OpenRaiser/Intern-Atlas huggingface dataset:
Efficient set-correlation operator inside databases
Authors: Shaoxu Song, Lei Chen
Venue: CIKM
Year: 2010

Large scale of short text records are now prevalent, such as news highlights, scientific paper citations, and posted messages in a discussion forum, which are often stored as set records in (hidden) databases. Many interesting information retrieval tasks are correspondingly raised on the correlation query over these short text records, such as finding hot topics over news highlights and searching related scientific papers on a certain topic. However, current relational database management systems (RDBMS) do not directly provide support on set correlation query. Thus, in this paper, we address both the effectiveness and efficiency issues of set correlation query over set records in databases. First, we present a framework of set correlation query inside databases. To our best knowledge, only the Pearson's correlation can be implemented to construct token correlations by using RDBMS facilities. Thereby, we propose a novel correlation coefficient to extend Pearson's correlation, and provide a pure-SQL implementation inside databases. We further propose optimal strategies to set up correlation filtering threshold, which can greatly reduce the query time. Our theoretical analysis proves that, with a proper setting of filtering threshold, we can improve the query efficiency with a little effectiveness loss. Finally, we conduct extensive experiments to show the effectiveness and efficiency of proposed correlation query and optimization strategies.
Domain-independent entity coreference in RDF graphs
Authors: Dezhao Song, Jeff Heflin
Venue: CIKM
Year: 2010

In this paper, we present a novel entity coreference algorithm for Semantic Web instances. The key issues include how to locate context information and how to utilize the context appropriately. To collect context information, we select a neighborhood (consisting of triples) of each instance from the RDF graph. To determine the similarity between two instances, our algorithm computes the similarity between comparable property values in the neighborhood graphs. The similarity of distinct URIs and blank nodes is computed by comparing their outgoing links. To provide the best possible domain-independent matches, we examine an appropriate way to compute the discriminability of triples. To reduce the impact of distant nodes, we explore a distance-based discounting approach. We evaluated our algorithm using different instance categories in two datasets. Our experiments show that the best results are achieved by including both our triple discrimination and discounting approaches.
Automatic extraction of web data records containing user-generated content
Authors: Xinying Song, Jing Liu, Yunbo Cao, Chin-Yew Lin, Hsiao-Wuen Hon
Venue: CIKM
Year: 2010

In this paper, we are concerned with the problem of automatically extracting web data records that contain user-generated content (UGC). In previous work, web data records are usually assumed to be well-formed with a limited amount of UGC, and thus can be extracted by testing repetitive structure similarity. However, when a web data record includes a large portion of free-format UGC, the similarity test between records may fail, which in turn results in lower performance. In our work, we find that certain domain constraints (e.g., post-date) can be used to design better similarity measures capable of circumventing the influence of UGC. In addition, we also use anchor points provided by the domain constraints to improve the extraction process, which ends in an algorithm called MiBAT (Mining data records Based on Anchor Trees). We conduct extensive experiments on a dataset consisting of forum thread pages which are collected from 307 sites that cover 219 different forum software packages. Our approach achieves a precision of 98.9% and a recall of 97.3% with respect to post record extraction. On page level, it perfectly handles 91.7% of pages without extracting any wrong posts or missing any golden posts. We also apply our approach to comment extraction and achieve good results as well.
Partial drift detection using a rule induction framework
Authors: Damon Sotoudeh, Aijun An
Venue: CIKM
Year: 2010

The major challenge in mining data streams is the issue of concept drift, the tendency of the underlying data generation process to change over time. In this paper, we propose a general rule learning framework that can efficiently handle concept-drifting data streams and maintain a highly accurate classification model. The main idea is to focus on partial drifts by allowing individual rules to monitor the stream and detect if there is a drift in the regions they cover. A rule quality measure then decides whether the affected rules are inconsistent with the concept drift. The model is accordingly updated to only include rules that are consistent with the newly arrived concept. A dynamically maintained set of instances deemed relevant to the most recent concept is also kept at memory. Learning a new concept from a larger set of instances reduces the variance of data distribution and allows for a more accurate, stable classification model. Our experiments show that this approach not only handles the drift efficiently, but it also can provide higher classification accuracy compared to other competitive approaches on a variety of real and synthetic data sets.
Image retrieval at memory's edge: known image search based on user-drawn sketches
Authors: Michael Springmann, Ihab Al Kabary, Heiko Schuldt
Venue: CIKM
Year: 2010

