OpenRaiser/Intern-Atlas huggingface dataset:
Orientation distance-based discriminative feature extraction for multi-class classification
Authors: Bo Liu, Yanshan Xiao, Longbing Cao, Philip S. Yu
Venue: CIKM
Year: 2010

Feature extraction is an effective step in data mining and machine learning. While many feature extraction methods have been proposed for clustering, classification and regression, very limited work has been done on multi-class classification problems. In fact, the accuracy of multi-class classification problems relies on well-extracted features, the modeling part aside. This paper proposes a new feature extraction method, namely extracting orientation distance-based discriminative (ODD) features, which is particularly designed for multi-class classification problems. The proposed method works in two steps. In the first step, we extend the Fisher Discriminant idea to determine more appropriate kernel function and map the input data with all classes into a feature space. In the second step, the ODD features are extracted based on the one-vs-all scheme to generate discriminative features between a pattern and each hyperplane. These newly extracted features are treated as the representative features and are further used in the subsequent classification procedure. Substantial experiments on both UCI and real-world datasets have been conducted to investigate the performance of ODD features based multi-class classification. The statistical results show that the classification accuracy based on ODD features outperforms that of the state-of-the-art feature extraction methods.
Ranking with auxiliary data
Authors: Bo Long, Yi Chang, Srinivas Vadrevu, Shuang-Hong Yang, Zhaohui Zheng
Venue: CIKM
Year: 2010

Learning to rank arises in many information retrieval applications, ranging from Web search engine, online advertising to recommendation system. In learning to rank, the performance of a ranking function heavily depends on the number of labeled examples in the training set; on the other hand, obtaining labeled examples for training data is very expensive and time-consuming. This presents a great need for making use of available auxiliary data, i.e., the within-domain unlabeled data and the out-of-domain labeled data. In this paper, we propose a general framework for ranking with auxiliary data, which is applicable to various ranking applications. Under this framework, we derive a generic ranking algorithm to effectively make use of both the within-domain unlabeled data and the out-of-domain labeled data. The proposed algorithm iteratively learns ranking functions for target domain and source domains and enforces their consensus on the unlabeled data in the target domain.
Visualization and clustering of crowd video content in MPCA subspace
Authors: Haiping Lu, How-Lung Eng, Myo Thida, Konstantinos N. Plataniotis
Venue: CIKM
Year: 2010

This paper presents a novel approach for the visualization and clustering of crowd video contents by using multilinear principal component analysis (MPCA). In contrast to feature-point-based approach and frame-based dimensionality reduction approach, the proposed method maps each short video segment to a point in MPCA subspace to take temporal information into account naturally through tensorial representations. Specifically, MPCA projects each short segment of a video to a low-dimensional tensor first. A few MPCA features are then selected according to the variance captured as the final representation. Thus, a video is visualized as a trajectory in MPCA subspace. The trajectory generated enables visual interpretation of video content in a compact space as well as visual clustering of video events. The proposed method is evaluated on the PETS 2009 datasets through comparison with three existing methods for video visualization. The MPCA visualization shows superior performance in clustering segments of the same event as well as identifying the transitions between events.
Multi-information fusion for uncertain semantic representations of videos
Authors: Bo Lu, Guoren Wang, Xiaofeng Gong
Venue: CIKM
Year: 2010

Concept-Based Semantic Video Retrieval(CBSVR) usually uses semantic representations of videos to handle user's retrieval requests. It is obvious that the accuracy of semantic video retrieval depends on results of concept detectors, but the detection results are usually imprecise and uncertain . In this paper, we propose a multi-information fusion approach (MIF) which is dedicated to solving the problem of uncertain semantic representations of videos for improving retrieval accuracy. This approach is based on a novel two-phase framework that involves the inferring phase and the fusing phase. In the inferring phase, the most relevant concepts to the user's query are chosen by exploring both contextual correlation among concepts and temporal correlation among shots. In the fusing phase, the inferred probabilities of the related concepts are fused together with the detection results via minimization of potential function to refine the detector prediction. Experiments on the widely used TRECVID datasets demonstrate that our approach can effectively improve the accuracy of semantic concept detection.
3rd international workshop on patent information retrieval (PaIR'10)
Authors: Mihai Lupu, John Tait, Katja Mayer, Christopher G. Harris
Venue: CIKM
Year: 2010

