OpenRaiser/Intern-Atlas huggingface dataset:
Preference query evaluation over expensive attributes
Authors: Justin J. Levandoski, Mohamed F. Mokbel, Mohamed E. Khalefa
Venue: CIKM
Year: 2010

Most database systems allow query processing over attributes that are derived at query runtime (e.g., user-defined functions and remote data calls to web services), making them expensive to compute relative to relational data stored in a heap or index. In addition, core support for efficient preference query processing has become an important objective in database systems. This paper addresses an important problem at the intersection of these two query processing objectives: efficient preference query evaluation involving expensive attributes. We explore an efficient framework for processing skyline and multi-objective queries in a database when the data involves a mix of "cheap" and "expensive" attributes. Our solution involves a three-phase approach that evaluates a correct final preference answer while aiming to minimizing the number of expensive attributes computations. Unlike previous works for distributed preference algorithms that assume sorted access over each attribute, our framework assumes expensive attribute requests are stateless, i.e., know nothing previous requests. Thus, the proposed approach is more in line with realistic system architectures. Our framework is implemented inside the query processor of PostgreSQL, and evaluated over both synthetic and real data sets involving computation of expensive attributes over real web-service data (e.g., Microsoft MapPoint).
Affinity-driven prediction and ranking of products in online product review sites
Authors: Hui Li, Sourav S. Bhowmick, Aixin Sun
Venue: CIKM
Year: 2010

Large online product review websites (e.g., Epinions, Blippr)to various types of products. Typically, each product in these sites is associated with a group of members who have provided ratings and comments on it. These people form a product community. A potential member can join a produce community by giving a new rating to the product. We refer to this phenomenon of a product community's ability to attract new members as product affinity. The knowledge of a ranked list of products based on product affinity is of much importance to be utilized for implementing policies, marketing research, online advertisement, and other applications. In this paper, we identify and analyze an array of features that exert effect on product affinity and propose a novel model, called AffRank, that utilizes these features to predict the future rank of products according to their affinities. Evaluated on a real-world dataset, we demonstrate the effectiveness and superior prediction quality of AffRank compared to baseline methods. Our experiments show that features such as affinity rank history, affinity evolution distance, and average rating are the most important factors affecting future rank of products.
Extending dictionary-based entity extraction to tolerate errors
Authors: Guoliang Li, Dong Deng, Jianhua Feng
Venue: CIKM
Year: 2010

Entity extraction (also known as entity recognition) extracts entities (e.g., person names, locations, companies) from text. Approximate (dictionary-based) entity extraction is a recent trend to improve extraction quality, which extracts substrings in text that approximately match predefined entities in a given dictionary. In this paper, we study the problem of approximate entity extraction with edit-distance constraints. A straightforward method first extracts all substrings from the text and then for each substring identifies its similar entities from the dictionary using existing methods for approximate string search. However many substrings of the text have overlaps, and we have an opportunity to utilize the shared computation across the overlaps to avoid unnecessary duplicate computations. To this end, we propose a heap-based framework to efficiently extract entities. We have implemented our techniques, and the experimental results show that our method achieves high performance and outperforms existing studies significantly.
Query optimization for ontology-based information integration
Authors: Yingjie Li, Jeff Heflin
Venue: CIKM
Year: 2010

In recent years, there has been an explosion of publicly avail-able RDF and OWL data sources. In order to effectively and quickly answer queries in such an environment, we present an approach to identifying the potentially relevant Semantic Web data sources using query rewritings and a term index. We demonstrate that such an approach must carefully han-dle query goals that lack constants; otherwise the algorithm may identify many sources that do not contribute to even-tual answers. This is because the term index only indicates if URIs are present in a document, and specific answers to a subgoal cannot be calculated until the source is physi-cally accessed- an expensive operation given disk/network latency. We present an algorithm that, given a set of query rewritings that accounts for ontology heterogeneity, incre-mentally selects and processes sources in order to maintain selectivity. Once sources are selected, we use an OWL rea-soner to answer queries over these sources and their corre-sponding ontologies. We present the results of experiments using both a synthetic data set and a subset of the real-world Billion Triple Challenge data.
Optimizing unified loss for web ranking specialization
Authors: Fan Li, Xin Li, Jiang Bian, Zhaohui Zheng
Venue: CIKM
Year: 2010

In this paper, we proposed a novel divide-and-conquer approach to optimize the overall relevance in an unified framework for query clustering and query-based ranking. In our model, latent topics and specialized ranking models are learned iteratively so that an unified objective function, which lower-bounds the conditional probability of observed grades annotated by human editors on training data, is maximized. We conducted experiments comparing the proposed method with several baseline approaches on two data-sets. Experimental results illustrate that our method can significantly improve the ranking relevance over these baselines
Multi-modal multi-correlation person-centric news retrieval
Authors: Zechao Li, Jing Liu, Xiaobin Zhu, Hanqing Lu
Venue: CIKM
Year: 2010

