OpenRaiser/Intern-Atlas huggingface dataset:
Simultaneous joint and conditional modeling of documents tagged from two perspectives
Authors: Pradipto Das, Rohini K. Srihari, Yun Fu
Venue: CIKM
Year: 2011

This paper explores correspondence and mixture topic modeling of documents tagged from two different perspectives. There has been ongoing work in topic modeling of documents with tags (tag-topic models) where words and tags typically reflect a single perspective, namely document content. However, words in documents can also be tagged from different perspectives, for example, syntactic perspective as in part-of-speech tagging or an opinion perspective as in sentiment tagging. The models proposed in this paper are novel in: (i) the consideration of two different tag perspectives -- a document level tag perspective that is relevant to the document as a whole and a word level tag perspective pertaining to each word in the document; (ii) the attribution of latent topics with word level tags and labeling latent topics with images in case of multimedia documents; and (iii) discovering the possible correspondence of the words to document level tags. The proposed correspondence tag-topic model shows better predictive power i.e. higher likelihood on heldout test data than all existing tag topic models and even a supervised topic model. To evaluate the models in practical scenarios, quantitative measures between the outputs of the proposed models and the ground truth domain knowledge have been explored. Manually assigned (gold standard) document category labels in Wikipedia pages are used to validate model-generated tag suggestions using a measure of pairwise concept similarity within an ontological hierarchy like WordNet. Using a news corpus, automatic relationship discovery between person names was performed and compared to a robust baseline.
Web science and information exchange in the medical web
Authors: Kerstin Denecke, Peter Dolog
Venue: CIKM
Year: 2011

The amount of social media data dealing with medical and health issues increased significantly in the last couple of years. Medical social media data now provides a new source of information within information gaining contexts. Facts, experiences, opinions or information on behavior can be found in the Medicine 2.0 or Health 2.0 and could support a broad range of applications. This workshop is devoted to the technologies for dealing with social- and multi media for medical information gathering and exchange. This specific data and the processes of information gathering poses many challenges given the increasing content on the Web and the trade off of filtering noise at the cost of losing information which is potentially relevant.
Predicting the optimal ad-hoc index for reachability queries on graph databases
Authors: Jintian Deng, Fei Liu, Yun Peng, Byron Choi, Jianliang Xu
Venue: CIKM
Year: 2011

Due to the recent advances in graph databases, a large number of ad-hoc indexes for a fundamental query, in particular, reachability query, have been proposed. The performances of these indexes on different graphs have known to be very different. Worst still, deriving an accurate cost model for selecting the optimal index of a graph database appears to be a daunting task. In this paper, we propose a hierarchical prediction framework, based on neural networks and a set of graph features and a knowledge base on past predictions, to determine the optimal index for a graph database. For ease of presentation, we propose our framework with three structurally distinguishable indexes. Our experiments show that our framework is accurate.
Information re-finding by context: a brain memory inspired approach
Authors: Tangjian Deng, Liang Zhao, Ling Feng, Wenwei Xue
Venue: CIKM
Year: 2011

Re-finding what we have accessed before is a common behavior in real life. Psychological studies show that context under which information was accessed can serve as a powerful cue for information recall. "Finding the sweet recipe that I read at the hotel on the trip to Africa last year" is a context-based re-finding request example. Inspired by users' recall characteristics and human memory, we present a context memory model, where each context unit links to the data created/accessed before. Context units are organized in a clustering and associative manner, and evolve dynamically in life cycles. Based on the context memory, we build a recall-by-context query model. Two methods are devised to evaluate context-based recall queries. Our experiments with synthetic and real data show that evaluation exploring the use of context associations can get the best response time.
User action interpretation for personalized content optimization in recommender systems
Authors: Anlei Dong, Jiang Bian, Xiaofeng He, Srihari Reddy, Yi Chang
Venue: CIKM
Year: 2011

User interaction plays a vital role in recommender systems. Previous studies on algorithmic recommender systems have mainly focused on modeling techniques and feature development. Traditionally, implicit user feedback or explicit user ratings on the recommended items form the basis for designing and training of recommendation algorithms. But user interactions in real-world Web applications (e.g., a portal website with different recommendation modules in the interface) are unlikely to be as ideal as those assumed by previously proposed models. To address this problem, we build an online learning framework for personalized recommendation. We argue that appropriate user action interpretation is critical for a recommender system. The main contribution in this paper is an approach of interpreting users' actions for the online learning to achieve better item relevance estimation. Our experiments on the large-scale data from a commercial Web recommender system demonstrate significant improvement in terms of a precision metric over the baseline model that does not incorporate user action interpretation. The efficacy of this new algorithm is also proved by the online test results on real user traffic.
e-NSP: efficient negative sequential pattern mining based on identified positive patterns without database rescanning
Authors: Xiangjun Dong, Zhigang Zheng, Longbing Cao, Yanchang Zhao, Chengqi Zhang, Jinjiu Li, Wei Wei, Yuming Ou
Venue: CIKM
Year: 2011

