OpenRaiser/Intern-Atlas huggingface dataset:
Annotating knowledge work lifelog: term extraction from sensor and operation history
Authors: Masayuki Okamoto, Nayuko Watanabe, Shinichi Nagano, Kenta Cho
Venue: CIKM
Year: 2011

We present a system that supports review of a knowledge work lifelog as an activity history. Since knowledge workers often review their own activity histories, gathering each user's activities on his/her terminal as a lifelog is a promising approach. However, readability of the stored lifelog is a large problem of lifelog-based application. We propose a term extraction method to add annotation labels to the stored lifelog for supporting knowledge workers, exploiting text data acquired from desktop activities. Our prototype system monitors a user's desktop activities after combining raw events, and then extracts possible annotation labels with LDA and C-value techniques from documents and text data in sensor events. In this paper, we introduce a lifelogging module and a lifelog annotation method based on term extraction techniques. According to an empirical evaluation for three weeks, we found that the current method is useful for one-week review.
A data mining system based on SQL queries and UDFs for relational databases
Authors: Carlos Ordonez, Carlos Garcia-Alvarado
Venue: CIKM
Year: 2011

Most research on data mining has proposed algorithms and optimizations that work on flat files, outside a DBMS, mainly due to the following reasons. It is easier to develop efficient algorithms in a traditional programming language. The integration of data mining algorithms into a DBMS is difficult given its relational model foundation and system architecture. Moreover, SQL may be slow and cumbersome for numerical analysis computations. Therefore, data mining users commonly export data sets outside the DBMS for data mining processing, which creates a performance bottleneck and eliminates important data management capabilities such as query processing and security, among others (e.g. concurrency control and fault tolerance). With that motivation in mind, we developed a novel system based on SQL queries and User-Defined Functions (UDFs) that can directly analyze relational tables to compute statistical models, storing such models as relational tables as well. Most algorithms have been optimized to reduce the number of passes on the data set. Our system can analyze large and high dimensional data sets faster than external data mining tools.
SILA: a spatial instance learning approach for deep webpages
Authors: Ermelinda Oro, Massimo Ruffolo
Venue: CIKM
Year: 2011

Deep Web pages convey very relevant information for different application domains like e-government, e-commerce, social networking. For this reason there is a constant high interest in efficiently, effectively and automatically extracting data from Deep Web data sources. In this paper we present SILA, a novel Spatial Instance Learning Approach, that allows for extracting data records from Deep Web pages by exploiting both the spatial arrangement and the presentation features of data items/fields produced by layout engines of Web browsers in visualizing Deep Web pages on the screen. SILA is independent from the internal HTML encodings of Web pages, and allows for recognizing data records in pages having multiple data regions in which data items are arranged by many different presentation layouts. Experimental results show that SILA has very high precision and recall and that it works much better than MDR and ViNTs approaches.
Semi-indexing semi-structured data in tiny space
Authors: Giuseppe Ottaviano, Roberto Grossi
Venue: CIKM
Year: 2011

Semi-structured textual formats are gaining increasing popularity for the storage of document collections and rich logs. Their flexibility comes at the cost of having to load and parse a document entirely even if just a small part of it needs to be accessed. For instance, in data analytics massive collections are usually scanned sequentially, selecting a small number of attributes from each document. We propose a technique to attach to a raw, unparsed document (even in compressed form) a "semi-index": a succinct data structure that supports operations on the document tree at speed comparable with an in-memory deserialized object, thus bridging textual formats with binary formats. After describing the general technique, we focus on the JSON format: our experiments show that avoiding the full loading and parsing step can give speedups of up to 12 times for on-disk documents using a small space overhead.
Suggestion set utility maximization using session logs
Authors: Umut Ozertem, Emre Velipasaoglu, Larry Lai
Venue: CIKM
Year: 2011

Assistance technology is undoubtedly one of the important elements in the commercial search engines, and routing the user towards the right direction throughout the search sessions is of great importance for providing a good search experience. Most search assistance methods in the literature that involve query generation, query expansion and other techniques consider each suggestion candidate individually, which implies an independence assumption. We challenge this independence assumption and give a method to maximize the utility of a given set of suggestions. For this, we will define a measure of conditional utility for query pairs using query-URL bipartite graphs based on the session logs (clicked and viewed URLs). Afterwards, we remove the redundant queries from the suggestion set using a greedy algorithm to be able to replace them with more useful ones. Both offline (based on user studies and session log analysis) and online (based on millions of user interactions) evaluations show that modeling the conditional utility and maximizing the utility of the set of queries (by eliminating redundant ones) significantly increases the effectiveness of the search assistance both for the presubmit and postsubmit modes.
More or better: on trade-offs in compacting textual problem solution repositories
Authors: Deepak P, Sutanu Chakraborti, Deepak Khemani
Venue: CIKM
Year: 2011