The 3rd International Workshop on Patent Information Retrieval builds on the experiences of the first two workshops, to provide its participants an exciting, scientifically challenging and interactive event, where the specific issues of patent retrieval may be put into the general context of Information Retrieval and Knowledge Management, in order to explore innovative solutions to new and old problems, but also to evaluate and adapt traditional or classic approaches to new problems. Between the scientific presentations and posters, distinguished keynote speakers and a panel discussion, PaIR 2010 shapes itself into a significant landmark in the field of domain specific information retrieval.
Discovery of numerous specific topics via term co-occurrence analysis
Authors: Omid Madani, Jiye Yu
Venue: CIKM
Year: 2010

We describe efficient techniques for construction of large term co-occurrence graphs, and investigate an application to the discovery of numerous fine-grained (specific) topics. A topic is a small dense subgraph discovered by a random walk initiated at a term (node) in the graph. We observe that the discovered topics are highly interpretable, and reveal the different meanings of terms in the corpus. We show the information-theoretic utility of the topics when they are used as features in supervised learning. Such features lead to consistent improvements in classification accuracy over the standard bag-of-words representation, even at high training proportions. We explain how a layered pyramidal view of the term distribution helps in understanding the algorithms and in visualizing and interpreting the topics.
Expansion and search in networks
Authors: Arun S. Maiya, Tanya Y. Berger-Wolf
Venue: CIKM
Year: 2010

Borrowing from concepts in expander graphs, we study the expansion properties of real-world, complex networks (e.g. social networks, unstructured peer-to-peer or P2P networks) and the extent to which these properties can be exploited to understand and address the problem of decentralized search. We first produce samples that concisely capture the overall expansion properties of an entire network, which we collectively refer to as the expansion signature. Using these signatures, we find a correspondence between the magnitude of maximum expansion and the extent to which a network can be efficiently searched. We further find evidence that standard graph-theoretic measures, such as average path length, fail to fully explain the level of "searchability" or ease of information diffusion and dissemination in a network. Finally, we demonstrate that this high expansion can be leveraged to facilitate decentralized search in networks and show that an expansion-based search strategy outperforms typical search methods.
On the definition of patterns for semantic annotation
Authors: Mónica Marrero, Julián Urbano, Jorge Morato, Sonia Sánchez-Cuadrado
Venue: CIKM
Year: 2010

The semantic annotation of documents is an additional advantage for retrieval, as long as the annotations and their maintenance process scale well. Automatic or semi-automatic annotation tools help in this matter with the use of patterns. In this paper we analyze the advantages of creating these patterns with standard web languages, as well as the requirements they should meet. We adopt the Speech Recognition Grammar Specification, by the W3C, initially intended for speech recognition in the Web. Our objective is to achieve its full adaptation to the information extraction processes, exploiting its powerful recognition, reuse and flexibility capabilities.
Modeling betweenness for question answering
Authors: Brandeis Marshall
Venue: CIKM
Year: 2010

The gap between the user's information need and query is expanding due to the pervasiveness of Web search. Multimedia question answering is restricted by disjoint infrastructures of text, image, video and audio content. We address the construction and integration of multiple sources of related information. In particular, we consider the challenging information need of betweenness and defining it for any form of multimedia. We motivate betweenness definition using four questions and suggest k nearest neighbor as a first-step solution.
The surplus value of semantic annotations
Authors: Maarten Marx
Venue: CIKM
Year: 2010

We compare the costs of semantic annotation of textual documents to its benefits for information processing tasks. Semantic annotation can improve the performance of retrieval tasks and facilitates an improved search experience through faceted search, focused retrieval, better document summaries, and result grouping. Applications which summarize large collections of text or explain real world phenomena based on textual evidence may receive even more benefit from semantic annotations.
A peer-selection algorithm for information retrieval
Authors: Yosi Mass, Yehoshua Sagiv, Michal Shmueli-Scheuer
Venue: CIKM
Year: 2010

A novel method for creating collection summaries is developed, and a fully decentralized peer-selection algorithm is described. This algorithm finds the most promising peers for answering a given query. Specifically, peers publish per-term synopses of their documents. The synopses of a peer for a given term are divided into score intervals and for each interval, a KMV (K Minimal Values) synopsis of its documents is created. The synopses are used to effectively rank peers by their relevance to a multi-term quer. The proposed approach is verified by experiments on a large real-world dataset. In particular, two collections were created from this dataset, each with a different number of peers. Compared to the state-of-the-art approaches, the proposed method is effective and efficient even when documents are randomly distributed among peers
Supervised identification and linking of concept mentions to a domain-specific ontology
Authors: Gabor Melli, Martin Ester
Venue: CIKM
Year: 2010