In this paper, we propose a framework of multi-modal multi-correlation person-centric news retrieval, which integrates news event correlations, news entity correlations, and event-entity correlations simultaneously by exploring both text and image information. The proposed framework is confined to a person-name query and enables a more vivid and informative person-centric news retrieval by providing two views of result presentation, namely a query-oriented multi-correlation map and a ranking list of news items with necessary descriptions including news image, news title and summary, central entities and relevant news events. First, we pre-process news articles using natural language techniques, and initialize the three correlations by statistical analysis about events and entities in news articles and face images. Second, a Multi-correlation Probabilistic Matrix Factorization (MPMF) algorithm is proposed to complete and refine the three correlations. Different from traditional Probabilistic Matrix Factorization (PMF), the proposed MPFM additionally considers the event correlations and the entity correlations as well as the event-entity correlations during the factor analysis. Third, the result ranking and visualization are conducted to present search results relevant to a target news topic. Experimental results on a news dataset collected from multiple news websites demonstrate the attractive performance of the proposed solution for news retrieval.
Quality factory and quality notification service in data warehouse
Authors: Yan Li, Kweku-Muata Osei-Bryson
Venue: CIKM
Year: 2010

Many modern organizations operate in a dynamic and large-scaled web-based environment that involves critical and frequent interactions with external organizations. Within this context, the associated data warehousing environment is often dynamic and evolutionary, with sometimes frequent changes in the quality of various resources (e.g. source systems, processes, metadata), and also in the users' quality requirements. There is thus the need to not only capture quality changes on-the-fly, but also provide automated quality notifications to relevant end users. Managing data warehousing systems in such environment imposes many new challenges for DW design, management and maintenance. In this paper, we propose an extended data warehousing systems architecture that incorporates and extends the concepts of the Quality Factory (QF) and the Quality Notification Service (QNS) that were previously presented in the Cooperative Information Systems (CIS) literature.
Automatic schema merging using mapping constraints among incomplete sources
Authors: Xiang Li, Christoph Quix, David Kensche, Sandra Geisler
Venue: CIKM
Year: 2010

Schema merging is the process of consolidating multiple schemas into a unified view. The task becomes particularly challenging when the schemas are highly heterogeneous and autonomous. Classical data integration systems rely on a mediated schema created by human experts through an intensive design process.
Exploiting novelty, coverage and balance for topic-focused multi-document summarization
Authors: Xuan Li, Yi-Dong Shen, Liang Du, Chen-Yan Xiong
Venue: CIKM
Year: 2010

Novelty, coverage and balance are important requirements in topic-focused summarization, which to a large extent determine the quality of a summary. In this paper, we propose a novel method that incorporates these requirements into a sentence ranking probability model. It differs from the existing methods in that the novelty, coverage and balance requirements are all modeled w.r.t. a given topic, so that summaries are highly relevant to the topic and at the same time comply with topic-aware novelty, coverage and balance. Experimental results on the DUC 2005, 2006 and 2007 benchmark data sets demonstrate the effectiveness of our method.
Approximate membership localization (AML) for web-based join
Authors: Zhixu Li, Laurianne Sitbon, Liwei Wang, Xiaofang Zhou, Xiaoyong Du
Venue: CIKM
Year: 2010

In this paper, we propose a search-based approach to join two tables in the absence of clean join attributes. Non-structured documents from the web are used to express the correlations between a given query and a reference list. To implement this approach, a major challenge we meet is how to efficiently determine the number of times and the locations of each clean reference from the reference list that is approximately mentioned in the retrieved documents. We formalize the Approximate Membership Localization (AML) problem and propose an efficient partial pruning algorithm to solve it. A study using real-word data sets demonstrates the effectiveness of our search-based approach, and the efficiency of our AML algorithm.
TC-DCA: a system for text classification based on document's content allocation
Authors: Wenbo Li, Le Sun, Zhenzhong Zhang, Xue Jiang, Weiru Zhang
Venue: CIKM
Year: 2010

The text classification methods heavily depend on machine learning algorithms with abstract mathematic metrics, which obstruct the direct observation and intuitive understanding of the text-specific classification. In this paper, we model a document as a Document-Classes-Topics top-down hierarchical structure. Furthermore, by running the document generation procedure, we can obtain each class's content share, which not only can be used to make the classification decision but also can provide a natural visualization approach for text classification. We implement this idea by a new tool named TC-DCA, which provides the visualization of text classification result, where the target document is expressed graphically as its content's allocation on every class. TC-DCA can also perform the drilling down operation to reveal the classification effect of each word of the document.
StableBuffer: optimizing write performance for DBMS applications on flash devices
Authors: Yu Li, Jianliang Xu, Byron Choi, Haibo Hu
Venue: CIKM
Year: 2010

