based on historical usage data which is collected in the specific domain only. Cross-domain recommenders address the sparsity problem by using Machine Learning (ML) techniques to transfer knowledge from a dense domain into a sparse target domain. In this paper we propose a transfer learning technique that extracts knowledge from multiple domains containing rich data (e.g., movies and music) and generates recommendations for a sparse target domain (e.g., games). Our method learns the relatedness between the different source domains and the target domain, without requiring overlapping users between domains. The model integrates the appropriate amount of knowledge from each domain in order to enrich the target domain data. Experiments with several datasets reveal that, using multiple sources and the relatedness between domains improves accuracy of results.
WiSeNet: building a wikipedia-based semantic network with ontologized relations
Authors: Andrea Moro, Roberto Navigli
Venue: CIKM
Year: 2012

In this paper we present an approach for building a Wikipedia-based semantic network by integrating Open Information Extraction with Knowledge Acquisition techniques. Our algorithm extracts relation instances from Wikipedia page bodies and ontologizes them by, first, creating sets of synonymous relational phrases, called relation synsets, second, assigning semantic classes to the arguments of these relation synsets and, third, disambiguating the initial relation instances with relation synsets. As a result we obtain WiSeNet, a Wikipedia-based Semantic Network with Wikipedia pages as concepts and labeled, ontologized relations between them.
An automated data utility clustering methodology using data constraint rules
Authors: Stuart Morton, Malika Mahoui, P. Joseph Gibson
Venue: CIKM
Year: 2012

Many data privacy models have been created in the last few years using the k-anonymization methodology including l-diversity, p-sensitive k-anonymity, and t-closeness. While these methods differ in their approaches and quality of the results, they all focus on ensuring the anonymization of the data while at the same time attempt to protect the quality of the data by minimizing the loss of the information contained in the original data set. In this paper, we propose an automated k-anonymity approach that uses clustering to maximize the utility of the data while ensuring that the data privacy is maintained. Our method employs data constraint rules, which are defined by the data research expert to represent especially informative distributions in categorical attributes or inflections points in a continuous attribute. The values of the data constraints are an integral component of our utility function, which is used to maximize the utility of the anonymized dataset. Finally, we present our experimental results that show that our approach meets or exceeds existing methods that do not incorporate data constraint rules.
How do humans distinguish different people with identical names on the web?
Authors: Harumi Murakami, Yuki Miyake
Venue: CIKM
Year: 2012

This research investigates how humans distinguish different people with identical names on the web to improve web people search. We asked subjects to classify 20 pages of web people-search results for each of 20 person names and analyzed their decision processes through questionnaire, protocol analysis, and interview. We found that keywords, vocations, works (for a real person, works are those made by the individual and, for a fictional person, works are those in which the individual appears), facial images, and the names of related people are important for distinguishing individuals. We proposed a model for distinguishing individuals and a knowledge-structure model based on the experiment's results.
A math-aware search engine for math question answering system
Authors: Tam T. Nguyen, Kuiyu Chang, Siu Cheung Hui
Venue: CIKM
Year: 2012

We propose a math-aware search engine that is capable of handling both textual keywords as well as mathematical expressions. Our math feature extraction and representation framework captures the semantics of math expressions via a Finite State Machine model. We adapt the passive aggressive online learning binary classifier as the ranking model. We benchmarked our approach against three classical information retrieval (IR) strategies on math documents crawled from Math Overflow, a well-known online math question answering system. Experimental results show that our proposed approach can perform better than other methods by more than 9%.
Clustering Wikipedia infoboxes to discover their types
Authors: Thanh Hoang Nguyen, Huong Dieu Nguyen, Viviane Pereira Moreira, Juliana Freire
Venue: CIKM
Year: 2012

Wikipedia has emerged as an important source of structured information on the Web. But while the success of Wikipedia can be attributed in part to the simplicity of adding and modifying content, this has also created challenges when it comes to using, querying, and integrating the information. Even though authors are encouraged to select appropriate categories and provide infoboxes that follow pre-defined templates, many do not follow the guidelines or follow them loosely. This leads to undesirable effects, such as template duplication, heterogeneity, and schema drift. As a step towards addressing this problem, we propose a new unsupervised approach for clustering Wikipedia infoboxes. Instead of relying on manually assigned categories and template labels, we use the structured information available in infoboxes to group them and infer their entity types. Experiments using over 48,000 infoboxes indicate that our clustering approach is effective and produces high quality clusters.
Adapt: adaptive database schema design for multi-tenant applications
Authors: Jiacai Ni, Guoliang Li, Jun Zhang, Lei Li, Jianhua Feng
Venue: CIKM
Year: 2012

