OpenRaiser/Intern-Atlas huggingface dataset:
Improving the performance of the reinforcement learning model for answering complex questions
Authors: Yllias Chali, Sadid A. Hasan, Kaisar Imam
Venue: CIKM
Year: 2012

This paper addresses the task of answering complex questions using a multi-document summarization approach within a reinforcement learning setting. Given a set of complex questions, a list of relevant documents per question, and the corresponding human-generated summaries (i.e. answers to the questions) as training data, the reinforcement learning module iteratively learns a number of feature weights in order to facilitate the automatic generation of summaries i.e. answers to unseen complex questions. Previous works on this task have utilized a fully automatic reinforcement learning framework that selects the document sentences as the potential candidate (i.e. machine-generated) summary sentences by exploiting a relatedness measure with the available human-written summaries. In this paper, we propose an extension to this model that incorporates user interaction into the reinforcement learner to guide the candidate summary sentence selection process. Experimental results reveal the effectiveness of the user interaction component in the reinforcement learning framework.
SciQL: a query language for unified scientific data processing and management
Authors: Javad Chamanara, Birgitta König-Ries
Venue: CIKM
Year: 2012

Science is more and more data-driven. This means, that a significant part of a scientist's work is dedicated to accessing, visualizing, integrating and analyzing data from a possibly wide range of heterogeneous sources. In this paper we propose SciQL, a query language that supports scientists in this task and allows them to focus on their main purpose, i.e., on doing research.
Cager: a framework for cross-page search
Authors: Zhumin Chen, Byron J. Gao, Qi Kang
Venue: CIKM
Year: 2012

Existing search engines have page as the unit of information of retrieval. They typically return a ranked list of pages, each being a search result containing the query keywords. This within-one-page constraint disallows utilization of relationship information that is often available and greatly beneficial. To utilize relationship information and improve search precision, we explore cross-page search, where each answer is a logical page consisting of multiple closely related pages that collectively contain the query keywords. We have implemented a prototype Cager, providing cross-page search and visualization over real dataset.
Modeling semantic relations between visual attributes and object categories via dirichlet forest prior
Authors: Xin Chen, Xiaohua Hu, Zhongna Zhou, Yuan An, Tingting He, E. K. Park
Venue: CIKM
Year: 2012

In this paper, we deal with two research issues: the automation of visual attribute identification and semantic relation learning between visual attributes and object categories. The contribution is two-fold, firstly, we provide uniform framework to reliably extract both categorical attributes and depictive attributes. Secondly, we incorporate the obtained semantic associations between visual attributes and object categories into a text-based topic model and extract descriptive latent topics from external textual knowledge sources. Specifically, we show that in mining natural language descriptions from external knowledge sources, the relation between semantic visual attributes and object categories can be encoded as Must-Links and Cannot-Links, which can be represented by Dirichlet-Forest prior. To alleviate the workload of manual supervision and labeling in image categorization process, we introduce a semi-supervised training framework using soft-margin semi-supervised SVM classifier. We also show that the large-scale image categorization results can be significantly improved by combining automatically acquired visual attributes. Experimental results show that the proposed model achieves better ability in describing object-related attributes and makes the inferred latent topics more descriptive.
Information preservation in static index pruning
Authors: Ruey-Cheng Chen, Chia-Jung Lee, Chiung-Min Tsai, Jieh Hsiang
Venue: CIKM
Year: 2012

We develop a new static index pruning criterion based on the notion of information preservation. This idea is motivated by the fact that model degeneration, as does static index pruning, inevitably reduces the predictive power of the resulting model. We model this loss in predictive power using conditional entropy and show that the decision in static index pruning can therefore be optimized to preserve information as much as possible. We evaluated the proposed approach on three different test corpora, and the result shows that our approach is comparable in retrieval performance to state-of-the-art methods. When efficiency is of concern, our method has some advantages over the reference methods and is therefore suggested in Web retrieval settings.
A co-training based method for chinese patent semantic annotation
Authors: Xu Chen, Zhiyong Peng, Cheng Zeng
Venue: CIKM
Year: 2012

Patents are public and scientific literatures protected by the law, and their abstracts highly contained valuable information. Patent's semantic annotation can effectively protect intellectual property rights and promote corporations' scientific research innovation. Currently, automatic patent annotation mainly used supervised machine learning algorithms, which required abundant expensive labeled patent data. Due to lack of enough labeled Chinese patent data, this paper adopted a semi-supervised machine learning method named co-training, which started from a little labeled data. This method combined keyword extraction with list extraction, and incrementally annotated functional clauses in patent abstract. Experiment results indicated this method can gradually improve the recall without sacrificing the precision.
Automated feature weighting in naive bayes for high-dimensional data classification
Authors: Lifei Chen, Shengrui Wang
Venue: CIKM
Year: 2012