Mining Negative Sequential Patterns (NSP) is much more challenging than mining Positive Sequential Patterns (PSP) due to the high computational complexity and huge search space required in calculating Negative Sequential Candidates (NSC). Very few approaches are available for mining NSP, which mainly rely on re-scanning databases after identifying PSP. As a result, they are very inefficient. In this paper, we propose an efficient algorithm for mining NSP, called e-NSP, which mines for NSP by only involving the identified PSP, without re-scanning databases. First, negative containment is defined to determine whether or not a data sequence contains a negative sequence. Second, an efficient approach is proposed to convert the negative containment problem to a positive containment problem. The supports of NSC are then calculated based only on the corresponding PSP. Finally, a simple but efficient approach is proposed to generate NSC. With e-NSP, mining NSP does not require additional database scans, and the existing PSP mining algorithms can be integrated into e-NSP to mine for NSP efficiently. e-NSP is compared with two currently available NSP mining algorithms on 14 synthetic and real-life datasets. Intensive experiments show that e-NSP takes as little as 3% of the runtime of the baseline approaches and is applicable for efficient mining of NSP in large datasets.
Finding dimensions for queries
Authors: Zhicheng Dou, Sha Hu, Yulong Luo, Ruihua Song, Ji-Rong Wen
Venue: CIKM
Year: 2011

We address the problem of finding multiple groups of words or phrases that explain the underlying query facets, which we refer to as query dimensions. We assume that the important aspects of a query are usually presented and repeated in the query's top retrieved documents in the style of lists, and query dimensions can be mined out by aggregating these significant lists. Experimental results show that a large number of lists do exist in the top results, and query dimensions generated by grouping these lists are useful for users to learn interesting knowledge about the queries.
P2Prec: a social-based P2P recommendation system
Authors: Fady Draidi, Esther Pacitti, Didier Parigot, Guillaume Verger
Venue: CIKM
Year: 2011

P2Prec is a social-based P2P recommendation system for large-scale content sharing that leverages content-based and social-based recommendation. The main idea is to recommend high quality documents related to query topics and contents held by useful friends (of friends) of the users, by exploiting friendship networks. We have implemented a prototype of P2Prec using the Shared-Data Overlay Network (SON), an open source development platform for P2P networks using web services, JXTA and OSGi. In this paper, we describe the demo of P2Prec's main services (installing P2Prec peers, initializing peers, gossiping topics of interest among friends, key-word querying for contents) using our prototype implemented as an application of SON.
ReDRIVE: result-driven database exploration through recommendations
Authors: Marina Drosou, Evaggelia Pitoura
Venue: CIKM
Year: 2011

Typically, users interact with database systems by formulating queries. However, many times users do not have a clear understanding of their information needs or the exact content of the database, thus, their queries are of an exploratory nature. In this paper, we propose assisting users in database exploration by recommending to them additional items that are highly related with the items in the result of their original query. Such items are computed based on the most interesting sets of attribute values (or faSets) that appear in the result of the original user query. The interestingness of a faSet is defined based on its frequency both in the query result and in the database instance. Database frequency estimations rely on a novel approach that employs an e-tolerance closed rare faSets representation. We report evaluation results of the efficiency and effectiveness of our approach on both real and synthetic datasets.
Automatic query reformulation with syntactic operators to alleviate search difficulty
Authors: Huizhong Duan, Rui Li, ChengXiang Zhai
Venue: CIKM
Year: 2011

Modern search engines usually provide a query language with a set of advanced syntactic operators (e.g., plus sign to require a term's appearance, or quotation marks to require a phrase's appearance) which if used appropriately, can significantly improve the effectiveness of a plain keyword query. However, they are rarely used by ordinary users due to the intrinsic difficulties and users' lack of corpora statistics. In this paper, we propose to automatically reformulate queries that do not work well by selectively adding syntactic operators. Particularly, we propose to perform syntactic operator-based query reformulation when a retrieval system detects users encounter difficulty in search as indicated by users' behaviors such as scanning over top k documents without click-through. We frame the problem of automatic reformulation with syntactic operators as a supervised learning problem, and propose a set of effective features to represent queries with syntactic operators. Experiment results verify the effectiveness of the proposed method and its applicability as a query suggestion mechanism for search engines. As a negative feedback strategy, syntactic operator-based query reformulation also shows promising results in improving search results for difficult queries as compared with existing methods.
Keyword search over RDF graphs
Authors: Shady Elbassuoni, Roi Blanco
Venue: CIKM
Year: 2011