We propose a pipelined supervised learning approach named SDOI to the task of interlinking the concepts mentioned within a document to the concepts within an ontology. Concept mention identification is performed by training a sequential tagging model. Each identified concept mention is then associated with a set of candidate ontology concepts along with a feature vector based on features proposed in the literature and novel ones based on new data sources, such as from the training corpus itself. An iterative algorithm is defined for handling collective features. We show a lift in performance over applicable baselines against the ability to identify the concept mentions within the 139 KDD-2009 conference paper abstracts, and to link these concept mentions to a domain-specific ontology for the field of data mining. Additional experiments of 22 ICDM-2009 abstracts suggest that the trained models are portable both in terms of accuracy and in their ability to reduce annotation time.
MENTA: inducing multilingual taxonomies from wikipedia
Authors: Gerard de Melo, Gerhard Weikum
Venue: CIKM
Year: 2010

In recent years, a number of projects have turned to Wikipedia to establish large-scale taxonomies that describe orders of magnitude more entities than traditional manually built knowledge bases. So far, however, the multilingual nature of Wikipedia has largely been neglected. This paper investigates how entities from all editions of Wikipedia as well as WordNet can be integrated into a single coherent taxonomic class hierarchy. We rely on linking heuristics to discover potential taxonomic relationships, graph partitioning to form consistent equivalence classes of entities, and a Markov chain-based ranking approach to construct the final taxonomy. This results in MENTA (Multilingual Entity Taxonomy), a resource that describes 5.4 million entities and is presumably the largest multilingual lexical knowledge base currently available.
Identifying new categories in community question answering archives: a topic modeling approach
Authors: Yajie Miao, Chunping Li, Jie Tang, Lili Zhao
Venue: CIKM
Year: 2010

Community Question Answering (CQA) services have evolved into a popular way of information seeking and providing. User-posted questions in CQA are generally organized into hierarchical categories. In this paper, we define and study a novel problem which is referred to as New Category Identification (NCI) in CQA question archives. New Category Identification is primarily concerned with detecting and characterizing new or emerging categories which are not included in the existing category hierarchy. We define this problem formally, and propose both unsupervised and semi-supervised topic modeling methods to solve it. Experiments with a ground-truth set built from Yahoo! Answers show that our methods identify and interpret new categories effectively.
Exploring domain-specific term weight in archived question search
Authors: Zhaoyan Ming, Tat-Seng Chua, Gao Cong
Venue: CIKM
Year: 2010

Community Question Answering services, e.g., Yahoo! Answers, have accumulated large archives of question answer (QA) pairs for information and answer retrieval. An effective question retrieval model is essential to increase the accessibility of the QA archives. QA archives are usually organized into categories and question search can be performed within the whole collection or within a certain category..
Collaborative future event recommendation
Authors: Einat Minkov, Ben Charrow, Jonathan Ledlie, Seth J. Teller, Tommi S. Jaakkola
Venue: CIKM
Year: 2010

We demonstrate a method for collaborative ranking of future events. Previous work on recommender systems typically relies on feedback on a particular item, such as a movie, and generalizes this to other items or other people. In contrast, we examine a setting where no feedback exists on the particular item. Because direct feedback does not exist for events that have not taken place, we recommend them based on individuals' preferences for past events, combined collaboratively with other peoples' likes and dislikes. We examine the topic of unseen item recommendation through a user study of academic (scientific) talk recommendation, where we aim to correctly estimate a ranking function for each user, predicting which talks would be of most interest to them. Then by decomposing user parameters into shared and individual dimensions, we induce a similarity metric between users based on the degree to which they share these dimensions. We show that the collaborative ranking predictions of future events are more effective than pure content-based recommendation. Finally, to further reduce the need for explicit user feedback, we suggest an active learning approach for eliciting feedback and a method for incorporating available implicit user cues.
From exploratory search to web search and back
Authors: Roberto Mirizzi, Tommaso Di Noia
Venue: CIKM
Year: 2010

The power of search is with no doubt one of the main aspects for the success of the Web. Currently available search engines on the Web allow to return results with a high precision. Nevertheless, if we limit our attention only to lookup search we are missing another important search task. In exploratory search, the user is willing not only to find documents relevant with respect to her query but she is also interested in learning, discovering and understanding novel knowledge on complex and sometimes unknown topics..
Semantic tags generation and retrieval for online advertising
Authors: Roberto Mirizzi, Azzurra Ragone, Tommaso Di Noia, Eugenio Di Sciascio
Venue: CIKM
Year: 2010