Naive Bayes (NB for short) is one of the popular methods for supervised classification in a knowledge management system. Currently, in many real-world applications, high-dimensional data pose a major challenge to conventional NB classifiers, due to noisy or redundant features and local relevance of these features to classes. In this paper, an automated feature weighting solution is proposed to result in a NB method effective in dealing with high-dimensional data. We first propose a locally weighted probability model, for Bayesian modeling in high-dimensional spaces, to implement a soft feature selection scheme. Then we propose an optimization algorithm to find the weights in linear time complexity, based on the Logitnormal priori distribution and the Maximum a Posteriori principle. Experimental studies show the effectiveness and suitability of the proposed model for high-dimensional data classification.
4Is of social bully filtering: identity, inference, influence, and intervention
Authors: Yunfei Chen, Lanbo Zhang, Aaron Michelony, Yi Zhang
Venue: CIKM
Year: 2012

As the increasing of popularity of social web, cyber bullying has become a more and more serious issue among children. Bullying causes huge negative effects on children, even suicide. SocialFilter is a realtime system that helps parents and educators track children's messages on Twitter, especially in order to detect whether they have been bullied or bullying others. The aim of the system is 4 I's, identity of bullies, inference of bullying message, influence of bully behavior, and intervention. We solve this problem by using machine learning technique. The current system is tracking tens of thousands of active children users on Twitter and automatically detect bullying messages at real time.
Hierarchical co-clustering based on entropy splitting
Authors: Wei Cheng, Xiang Zhang, Feng Pan, Wei Wang
Venue: CIKM
Year: 2012

Two dimensional contingency tables or co-occurrence matrices arise frequently in various important applications such as text analysis and web-log mining. As a fundamental research topic, co-clustering aims to generate a meaningful partition of the contingency table to reveal hidden relationships between rows and columns. Traditional co-clustering algorithms usually produce a predefined number of flat partition of both rows and columns, which do not reveal relationship among clusters. To address this limitation, hierarchical co-clustering algorithms have attracted a lot of research interests recently. Although successful in various applications, the existing hierarchial co-clustering algorithms are usually based on certain heuristics and do not have solid theoretical background.
CONSENTO: a new framework for opinion based entity search and summarization
Authors: Jaehoon Choi, Donghyeon Kim, Seongsoon Kim, Junkyu Lee, Sangrak Lim, Sunwon Lee, Jaewoo Kang
Venue: CIKM
Year: 2012

Search engines have become an important decision making tool today. Decision making queries are often subjective, such as "a good birthday present for my girlfriend", "best action movies in 2010", to name a few. Unfortunately, such queries may not be answered properly by conventional search systems. In order to address this problem, we introduce Consento, a consensus search engine designed to answer subjective queries. Consento performs segment indexing, as opposed to document indexing, to capture semantics from user opinions more precisely. In particular, we define a new indexing unit, Maximal Coherent Semantic Unit (MCSU).
HadoopXML: a suite for parallel processing of massive XML data with multiple twig pattern queries
Authors: Hyebong Choi, Kyong-Ha Lee, Soo-Hyong Kim, Yoon-Joon Lee, Bongki Moon
Venue: CIKM
Year: 2012

The volume of XML data is tremendous in many areas, but especially in data logging and scientific areas. XML data in the areas are accumulated over time as new data are continuously collected. It is a challenge to process massive XML data with multiple twig pattern queries given by multiple users in a timely manner. We showcase HadoopXML, a system that simultaneously processes many twig pattern queries for a massive volume of XML data with Hadoop. Specifically, HadoopXML provides an efficient way to process a single large XML file in parallel. It processes multiple twig pattern queries simultaneously with a shared input scan. Users do not need to iterate M/R jobs for each query. HadoopXML also reduces many I/Os by enabling twig pattern queries to share their path solutions each other. Moreover, HadoopXML provides a sophisticated runtime load balancing scheme for fairly assigning multiple twig pattern joins across nodes. With synthetic and real world XML dataset, we demonstrate how efficiently HadoopXML processes many twig pattern queries in a shared and balanced way.
RDF pattern matching using sortable views
Authors: Zhihong Chong, He Chen, Zhenjie Zhang, Hu Shu, Guilin Qi, Aoying Zhou
Venue: CIKM
Year: 2012