Large knowledge bases consisting of entities and relationships between them have become vital sources of information for many applications. Most of these knowledge bases adopt the Semantic-Web data model RDF as a representation model. Querying these knowledge bases is typically done using structured queries utilizing graph-pattern languages such as SPARQL. However, such structured queries require some expertise from users which limits the accessibility to such data sources. To overcome this, keyword search must be supported. In this paper, we propose a retrieval model for keyword queries over RDF graphs. Our model retrieves a set of subgraphs that match the query keywords, and ranks them based on statistical language models. We show that our retrieval model outperforms the-state-of-the-art IR and DB models for keyword search over structured data using experiments over two real-world datasets.
When close enough is good enough: approximate positional indexes for efficient ranked retrieval
Authors: Tamer Elsayed, Jimmy Lin, Donald Metzler
Venue: CIKM
Year: 2011

Previous research has shown that features based on term proximity are important for effective retrieval. However, they incur substantial costs in terms of larger inverted indexes and slower query execution times as compared to term-based features. This paper explores whether term proximity features based on approximate term positions are as effective as those based on exact term positions. We introduce the novel notion of approximate positional indexes based on dividing documents into coarse-grained buckets and recording term positions with respect to those buckets. We propose different approaches to defining the buckets and compactly encoding bucket ids. In the context of linear ranking functions, experimental results show that features based on approximate term positions are able to achieve effectiveness comparable to exact term positions, but with smaller indexes and faster query evaluation.
HealthTrust: trust-based retrieval of you tube's diabetes channels
Authors: Luis Fernández-Luque, Randi Karlsen, Genevieve B. Melton
Venue: CIKM
Year: 2011

The Internet has become one of the main sources of consumer health information. Health consumers have access to ever-growing health information resources, especially since the rise of the Social Media. For example, over 20.000 videos have been uploaded by American hospitals on to YouTube. To find health videos is challenging because of factors like tags spamming and misleading information. Previous studies have found difficulties when searching for good health videos in YouTube, including false information (e.g., herbal cures for diabetes or cancer).
Scalable similarity search of timeseries with variable dimensionality
Authors: Omar U. Florez, Curtis E. Dyreson
Venue: CIKM
Year: 2011

Timeseries can be similar in shape but differ in length. For example, the sound waves produced by the same word spoken twice have roughly the same shape, but one may be shorter in duration. Stream data mining, approximate querying of image and video databases, data compression, and near duplicate detection are applications that need to be able to classify or cluster such timeseries, and to search for and rank timeseries that are similar to a chosen timeseries. We demonstrate software for clustering and performing similarity search in databases of timeseries data, where the timeseries have high and variable dimensionality. Our demonstration uses Timeseries Sensitive Hashing (TSH)[3] to index the timeseries. TSH adapts Locality Sensitive Hashing (LSH), which is an approximate algorithm to index data points in a d-dimensional space under some (e.g., Euclidean) distance function. TSH, unlike LSH, can index points that do not have the same dimensionality. As examples of the potential of TSH, the demonstration will index and classify timeseries from an image database and timeseries describing human motion extracted from a video stream and a motion capture system.
Do they belong to the same class: active learning by querying pairwise label homogeneity
Authors: Yifan Fu, Bin Li, Xingquan Zhu, Chengqi Zhang
Venue: CIKM
Year: 2011

Traditional active learning methods request experts to provide ground truths to the queried instances, which can be expensive in practice. An alternative solution is to ask nonexpert labelers to do such labeling work, which can not tell the definite class labels. In this paper, we propose a new active learning paradigm, in which a nonexpert labeler is only asked "whether a pair of instances belong to the same class". To instantiate the proposed paradigm, we adopt the MinCut algorithm as the base classifier. We first construct a graph based on the pairwise distance of all the labeled and unlabeled instances and then repeatedly update the unlabeled edge weights on the max-flow paths in the graph. Finally, we select an unlabeled subset of nodes with the highest prediction confidence as the labeled data, which are included into the labeled data set to learn a new classifier for the next round of active learning. The experimental results and comparisons, with state-of-the-art methods, demonstrate that our active learning paradigm can result in good performance with nonexpert labelers.
Uncertain schema matching: the power of not knowing
Authors: Avigdor Gal
Venue: CIKM
Year: 2011

Schema matching is the task of providing correspondences between concepts describing the meaning of data in various heterogeneous, distributed data sources. Schema matching is one of the basic operations required by the process of data and schema integration, and thus has a great effect on its outcomes, whether these involve targeted content delivery, view integration, database integration, query rewriting over heterogeneous sources, duplicate data elimination, or automatic streamlining of workflow activities that involve heterogeneous data sources. Although schema matching research has been ongoing for over 25 years, more recently a realization has emerged that schema matchers are inherently uncertain. Since 2003, work on the uncertainty in schema matching has picked up, along with research on uncertainty in other areas of data management. This lecture presents various aspects of uncertainty in schema matching within a single unified framework. We introduce basic formulations of uncertainty and provide several alternative representations of schema matching uncertainty. Then, we cover two common methods that have been proposed to deal with uncertainty in schema matching, namely ensembles, and top-K matchings, and analyze them in this context. We conclude with a set of real-world applications.
Defining isochrones in multimodal spatial networks
Authors: Johann Gamper, Michael H. Böhlen, Willi Cometti, Markus Innerebner
Venue: CIKM
Year: 2011