In this paper, we look into the problem of filtering problem solution repositories (from sources such as community-driven question answering systems) to render them more suitable for usage in knowledge reuse systems. We explore harnessing the fuzzy nature of usability of a solution to a problem, for such compaction. Fuzzy usabilities lead to several challenges; notably, the trade-off between choosing generic or better solutions. We develop an approach that can heed to a user specification of the trade-off between these criteria and introduce several quality measures based on fuzzy usability estimates to ascertain the quality of a problem-solution repository for usage in a Case Based Reasoning system. We establish, through a detailed empirical analysis, that our approach outperforms state-of-the-art approaches on virtually all quality measures.
Annotating attribution relations and their features
Authors: Silvia Pareti
Venue: CIKM
Year: 2011

Attribution has attracted increasing attention in recent years following the development of Opinion Analysis and Information Extraction applications. Given a piece of information or an opinion, being able to correctly identify its source (either a specific entity e.g. President Obama or a class thereof e.g. experts, official sources, rumours) and determine how different sources and features of attribution affect the way information is perceived would be extremely beneficial. It would in fact enhance opinion-oriented applications of Language Technology and revolutionise the way we can select information, e.g. on the basis of source expertise and reliability. However, approaches to the automatic extraction of attribution relations have achieved only partial results to date and are therefore not sufficient to enable the development of reliable applications. In addition, there has been little or no attempt to discern the impact of different sources and attribution strategies on the perception and interpretation of the attributed material. Grounded on previous analysis of this relation in Italian [2] and English [4] news corpora, this study addresses several of the attribution strategies identified to develop a more comprehensive system for the extraction of attributions and their relevant features from news texts.
Marco Polo: a system for brand-based shopping and exploration
Authors: Nish Parikh, Neel Sundaresan
Venue: CIKM
Year: 2011

In today's world, brand based shopping is popular especially in product lines like clothing and shoes, appliances, and electronics. Because of the importance of brands while shopping, it has become important for online shopping portals to consider brand loyalty and brand preferences of users. In this paper, we describe a system designed for brand-based shopping and exploration. The system is built by analyzing a large query set consisting of 115M queries from eBay.com -- a vibrant marketplace with more than 95M active users. The system allows brand-pivoted exploration of inventory. It allows exploration and purchase of substitute branded goods (e.g. Sony camcorder for Canon camcorder) and complementary branded merchandise (e.g. Lego castle set for Lego train station set).
A continuous query evaluation scheme for a detection-only query over data streams
Authors: Hong Kyu Park, Won Suk Lee
Venue: CIKM
Year: 2011

In a data stream environment, a multi-way join continuous query is employed to monitor a considerable number of source data streams from various remote sites in real-time. One key role of a continuous query is detecting only the invocation of a particular event corresponding to the specifications of the query. The evaluation of such a detection-only query does not require to produce either an intermediate tuple or a final result tuple, which not only shortens the processing time of a query but also reduces the usage of memory space. However, there has been no special effort to deal with a query of this type. This paper proposes a new evaluation framework which efficiently processes a multi-way detection-only query without generating any intermediate result tuple explicitly.
k-Nearest neighbor query processing method based on distance relation pattern
Authors: Yonghun Park, Dongmin Seo, Kyoungsoo Bok, Jaesoo Yoo
Venue: CIKM
Year: 2011

The k-nearest neighbor (k-NN) query is one of the most important query types for location based services (LBS). Various methods have been proposed to efficiently process the k-NN query. However, most of the existing methods suffer from high computation time and larger memory requirement because they unnecessarily access cells to find the nearest cells on a grid index. In this paper, we propose a new efficient method, called Pattern Based k-NN (PB-kNN) to process the k-NN query. The proposed method uses the patterns of the distance relationships among the cells in a grid index. The basic idea is to normalize the distance relationships as certain patterns. Using this approach, PB-kNN significantly improves the overall performance of the query processing. It is shown through various experiments that our proposed method outperforms the existing methods in terms of query processing time and storage overhead.
Asking what no one has asked before: using phrase similarities to generate synthetic web search queries
Authors: Marius Pasca
Venue: CIKM
Year: 2011

This paper introduces a method for automatically inferring meaningful, not-yet-submitted queries. The inferred queries fill some of the knowledge gaps between documents, on one hand, and known (i.e., already-submitted) queries, on the other hand. Thus, the inferred queries expand query logs and increase their coverage. New candidate queries are over-generated from known queries via phrase similarity data, then filtered against the set of known queries. The accuracy of the generated queries is computed using open-domain questions from standard question answering evaluation sets. Over the ranked lists of questions inferred for each of the evaluation questions, the precision reaches 0.9 at rank 50. The set of inferred queries is more than twice as large as the set of input queries.
Web-based open-domain information extraction
Authors: Marius Pasca
Venue: CIKM
Year: 2011