With the increasingly growing size of digital image collections, known image search is gaining more and more importance. Especially in collections where individual objects are not tagged with metadata describing their content, content-based image retrieval (CBIR) is a promising approach, but usually suffers from the unavailability of query images that are good enough to express the user's information need. In this paper, we present the QbS system that provides CBIR based on user-drawn sketches. The QbS system combines angular radial partitioning for the extraction of features in the user-provided sketch, taking into account the spatial distribution of edges, and the image distortion model. This combination offers several highly relevant invariances that allow the query sketch to slightly deviate from the searched image in terms of rotation, translation, relative size, and/or unknown objects in the background. To illustrate the benefits of the approach, we present search results from the evaluation of the QbS system on the basis of the MIRFLICKR collection with 25,000 objects and compare the retrieval results of pure metadata-driven approaches, pure content-based retrieval using different sketches, and combinations thereof.
Schema extraction
Authors: Divesh Srivastava
Venue: CIKM
Year: 2010

Understanding the schema of a complex database is a crucial step in exploratory data analysis. However, gaining such an understanding is challenging for new users for many reasons. First, complex databases often have thousands of inter-linked tables, with little indication of the important tables or the main concepts in the database schema. Second, schemas can be inaccurate, e.g., some foreign/primary key relationships are not known to designers but are inherent in the data, while others become invalid due to data inconsistencies. In this talk, we present an approach to effectively address these challenges and automatically extract an understandable schema from a complex database.
Support elements in graph structured schema reintegration
Authors: Xun Sun, Rachel Pottinger, Michael K. Lawrence
Venue: CIKM
Year: 2010

Manipulating graph-structured schemas (ontologies, models, etc.) requires the result to remain fully connected. In certain cases, e.g., calculating the difference of two schemas, support structures may be needed in the result. We describe our engine to process support structures in the context of a schema management system and describe schema reintegration experiments which validate the performance and correctness of our system
Factors affecting click-through behavior in aggregated search interfaces
Authors: Shanu Sushmita, Hideo Joho, Mounia Lalmas, Robert Villa
Venue: CIKM
Year: 2010

An aggregated search interface is designed to integrate search results from different sources (web, image, video, blog, etc) into a single result page. This paper presents two user studies investigating factors affecting users click-through behavior on aggregated search interfaces. We tested two aggregated search interfaces: one where results from the different sources are blended into a single list (called blended), and another, where results from each source are presented in a separate panel (called non-blended). A total of 1,296 search sessions performed by 48 participants were analysed in our study. Our results suggest that 1) the position of search results is significant only in the blended and not in the non-blended design; 2) participants' click-through behavior on videos is different from other sources; and finally 3) capturing a task's orientation towards particular sources is an important factor for further investigation and research.
Automatically acquiring a semantic network of related concepts
Authors: Sean R. Szumlanski, Fernando Gomez
Venue: CIKM
Year: 2010

We describe the automatic construction of a semantic network1, in which over 3000 of the most frequently occurring monosemous nouns2 in Wikipedia (each appearing between 1,500 and 100,000 times) are linked to their semantically related concepts in the WordNet noun ontology. Relatedness between nouns is discovered automatically from co-occurrence in Wikipedia texts using an information theoretic inspired measure. Our algorithm then capitalizes on salient sense clustering among related nouns to automatically disambiguate them to their appropriate senses (i.e., concepts). Through the act of disambiguation, we begin to accumulate relatedness data for concepts denoted by polysemous nouns, as well. The resultant concept-to-concept associations, covering 17,543 nouns, and 27,312 distinct senses among them, constitute a large-scale semantic network of related concepts that can be conceived of as augmenting the WordNet noun ontology with related-to links.
Modeling a trust cloud context
Authors: Sean S. E. Thorpe
Venue: CIKM
Year: 2010