In the last few years, RDF is becoming the dominating data model used in semantic web for knowledge representation and inference. In this paper, we revisit the problem of pattern matching query in RDF model, which is usually expensive in efficiency due to the huge cost on join operations. To alleviate the efficiency pain, view materialization techniques are usually deployed to accelerate the query processing. However, given an arbitrary view, it remains difficult to identify how to reuse the view for a particular query, because of the NP-hardness behind the algorithm matching patterns and views. To fully exploit the benefit of the materialized views, we propose a new paradigm to enhance the effectiveness of the materialized view. Instead of choosing materialized views in arbitrary form, our paradigm aims to select the views only if they are sortable. The property of sortability raises huge gains on the pattern-view matching, bringing down the cost to linear complexity in terms of the pattern size. On the other side, the costs on identifying sortable views and searching over the views using inverted index are affordable. Moreover, sortable views generally improve the overall performance of pattern matching, by means of a cost model used to optimize the query rewriting on the most appropriate views. Finally, we demonstrate extensive experimental results to verify the superiority of our proposal on both efficiency and effectiveness.
Community-based classification of noun phrases in twitter
Authors: Freddy Chong Tat Chua, William W. Cohen, Justin Betteridge, Ee-Peng Lim
Venue: CIKM
Year: 2012

Many event monitoring systems rely on counting known keywords in streaming text data to detect sudden spikes in frequency. But the dynamic and conversational nature of Twitter makes it hard to select known keywords for monitoring. Here we consider a method of automatically finding noun phrases (NPs) as keywords for event monitoring in Twitter. Finding NPs has two aspects, identifying the boundaries for the subsequence of words which represent the NP, and classifying the NP to a specific broad category such as politics, sports, etc. To classify an NP, we define the feature vector for the NP using not just the words but also the author's behavior and social activities. Our results show that we can classify many NPs by using a sample of training data from a knowledge-base.
Analyzing sentiments from street harassment stories
Authors: Parvathi Chundi, April L. Corbet
Venue: CIKM
Year: 2012

Street harassment is a pervasive problem that typically targets women and LGBTQ community. There are no effective ways to deal with the harassers as the acts of harassment happen randomly and are difficult, if not impossible, to prosecute. Hollaback! is an international movement aimed at stopping street harassment. Hollaback! servers collect street harassment stories from victims around the globe to share, gather statistics, and create awareness. In this paper, we present a preliminary study focused on analyzing a small sample of Hollaback! stories submitted from New York city. The LIWC software [1] is used to measure the positive and negative emotions hidden in each story and correlate it to the socio-economic status of the location from which the story was submitted.
Finding food entity relationships using user-generated data in recipe service
Authors: Young-joo Chung
Venue: CIKM
Year: 2012

Rakuten recipe is a recipe site where users can submit their recipes and share with the others. Since recipe contents are generated by users, they usually contain many misspellings, abbreviations, synonyms, hypernyms and hyponyms. Identifying and normalizing these words is essential to retrieve relevant recipes to user's request. In this paper, we introduce a new approach to finding related words in a recipe domain using the data structure. Based on the observation that people usually write the main ingredient in the first position of ingredient lists of each recipe and such a ingredient is strongly related to the categories where recipes belong, we calculate relation scores of word pairs using real service data, which contains 790 categories and 405,519 recipes. The experimental result showed that we successfully found semantically related word pairs with f-score of 0.93.
Concavity in IR models
Authors: Stéphane Clinchant
Venue: CIKM
Year: 2012

We study the impact of concavity in IR models and propose to use a generalized logarithm function, the n-logarithm to weight words in documents. We extend the family of information based Information Retrieval (IR) models with this function. We show that that concavity is indeed an important property of IR models. Experiments conducted for IR tasks, Latent Semantic Indexing and Text Categorization show improvements.
Learning similarity measures based on random walks
Authors: William W. Cohen
Venue: CIKM
Year: 2012

We describe a novel learnable proximity measure based on personalized PageRank (also known as "random walk with reset"). Instead of introducing one weight per edge label, as in most prior work, we introduce one weight for each edge label sequence. We show that this approach is advantageous for a number of real-world tasks, including querying graph databases, recommendation tasks, and inference in large, noisy knowledge bases.
Weighted linear kernel with tree transformed features for malware detection
Authors: Prakash Mandayam Comar, Lei Liu, Sabyasachi Saha, Antonio Nucci, Pang-Ning Tan
Venue: CIKM
Year: 2012

Malware detection from network traffic flows is a challenging problem due to data irregularity issues such as imbalanced class distribution, noise, missing values, and heterogeneous types of features. To address these challenges, this paper presents a two-stage classification approach for malware detection. The framework initially employs random forest as a macro-level classifier to separate the malicious from non-malicious network flows, followed by a collection of one-class support vector machine classifiers to identify the specific type of malware. A novel tree-based feature construction approach is proposed to deal with data imperfection issues. As the performance of the support vector machine classifier often depends on the kernel function used to compute the similarity between every pair of data points, designing an appropriate kernel is essential for accurate identification of malware classes. We present a simple algorithm to construct a weighted linear kernel on the tree transformed features and demonstrate its effectiveness in detecting malware from real network traffic data.
iTop: interaction based topic centric community discovery on twitter
Authors: Denzil Correa, Ashish Sureka, Mayank Pundir
Venue: CIKM
Year: 2012