This tutorial provides an overview of extraction methods developed in the area of Web-based open-domain information extraction, whose purpose is the acquisition of open-domain classes, instances and relations from Web text. The extraction methods operate over unstructured or semi-structured text. They take advantage of weak supervision provided in the form of seed examples or small amounts of annotated data, or draw upon knowledge already encoded within resources created strictly by experts or collaboratively by users. The tutorial teaches the audience about existing resources that include instances and relations; details of methods for extracting such data from structured and semi-structured text available on the Web; and strengths and limitations of resources extracted from text as part of recent literature, with applications in knowledge discovery and information retrieval.
Collection-based compression using discovered long matching strings
Authors: Andrew Peel, Anthony Wirth, Justin Zobel
Venue: CIKM
Year: 2011

Many collections of data contain items that are inherently similar. For example, archives contain files with incremental changes between releases. Long-range inter-file similarities are not exploited by standard approaches to compression. We investigate compression using similarity from all parts of a collection, collection-based compression (CBC). Input files are delta-encoded by reference to long string matches in a source collection. The expected space requirement of our encoding algorithm is sublinear with the collection size, and the compression time complexity is linear with the input file size. We show that our scheme achieves better compression for large input files than existing differential compression systems, and scales better. Also, we achieve significant compression improvement compared to compressing each file individually using standard utilities: our scheme achieves several times the compression of gzip or 7-zip. The overall result is a dramatic improvement on compression available with existing approaches.
Privacy-aware querying over sensitive trajectory data
Authors: Nikos Pelekis, Aris Gkoulalas-Divanis, Marios Vodas, Despina Kopanaki, Yannis Theodoridis
Venue: CIKM
Year: 2011

Existing approaches for privacy-aware mobility data sharing aim at publishing an anonymized version of the mobility dataset, operating under the assumption that most of the information in the original dataset can be disclosed without causing any privacy violations. In this paper, we assume that the majority of the information that exists in the mobility dataset must remain private and the data has to stay in-house to the hosting organization. To facilitate privacy-aware sharing of the mobility data we develop a trajectory query engine that allows subscribed users to gain restricted access to the database to accomplish various analysis tasks. The proposed engine (i) audits queries for trajectory data to block potential attacks to user privacy, (ii) supports range, distance, and k-nearest neighbors spatial and spatiotemporal queries, and (iii) preserves user anonymity in answers to queries by (a) augmenting the real trajectories with a set of carefully crafted, realistic fake trajectories, and (b) ensuring that no user-specific sensitive locations are reported as part of the returned trajectories.
A personalized recommendation system on scholarly publications
Authors: Maria Soledad Pera, Yiu-Kai Ng
Venue: CIKM
Year: 2011

Researchers, as well as ordinary users who seek information in diverse academic fields, turn to the web to search for publications of interest. Even though scholarly publication recommenders have been developed to facilitate the task of discovering literature pertinent to their users, they (i) are not personalized enough to meet users' expectations, since they provide the same suggestions to users sharing similar profiles/preferences, (ii) generate recommendations pertaining to each user's general interests as opposed to the specific need of the user, and (iii) fail to take full advantages of valuable user-generated data at social websites that can enhance their performance. To address these problems, we propose PubRec, a recommender that suggests closely-related references to a particular publication P tailored to a specific user U, which minimizes the time and efforts imposed on U in browsing through general recommended publications. Empirical studies conducted using data extracted from CiteULike (i) verify the efficiency of the recommendation and ranking strategies adopted by PubRec and (ii) show that PubRec significantly outperforms other baseline recommenders.
A query-based multi-document sentiment summarizer
Authors: Maria Soledad Pera, Rani Qumsiyeh, Yiu-Kai Ng
Venue: CIKM
Year: 2011

Review websites, such as Epinions.com, which offer users a platform to share their opinions on diverse products and services, provide a valuable source of opinion-rich information. Browsing through archived reviews to locate different opinions on a product or service, however, is a time-consuming and tedious task, and in most cases, the large amount of available information is difficult for users to absorb. To facilitate the process of synthesizing opinions expressed in reviews on a product or service P specified in a user query/question Q, we introduce QMSS, a query-based multi-document sentiment summarizer. QMSS creates a summary for Q, which either reflects the general opinions on P or is tailored to specific facets (i.e., features) and/or sentiment of P as specified in Q. QMSS (i) identifies the facets addressed in reviews retrieved for Q, (ii) employs a sentence-based, sentiment classifier to determine the polarity of each sentence in each review, and (iii) clusters sentences in reviews according to the facets captured in the sentences, which are identified using a keyword-label extraction algorithm. This process dictates which sentences in the reviews should be included in the summary for Q. Empirical studies have verified that QMSS is highly effective in generating summaries that satisfy users' information needs and ranks on top among the state-of-the-art query-based multi-document sentiment summarizers
Social and collaborative information seeking: panel
Authors: Jeremy Pickens
Venue: CIKM
Year: 2011