An isochrone in a spatial network is the minimal, possibly disconnected subgraph that covers all locations from where a query point is reachable within a given time span and by a given arrival time. In this paper we formally define isochrones for multimodal spatial networks with different transportation modes that can be discrete or continuous in, respectively, space and time. For the computation of isochrones we propose the multimodal incremental network expansion (MINE) algorithm, which is independent of the actual network size and depends only on the size of the isochrone. An empirical study using real-world data confirms the analytical results.
A cross-domain adaptation method for sentiment classification using probabilistic latent analysis
Authors: Sheng Gao, Haizhou Li
Venue: CIKM
Year: 2011

Sentiment classification is becoming attractive in recent years because of its potential commercial applications. It exploits supervised learning methods to learn the classifiers from the annotated training documents. The challenge in sentiment classification lies in that the sentiment domains are diverse, heterogeneous and fast-growing. The classifiers trained on one domain (source domain) could not classify a document from another domain (target domain). The domain adaptation technique is to address the problem by making use of labeled samples in the source domain, and unlabeled samples in the target domain. This paper presents a new solution, a cross-domain topic indexing (CDTI) method, with which a common semantic space is found from the prior between-domain term correspondences and the term co-occurrences in the cross-domain documents. These observations are characterized with the mixture model in CDTI, with each component being a possible topic shared by the source and target domains. Such common topics are found to index the cross-domain content. We evaluate the algorithms on a multi-domain sentiment classification task, which shows that CDTI outperforms the state-of-the-art domain adaptation method, i.e. spectral feature alignment (SFA), and the traditional latent semantic indexing method.
PCMLogging: reducing transaction logging overhead with PCM
Authors: Shen Gao, Jianliang Xu, Bingsheng He, Byron Choi, Haibo Hu
Venue: CIKM
Year: 2011

Phase Changing Memory (PCM), as one of the most promising next-generation memory technologies, offers various attractive properties such as non-volatility, bit-alterability, and low idle energy consumption. In this paper, we present PCMLogging, a novel logging scheme that exploits PCM devices for both data buffering and transaction logging in disk-based databases. Different from the traditional approach where buffered updates and transaction logs are completely separated, they are integrated in the new logging scheme. Our preliminary experiments show an up to 40% improvement of PCMLogging in disk I/O performance in comparison with a basic buffering and logging scheme.
ONTOCUBE: efficient ontology extraction using OLAP cubes
Authors: Carlos Garcia-Alvarado, Zhibo Chen, Carlos Ordonez
Venue: CIKM
Year: 2011

Ontologies are knowledge conceptualizations of a particular domain and are commonly represented with hierarchies. While final ontologies appear deceivingly simple on paper, building ontologies represents a time-consuming task that is normally performed by natural language processing techniques or schema matching. On the other hand, OLAP cubes are most commonly used during decision-making processes via the analysis of data summarizations. In this paper, we present a novel approach based on using OLAP cubes for ontology extraction. The resulting ontology is obtained through an analytical process of the summarized frequencies of keywords within a corpus. The solution was implemented within a relational database system (DBMS). In our experiments, we show how all the proposed discrimination measures (frequency, correlation, lift) affect the resulting classes. We also show a sample ontology result and the accuracy of finding true classes. Finally, we show the performance breakdown of our algorithm.
Integrating and querying web databases and documents
Authors: Carlos Garcia-Alvarado, Carlos Ordonez
Venue: CIKM
Year: 2011

There exist many interrelated information sources on the Internet that can be categorized into structured (database) and semistructured (documents). A key challenge is to integrate, query and analyze such heterogeneous collections of information. In this paper, we defend the idea of building web metadata repositories using relational databases as the main source and central data management technology of structured data, enriched by the semistructured data surrounding it. Our proposal rests on the assumption that heterogeneous relational databases can be integrated (i.e. entity resolution is assumed to work well) and thus can serve as references for external data. That is, we tackle the problem of integrating information in the deep web, departing from databases. We discuss a prototype system that can integrate and query metadata and related documents, based on relational database technology. Metadata includes database ER model elements like database name, table, and column (entity, attribute). Web document data include files, documents and web pages. Links between metadata and external documents are built with SQL queries. Once databases and documents are linked, they are managed and queried with SQL. We discuss an interesting scientific application of our solution with a water pollution database.