Multi-tenant data management is a major application of software as a Service (SaaS). Many companies outsource their data to a third party which hosts a multi-tenant database system to provide data management service. The system should have high performance, low space and excellent scalability. One big challenge is to devise a high-quality database schema. Independent Tables Shared Instances and Shared Tables Shared Instances are two state-of-the-art methods. However, the former has poor scalability, while the latter achieves good scalability at the expense of poor performance and high space overhead. In this paper, we trade-off between the two methods and propose an adaptive database schema design approach to achieve good scalability and high performance with low space. To this end, we identify the important attributes and use them to generate a base table. For other attributes, we construct supplementary tables. We propose a cost-based model to adaptively generate the tables above. Our method has the following advantages. First, our method achieves high scalability. Second, our method can trade-off performance and space requirement. Third, our method can be easily applied to existing databases (e.g., MySQL) with minor revisions. Fourth, our method can adapt to any schemas and query workloads. Experimental results show our method achieves high performance and good scalability with low space and outperforms state-of-the-art method.
Conceptualizing documents with Wikipedia
Authors: Tadashi Nomoto, Noriko Kando
Venue: CIKM
Year: 2012

In this work, we will discuss how to improve Wikilabel, an approach which makes use of titles in Wikipedia pages to generate labels for documents, by retooling ideas from story link detection (SLD). A comparison of our approach against Elastic Net, a powerful machine learner, on the real world data, finds the visible superiority of our approach over the latter.
Contextualization using hyperlinks and internal hierarchical structure of Wikipedia documents
Authors: Muhammad Ali Norozi, Paavo Arvola, Arjen P. de Vries
Venue: CIKM
Year: 2012

Context surrounding hyperlinked semi-structured documents, externally in the form of citations and internally in the form of hierarchical structure, contains a wealth of useful but implicit evidence about a document's relevance. These rich sources of information should be exploited as contextual evidence. This paper proposes various methods of accumulating evidence from the context, and measures the effect of contextual evidence on retrieval effectiveness for document and focused retrieval of hyperlinked semi-structured documents.
Completeness of queries over SQL databases
Authors: Werner Nutt, Simon Razniewski
Venue: CIKM
Year: 2012

Data completeness is an important aspect of data quality. We consider a setting, where databases can be incomplete in two ways: records may be missing and records may contain null values. We (i) formalize when the answer set of a query is complete in spite of such incompleteness, and (ii) we introduce table completeness statements, by which one can express that certain parts of a database are complete. We then study how to deduce from a set of table-completeness statements that a query can be answered completely.
iSampling: framework for developing sampling methods considering user's interest
Authors: Jinoh Oh, Hwanjo Yu
Venue: CIKM
Year: 2012

Sampling is one of fundamental techniques for data preprocessing and mining. It helps to reduce computational costs and improve the mining quality. A sampling method is typically developed independently for a specific problem and for a specific user's interest, because it is hard to develop a method that is generalized across various user's interests. An absence of general framework for sampling makes it inefficient to develop or revise a sampling method as user's interest changes. This paper proposes a general framework, isampling, which facilitates a user developing sampling methods and easily modifying the user's sampling interest in the method. In the framework, a user explicitly describes her sampling interest into a graph model called interest model. Then, isampling automatically selects a sample set according to the model, which satisfies the user's interest. In order to demonstrate the effectiveness of our framework, we develop new trajectory sampling methods using our framework; trajectory sampling has been a challenging problem due to its high complexity of data and various user's interests. We demonstrate the flexibility of our framework by showing how easily trajectory samples of different interests can be generated within our framework.
A probabilistic approach to correlation queries in uncertain time series data
Authors: Mahsa Orang, Nematollaah Shiri
Venue: CIKM
Year: 2012

Numerous real-life applications, such as wireless sensor networks and location-based services, generate large amount of uncertain time series, where the exact value at each timestamp is unavailable or unknown. In this paper, we formalize the notion of correlation for uncertain time series data and consider a family of probabilistic, threshold-based correlation queries over such data. The proposed formulation extends the notion of correlation developed for standard, certain time series. We show that uncertain correlation is a random variable approaching normal distribution. We also formalize the notion of uncertain time series normalization which is at the core of our correlation query processing approach, while it proves to be an important pre-processing technique in particular for pattern discovery tasks. The results of our numerous experiments indicate that, unlike in the standard time series, there is a trade-off between false alarms and hit ratios, which can be controlled by the probability threshold provided by users. Our results also offer users a guideline for choosing proper threshold values.
Fast PCA computation in a DBMS with aggregate UDFs and LAPACK
Authors: Carlos Ordonez, Naveen Mohanam, Carlos Garcia-Alvarado, Predrag T. Tosic, Edgar Martinez
Venue: CIKM
Year: 2012

Efficient and scalable execution of numerical methods inside a DBMS is difficult as its architecture is not suited for intense numerical computations. We study computing Principal Component Analysis (PCA) on large data sets via Singular Value Decomposition (SVD). Given the difficulty to program and optimize numerical methods on an existing DBMS, we explore an alternative reusability approach: calling the well-known numerical library LAPACK. Thus we study several alternatives to summarize the data set with aggregate User-Defined Functions (UDFs) and how to efficiently call SVD numerical methods available in LAPACK via Stored Procedures (SPs). We propose algorithmic and system optimizations to enhance scalability and to push processing into RAM. We show it is feasible to efficiently solve PCA by first summarizing the data set with arrays incrementally updated with aggregate UDFs and then pushing heavy matrix processing in SVD to RAM calling LAPACK via SPs. We benchmark our solution on a modern DBMS. Our solution requires only one pass on the data set and it exhibits linear scalability.
A decentralized recommender system for effective web credibility assessment
Authors: Thanasis G. Papaioannou, Jean-Eudes Ranvier, Alexandra Olteanu, Karl Aberer
Venue: CIKM
Year: 2012