In recent years, information retrieval and information seeking have moved beyond their single-user roots and are becoming multi-user endeavors. However, there are multiple visions for how best to design multi-user interactions: social search versus collaborative search. The terms "social" and "collaborative" are overloaded with meaning, having been used to describe a wide variety of systems, user needs and goals, interaction styles, and algorithms. In this panel we adopt the following primary definitions: Information seeking tasks in which there are two or more people who lack the same information (share the same information need) and explicitly set out together to satisfy that need are known as collaborative. A collaborative information retrieval system provides mechanisms -- interfaces and mediation algorithms -- that allow the team to work together to find information that neither individual would have found when working alone. There is an inherent division of labor in collaborative work.
From names to entities using thematic context distance
Authors: Anja Pilz, Gerhard Paaß
Venue: CIKM
Year: 2011

Name ambiguity arises from the polysemy of names and causes uncertainty about the true identity of entities referenced in unstructured text. This is a major problem in areas like information retrieval or knowledge management, for example when searching for a specific entity or updating an existing knowledge base.
A partitioning method for symbolic interval data based on kernelized metric
Authors: Bruno A. Pimentel, Anderson F. B. F. da Costa, Renata M. C. R. de Souza
Venue: CIKM
Year: 2011

To solve the problem of situations with nonlinearly separable clusters, kernel clustering methods have been proposed. Symbolic Data Analysis (SDA) has emerged to deal with variables that can have intervals, histograms, and even functions as values, in order to consider the variability and/or uncertainty innate to the data. In this paper, we present a K-means clustering method based in kernelized squared L2 distance for symbolic interval-type data. Experiments with real and syntectic symbolic interval-type data sets are considered.
AWETO: efficient incremental update and querying in rdf storage system
Authors: Xu Pu, Jianyong Wang, Ping Luo, Min Wang
Venue: CIKM
Year: 2011

With the fast growth of the knowledge bases built over the Internet, storing and querying millions or billions of RDF triples in a knowledge base have attracted increasing research interests. Although the latest RDF storage systems achieve good querying performance, few of them pay much attention to the characteristic of dynamic growth of the knowledge base. In this paper, to consider the efficiency of both querying and incremental update in RDF data, we propose a hAsh-based tWo-tiEr rdf sTOrage system (abbr. to AWETO) with new index architecture and query execution engine. The performance of our system is systematically measured over two large-scale datesets. Compared with the other three state-of-the-art RDF storage systems, our system achieves the best incremental update efficiency, meanwhile, the query efficiency is competitive.
Hierarchy evolution for improved classification
Authors: Xiaoguang Qi, Brian D. Davison
Venue: CIKM
Year: 2011

Hierarchical classification has been shown to have superior performance than flat classification. It is typically performed on hierarchies created by and for humans rather than for classification performance. As a result, classification based on such hierarchies often yields suboptimal results. In this paper, we propose a novel genetic algorithm-based method on hierarchy adaptation for improved classification. Our approach customizes the typical GA to optimize classification hierarchies. In several text classification tasks, our approach produced hierarchies that significantly improved upon the accuracy of the original hierarchy as well as hierarchies generated by state-of-the-art methods.
Combining machine learning and human judgment in author disambiguation
Authors: Ya-nan Qian, Yunhua Hu, Jianling Cui, Qinghua Zheng, Zaiqing Nie
Venue: CIKM
Year: 2011

Author disambiguation in digital libraries becomes increasingly difficult as the number of publications and consequently the number of ambiguous author names keep growing. The fully automatic author disambiguation approach could not give satisfactory results due to the lack of signals in many cases. Furthermore, human judgment on the basis of automatic algorithms is also not suitable because the automatically disambiguated results are often mixed and not understandable for humans. In this paper, we propose a Labeling Oriented Author Disambiguation approach, called LOAD, to combine machine learning and human judgment together in author disambiguation. LOAD exploits a framework which consists of high precision clustering, high recall clustering, and top dissimilar clusters selection and ranking. In the framework, supervised learning algorithms are used to train the similarity functions between publications and a clustering algorithm is further applied to generate clusters. To validate the effectiveness and efficiency of the proposed LOAD approach, comprehensive experiments are conducted. Comparing to conventional author disambiguation algorithms, the LOAD yields much more accurate results to assist human labeling. Further experiments show that the LOAD approach can save labeling time dramatically.