Flash devices have been widely used in embedded systems, laptop computers, and enterprise servers. However, the poor random writes have been an obstacle to running write-intensive DBMS applications on flash devices. In this paper, we exploit the recently discovered, efficient write patterns of flash devices to optimize the performance of DBMS applications. Specifically, motivated by a focused write pattern, we propose to write pages temporarily to a small, pre-allocated storage space on the flash device, called StableBuffer, instead of directly writing to their actual destinations. We then recognize and flush efficient write patterns of the buffer to achieve a better write performance. In contrast to prior log-based techniques, our StableBuffer solution does not require modifying the driver of flash devices and hence works well for commodity flash devices. We discuss the detailed design and implementation of the StableBuffer solution. Performance evaluation based on a TPC-C benchmark trace shows that StableBuffer improves the response time and throughput of write operations by a factor of 1.5-12, in comparison with a direct write-through strategy.
Personalized recommender system based on item taxonomy and folksonomy
Authors: Huizhi Liang, Yue Xu, Yuefeng Li, Richi Nayak
Venue: CIKM
Year: 2010

Item folksonomy or tag information is popularly available on the web now. However, since tags are arbitrary words given by users, they contain a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise brings difficulties to improve the accuracy of item recommendations. In this paper, we propose to combine item taxonomy and folksonomy to reduce the noise of tags and make personalized item recommendations. The experiments conducted on the dataset collected from Amazon.com demonstrated the effectiveness of the proposed approaches. The results suggested that the recommendation accuracy can be further improved if we consider the viewpoints and the vocabularies of both experts and users.
Questions to be asked & answered as to NLP's role in improving semantic annotation
Authors: Elizabeth D. Liddy
Venue: CIKM
Year: 2010

In the realm of Information Retrieval, why is Semantic Annotation needed? What has changed? Is it the users, the sources, the genres, the technologies, the applications, the queries? If there are differences, why and how can Semantic Annotation help? And more specifically, how and what is Natural Language Processing (NLP) contributing.
Mr.KNN: soft relevance for multi-label classification
Authors: Xiaotong Lin, Xue-wen Chen
Venue: CIKM
Year: 2010

Multi-label classification refers to learning tasks with each instance belonging to one or more classes simultaneously. It arose from real-world applications such as information retrieval, text categorization and functional genomics. Currently, most of the multi-label learning methods use the strategy called binary relevance, which constructs a classifier for each unique label by grouping data into positives (examples with this label) and negatives (examples without this label). With binary relevance, an example with multiple labels is considered as a positive data for each label it belongs to. For some classes, this data point may behave like an outlier confusing classifiers, especially in the cases of well-separated classes. In this paper, we first introduce a new strategy called soft relevance, where each multi-label example is assigned a relevance score to the labels it belongs to. This soft relevance is then employed in a voting function used in a k nearest neighbor classifier. Furthermore, a voting-margin ratio is introduced to the k nearest neighbor classifier for better performance. We compare the proposed method to other multi-label learning methods over three multi-label datasets and demonstrate that the proposed method provides an effective way to multi-label learning.
Learning to rank with groups
Authors: Yuan Lin, Hongfei Lin, Zheng Ye, Song Jin, Xiaoling Sun
Venue: CIKM
Year: 2010

An essential issue in document retrieval is ranking, and the documents are ranked by their expected relevance to a given query. Multiple labels are used to represent different level of relevance for documents to a given query, and the corresponding label values are used to quantify the relevance of the documents. According to the training set for a given query, the documents can be divided into several groups. Specifically, the documents with the same label are assigned to the same group. If the documents in the group with higher relevance label can always be ranked higher over the ones in groups with lower relevance label by a ranking model, it is reasonable to expect perfect ranking performance. Inspired by this idea, we propose a novel framework for learning to rank, which depends on two new samples. The first one is one-group constituted by one document with higher level label and a group of documents with lower level label; the second one is group-group constituted by a group of documents with higher level label and a group of documents with lower level label. A novel loss function is proposed based on the likelihood loss similar to ListMLE. We demonstrate the advantages of our approaches on the Letor 3.0 data set. Experimental results show that our approaches are effective in improving the ranking performance.
Selective data acquisition for probabilistic K-NN query
Authors: Yu-Chieh Lin, De-Nian Yang, Ming-Syan Chen
Venue: CIKM
Year: 2010