An overwhelming and growing amount of data is available online. The problem of untrustworthy online information is augmented by its high economic potential and its dynamic nature, e.g. transient domain names, dynamic content, etc. In this paper, we address the problem of assessing the credibility of web pages by a decentralized social recommender system. Specifically, we concurrently employ i) item-based collaborative filtering (CF) based on specific web page features, ii) user-based CF based on friend ratings and iii) the ranking of the page in search results. These factors are appropriately combined into a single assessment based on adaptive weights that depend on their effectiveness for different topics and different fractions of malicious ratings. Simulation experiments with real traces of web page credibility evaluations suggest that our hybrid approach outperforms both its constituent components and classical content-based classification approaches.
Deco: declarative crowdsourcing
Authors: Aditya G. Parameswaran, Hyunjung Park, Hector Garcia-Molina, Neoklis Polyzotis, Jennifer Widom
Venue: CIKM
Year: 2012

Crowdsourcing enables programmers to incorporate "human computation" as a building block in algorithms that cannot be fully automated, such as text analysis and image recognition. Similarly, humans can be used as a building block in data-intensive applications--providing, comparing, and verifying data used by applications. Building upon the decades-long success of declarative approaches to conventional data management, we use a similar approach for data-intensive applications that incorporate humans. Specifically, declarative queries are posed over stored relational data as well as data computed on-demand from the crowd, and the underlying system orchestrates the computation of query answers.
Labeling by landscaping: classifying tokens in context by pruning and decorating trees
Authors: Siddharth Patwardhan, Branimir Boguraev, Apoorv Agarwal, Alessandro Moschitti, Jennifer Chu-Carroll
Venue: CIKM
Year: 2012

State-of-the-art approaches to token labeling within text documents typically cast the problem either as a classification task, without using complex structural characteristics of the input, or as a sequential labeling task, carried out by a Conditional Random Field (CRF) classifier. Here we explore principled ways for structure to be brought to bear on the task. In line with recent trends in statistical learning of structured natural language input, we use a Support Vector Machine (SVM) classification framework deploying tree kernels. We then propose tree transformations and decorations, as a methodology for modeling complex linguistic phenomena in highly multi-dimensional feature spaces. We develop a general purpose tree engineering framework, which enables us to transcend the typically complex and laborious process of feature engineering. We build kernel based classifiers for two token labeling tasks: fine-grained event recognition, and lexical answer type detection in questions. For both, we show that in comparison with a corresponding linear kernel SVM, our method of using tree kernels improves recognition, thanks to appropriately engineering tree structures for use by the tree kernel. We also observe significant improvements when comparing with a CRF-based realization of structured prediction, itself performing at levels comparable to state-of-the-art.
Making your interests follow you on twitter
Authors: Marco Pennacchiotti, Fabrizio Silvestri, Hossein Vahabi, Rossano Venturini
Venue: CIKM
Year: 2012

In this paper we introduce the task of "tweet recommendation", the problem of suggesting tweets that match a user's interests and likes. We propose an Information-Retrieval-like model that leverages the content of the user's tweets and those of her friends, and that effectively retrieves a set of tweets that is personalized and varied in nature. Our approach could be easily leveraged to build, for example, a Twitter or Facebook timeline that collects messages that are of interest for the user, but that are not posted by her friends. We compare to typical approaches used in similar tasks, reporting significant gains in terms of overall precision, up to about +20%, on both a corpus-based evaluation and real world user study.
Pinteresting: towards a better understanding of user interests
Authors: Ana-Maria Popescu
Venue: CIKM
Year: 2012

The ascendance of interest-based social networks allows for developing better models of user interest in the context of established and new tasks, connecting interests with demographic characteristics, studying changes in user interests with respect to time and location or mining interest data to build broadly useful resources. This submission focuses on Pinterest, a growing social bookmarking site; it identifies research avenues for improving the user experience and for exploiting Pinterest data for broader uses.
Efficient estimation of dynamic density functions with an application to outlier detection
Authors: Abdulhakim Ali Qahtan, Xiangliang Zhang, Suojin Wang
Venue: CIKM
Year: 2012

In this paper, we propose a new method to estimate the dynamic density over data streams, named KDE-Track as it is based on a conventional and widely used Kernel Density Estimation (KDE) method. KDE-Track can efficiently estimate the density with linear complexity by using interpolation on a kernel model, which is incrementally updated upon the arrival of streaming data. Both theoretical analysis and experimental validation show that KDE-Track outperforms traditional KDE and a baseline method Cluster-Kernels on estimation accuracy of the complex density structures in data streams, computing time and memory usage. KDE-Track is also demonstrated on timely catching the dynamic density of synthetic and real-world data. In addition, KDE-Track is used to accurately detect outliers in sensor data and compared with two existing methods developed for detecting outliers and cleaning sensor data.