Automatic detection of communities (or cohesive groups of actors in social network) in online social media platforms based on user interests and interaction is a problem that has recently attracted a lot of research attention. Mining user interactions on Twitter to discover such communities is a technically challenging information retrieval task. We present an algorithm - iTop - to discover interaction based topic centric communities by mining user interaction signals (such as @-messages and retweets) which indicate cohesion. iTop takes any topic as an input keyword and exploits local information to infer global topic-centric communities. We evaluate the discovered communities along three dimensions: graph based (node-edge quality), empirical-based (Twitter lists) and semantic based (frequent n-grams in tweets). We conduct experiments on a publicly available scrape of Twitter provided by InfoChimps via a web service. We perform a case study on two diverse topics - 'Computer Aided Design (CAD)' and 'Kashmir' to demonstrate the efficacy of iTop. Empirical results from both case studies show that iTop is successfully able to discover topic-centric, interaction based communities on Twitter.
Knowing where and how criminal organizations operate using web content
Authors: Michele Coscia, Viridiana Rios
Venue: CIKM
Year: 2012

We develop a framework that uses Web content to obtain quantitative information about a phenomenon that would otherwise require the operation of large scale, expensive intelligence exercises. Exploiting indexed reliable sources such as online newspapers and blogs, we use unambiguous query terms to characterize a complex evolving phenomena and solve a security policy problem: identifying the areas of operation and modus operandi of criminal organizations, in particular, Mexican drug trafficking organizations over the last two decades. We validate our methodology by comparing information that is known with certainty with the one we extracted using our framework. We show that our framework is able to use information available on the web to efficiently extract implicit knowledge about criminal organizations. In the scenario of Mexican drug trafficking, our findings provide evidence that criminal organizations are more strategic and operate in more differentiated ways than current academic literature thought.
Pay-as-you-go maintenance of precomputed nearest neighbors in large graphs
Authors: Tom Crecelius, Ralf Schenkel
Venue: CIKM
Year: 2012

An important building block of many graph applications such as searching in social networks, keyword search in graphs, and retrieval of linked documents is retrieving the transitive neighbors of a node in ascending order of their distances. Since large graphs cannot be kept in memory and graph traversals at query time would be prohibitively expensive, the list of neighbors for each node is usually precomputed and stored in a compact form. While the problem of precomputing all-pairs shortest distances has been well studied for decades, efficiently maintaining this information when the graph changes is not as well understood. This paper presents an algorithm for maintaining nearest neighbor lists in weighted graphs under node insertions and decreasing edge weights. It considers the important case where queries are a lot more frequent than updates, and presents two approaches for transparently performing necessary index updates while executing queries. Extensive experiments with large graphs, including a subset of Twitter's user graph, demonstrate that the overhead for this maintenance is small.
Semantically coherent image annotation with a learning-based keyword propagation strategy
Authors: Chaoran Cui, Jun Ma, Shuaiqiang Wang, Shuai Gao, Tao Lian
Venue: CIKM
Year: 2012

Automatic image annotation plays an important role in modern keyword-based image retrieval systems. Recently, many neighbor-based methods have been proposed and achieved good performance for image annotation. However, existing work mainly focused on exploring a distance metric learning algorithm to determine the neighbors of an image, and neglected the subsequent keyword propagation process. They usually used some simple heuristic propagation rules, and propagated each keyword independently without considering the inherent semantic coherence among keywords. In this paper, we propose a novel learning-based keyword propagation strategy and incorporate it into the neighbor-based method framework. In particular, we employ the structural SVM to learn a scoring function which can evaluate different candidate keyword sets for a test image. Moreover, we explicitly enforce the semantic coherence constraint for the propagated keywords in our approach. The annotation of the test image is propagated as a whole rather than separate keywords. Experiments on two benchmark data sets demonstrate the effectiveness of our approach for image annotation and ranked retrieval.
On the inference of average precision from score distributions
Authors: Ronan Cummins
Venue: CIKM
Year: 2012

Modelling the document scores returned from an IR system for a given query using parameterised score distributions is an area of research that has become more popular in recent years. Score distribution (SD) models are useful for a number of IR tasks. These include data fusion, query performance prediction, determining thresholds in filtering applications, and tasks in the area of distributed retrieval. The inference of performance metrics, such as average precision, from these SD models is an important consideration. In this paper, we study the accuracy of a number of methods of inferring average precision from an SD model.