Recently, management of uncertain data draws lots of attention to consider the granularity of devices and noises in collection and delivery of data. Previous works directly model and handle uncertain data to find the required results. However, when data uncertainty is not small or limited, users are not able to obtain useful insights and thereby tend to provide more resources to improve the solution, by reducing the uncertainty of data. In light of this issue, this paper formulates a new problem of choosing a given number of uncertain data objects for acquiring their attribute values to improve the solutions of Probabilistic k-Nearest-Neighbor (k-PNN) query. We prove that solutions must be better after data acquisition, and we devise algorithms to maximize expected improvement. Our experiment results demonstrate that the probability can be significantly improved with only a small number of data acquisitions.
Exploiting user interests for collaborative filtering: interests expansion via personalized ranking
Authors: Qi Liu, Enhong Chen, Hui Xiong, Chris H. Q. Ding
Venue: CIKM
Year: 2010

In real applications, a given user buys or rates an item based on his/her interests. Learning to leverage this interest information is often critical for recommender systems. However, in existing recommender systems, the information about latent user interests are largely under-explored. To that end, in this paper, we propose an interest expansion strategy via personalized ranking based on the topic model, named iExpand, for building an interest-oriented collaborative filtering framework. The iExpand method introduces a three-layer, user-interest-item, representation scheme, which leads to more interpretable recommendation results and helps the understanding of the interactions among users, items, and user interests. Moreover, iExpand strategically deals with many issues, such as the overspecialization and the cold-start problems. Finally, we evaluate iExpand on benchmark data sets, and experimental results show that iExpand outperforms state-of-the-art methods.
Ontology emergence from folksonomies
Authors: Kaipeng Liu, Binxing Fang, Weizhe Zhang
Venue: CIKM
Year: 2010

The folksonomies built from the large-scale social annotations made by collaborating users are perfect data sources for bootstrapping Semantic Web applications. In this paper, we develop an ontology induction approach to harvest the emergent semantics from the folksonomies. We propose a latent subsumption hierarchy model to uncover the implicit structure of tag space and develop our ontology induction approach on basis of this model. We identify tag subsumptions with a set-theoretical approach and model the tag space as a tag subsumption graph. While turning this graph into a concept hierarchy, we address the problem of inconsistent subsumptions and propose a random walk based tag generality ranking procedure to settle it. We propose an agglomerative hierarchical clustering algorithm utilizing the result of tag generality ranking to generate the concept hierarchy. We conduct experiments on the Delicious dataset. The results of both qualitative and quantitative evaluation demonstrate the effectiveness of the proposed approach.
Mining topic-level influence in heterogeneous networks
Authors: Lu Liu, Jie Tang, Jiawei Han, Meng Jiang, Shiqiang Yang
Venue: CIKM
Year: 2010

Influence is a complex and subtle force that governs the dynamics of social networks as well as the behaviors of involved users. Understanding influence can benefit various applications such as viral marketing, recommendation, and information retrieval. However, most existing works on social influence analysis have focused on verifying the existence of social influence. Few works systematically investigate how to mine the strength of direct and indirect influence between nodes in heterogeneous networks.
Automatically weighting tags in XML collection
Authors: Dexi Liu, Changxuan Wan, Lei Chen, Xiping Liu
Venue: CIKM
Year: 2010

In XML retrieval, nodes with different tags play different roles in XML documents and then tags should be reflected in the relevance ranking. An automatic method is proposed in this paper to infer the weights of tags. We first investigate 15 features about tags, and then select five of them based on the correlations between these features and manual tag weights. Using these features, a tag weight assignment model, ATG, is designed. We evaluate the performance of ATG on two real data sets, IEEECS and Wikipedia from two different perspectives. One is to evaluate the quality of the model by measuring the correlation between weights generated by our model and those given by experts. The other is to test the effectiveness of the model in improving retrieval performance. Experimental results show that the tag weights generated by ATG are highly correlated with the manually assigned weights and the ATG model improves retrieval effectiveness significantly.
Weighting common syntactic structures for natural language based information retrieval
Authors: Chang Liu, Hui Wang, Sally I. McClean, Epaminondas Kapetanios, Denis Carroll
Venue: CIKM
Year: 2010

Natural Language Processing (NLP) techniques are believed to hold the potential to assist "bag-of-words" Information Retrieval (IR) in terms of retrieval accuracy. In this paper, we report a natural language based IR approach where the common syntactic structures between documents and the query is regarded to as a query-dependent feature for documents. Specifically, a "structural weight" is proposed for query terms, which can be seen as a weight to model the degree of term's involvement in the common syntactic structures. This structural weight is used together with the TF-IDF weighting scheme, which results in a new ranking function. The accumulation of this structural weight of all the query terms in the new ranking function will be seen as a measure of how much a document and a query share the common syntactic structures. The experimental results show that by using this ranking function, significant improvements in the retrieval performance are achieved.