One of the main problems in online advertising is to display ads which are relevant and appropriate w.r.t. what the user is looking for. Often search engines fail to reach this goal as they do not consider semantics attached to keywords. In this paper we propose a system that tackles the problem by two different angles: help (i) advertisers to create more efficient ads campaigns and (ii) ads providers to properly match ads content to keywords in search engines. We exploit semantic relations stored in the DBpedia dataset and use an hybrid ranking system to rank keywords and to expand queries formulated by the user. Inputs of our ranking system are (i) the DBpedia dataset; (ii) external information sources such as classical search engine results and social tagging systems. We compare our approach with other RDF similarity measures, proving the validity of our algorithm with an extensive evaluation involving real users.
User behavior driven ranking without editorial judgments
Authors: Taesup Moon, Georges Dupret, Shihao Ji, Ciya Liao, Zhaohui Zheng
Venue: CIKM
Year: 2010

We explore the potential of using users click-through logs where no editorial judgment is available to improve the ranking function of a vertical search engine. We base our analysis on the Cumulate Relevance Model, a user behavior model recently proposed as a way to extract relevance signal from click-through logs. We propose a novel way of directly learning the ranking function, effectively by-passing the need to have explicit editorial relevance label for each query-document pair. This approach potentially adjusts more closely the ranking function to a variety of user behaviors both at the individual and at the aggregate levels. We investigate two ways of using behavioral model; First, we consider the parametric approach where we learn the estimates of document relevance and use them as targets for the machine learned ranking schemes. In the second, functional approach, we learn a function that maximizes the behavioral model likelihood, effectively by-passing the need to estimate a substitute for document labels. Experiments using user session data collected from a commercial vertical search engine demonstrate the potential of our approach. While in terms of DCG, the editorial model out-perform the behavioral one, online experiments show that the behavioral model is on par --if not superior-- to the editorial model. To our knowledge, this is the first report in the Literature of a competitive behavioral model in a commercial setting
Adaptive outlierness for subspace outlier ranking
Authors: Emmanuel Müller, Matthias Schiffer, Thomas Seidl
Venue: CIKM
Year: 2010

Outlier mining is an important data analysis task to distinguish exceptional outliers from regular objects. However, in recent applications traditional outlier mining approaches miss outliers as they are hidden in subspace projections.
Detecting periodic changes in search intentions in a search engine
Authors: Masaya Murata, Hiroyuki Toda, Yumiko Matsuura, Ryoji Kataoka, Takayoshi Mochizuki
Venue: CIKM
Year: 2010

Information needs expressed by using the same query for a search engine might be totally different, whether on week days or weekends, or during the day or at night. For queries having no temporal changes in search intentions, the same search results ranking may be returned regardless of the time, but for those with temporal changes the ranking must be suitably altered depending on the time of input. To achieve time-dependent search results rankings, we focus on the temporal changes in the search intentions. We present the results obtained by analyzing a commercial search engine log and propose a method of detecting queries showing periodic changes in the search intentions.
Towards a provenance framework for sub-image processing for astronomical data
Authors: Johnson Mwebaze, John McFarland, Danny Booxhorn, Edwin Valentijn
Venue: CIKM
Year: 2010

While there has been advances in observational equipment that generate huge high quality images, the processing of these images remains a major bottleneck. We show that provenance data collected during the processing of data can be reused to perform selective processing of data and support network collaboration without clogging distribution networks. We introduce the idea of sub-image processing (SIMP) in the context of processing a subset of pixels of an image and the use of provenance data to assemble pipelines and to select processing metadata for SIMP. We describe an implementation of SIMP in Astro-WISE
Massive structured data management solution
Authors: Ullas Nambiar, Rajeev Gupta, Himanshu Gupta, Mukesh K. Mohania
Venue: CIKM
Year: 2010

The need to analyze structured data for various business intelligence applications such as customer churn analysis, social network analysis, etc. is well known. However, the potential size to which such data will scale in future will make solutions that revolve around data warehouses hard to scale. We begin by presenting a business case that prompted us to look at building a distributed analytics platform that is leveraging the MapReduce framework pioneered by Google. We present the results of the study and highlight issues with the current structured data access techniques for MapReduce platforms. Finally, we present a distributed and scalable data platform that leverages Apache Hadoop to enable business analysts to seamlessly query archived data along with data stored in the warehouse.
A multi-functional architecture addressing workflow and service challenges using provenance data
Authors: Mahsa Naseri, Simone A. Ludwig
Venue: CIKM
Year: 2010

In service-oriented environments, keeping track of the composition process along with the data transformations and services provides a rich amount of information for later reasoning. Current exploitation and application of this information, which is referred to as provenance data, is very limited as provenance systems started being developed for specific applications. Therefore, there is a need for a multi-functional architecture, which would be application-independent and could be deployed in any area. In this paper, we present an architecture, which exploits provenance information to target the current challenges of workflows. These challenges include workflow composition, abstract workflow selection, refinement, evaluation, and graph model extraction.