As data services become increasingly pervasive, the need to provide users with trusted computing resources at optimal efficiencies of scale is critical. Cloud computing arguably provides this scalable architecture. However, the issue of establishing a reliable trust context for data within the cloud is not well defined and presents a challenging and relevant problem. The research aims to represent the set of parameters that defines the trust enabled context for managing such cloud data elements and their attributes. The motivation for this work is based on the author's earlier work on developing formal models for Cloud computing
Boosting social network connectivity with link revival
Authors: Yuan Tian, Qi He, Qiankun Zhao, Xingjie Liu, Wang-Chien Lee
Venue: CIKM
Year: 2010

Online social networking platforms have become a popular channel of communications among people. However, most people can only keep in touch with a limited number of friends. This phenomenon results in a low-connectivity social network in terms of communications, which is inefficient for information propagation and social engagement. In this paper, we introduce a new recommendation service, called link revival, that suggests users to re-connect with their old friends, such that the resulted connection will improve the social network connectivity. To achieve high connectivity improvement under the dynamic social network evolvement, we propose a graph prediction-based recommendation strategy, which selects proper candidates based on the prediction of their future behaviors. We then develop an effective model that exploits non-homogeneous Poisson process and second-order self-similarity in prediction. Through comprehensive experimental studies on two real datasets (Phone Call Network and Facebook Wall-posts), we demonstrate that our proposed approach can significantly increase the social network connectivity, and that the approach outperforms other baseline solutions. The results also show that our solution is more suitable for online social networks like Facebook, partially due to the stronger long range dependency and lower communication costs in the interactions.
Topic detection and organization of mobile text messages
Authors: Ye Tian, Wendong Wang, Xueli Wang, Jinghai Rao, Canfeng Chen, Jian Ma
Venue: CIKM
Year: 2010

How to organize and visualize big amount of text messages stored on one's mobile phone is a challenging problem, since they can hardly be organized by threads as we do for emails due to lack of necessary metadata such as "subject" and "reply-to". In this paper, we propose an innovative approach based on clustering algorithms and natural language processing methods. We first cluster the text messages into candidate conversations based on their temporal attributes, and then do further analysis using a semantic model based on Latent Dirichlet Allocation (LDA). Considering that the text messages are usually short and sparse, we trained the model using a large scale external data collected from twitter-like web sites, and applied the model to text messages. In the end, the text messages are organized as conversations based on their topics. We evaluated our approach based on 122,359 text messages collected from 50 university students during 6 months.
Creating software models with semantic annotation
Authors: Walter F. Tichy, Sven J. Körner, Mathias Landhäußer
Venue: CIKM
Year: 2010

Requirements engineering is a big part of software engineering and consumes a lot of time. We propose a novel approach of automatically creating software domain models from textual requirements specifications using semantic annotation. Natural language processing (NLP) has progressed much in the last years and the usage of NLP tools for automatic annotation shows promising results. We use Fillmore's thematic roles to explicitly denote the semantic relations in a sentence.
Research proposal for distributed deep web search
Authors: Kien-Tsoi T. E. Tjin-Kam-Jet
Venue: CIKM
Year: 2010

This proposal identifies two main problems related to deep web search, and proposes a step by step solution for each of them. The first problem is about searching deep web content by means of a simple free-text interface (with just one input field, instead of a complex interface with many input fields). To this end, we propose a real-time query conversion layer to translate a free-text query into a structured query. The second problem concerns the scalability of the system, and we propose a distributed approach.
Print: a provenance model to support integration processes
Authors: Bruno Tomazela, Carmem S. Hara, Ricardo Rodrigues Ciferri, Cristina Dutra de Aguiar Ciferri
Venue: CIKM
Year: 2010

In some integration applications, users are allowed to import data from heterogeneous sources, but are not allowed to update source data directly. Imported data may be inconsistent, and even when inconsistencies are detected and solved, these changes may not be propagated to the sources due to their update policies. Therefore, they continue to provide the same inconsistent data in the future until the proper authority updates them. In this paper, we propose PrInt, a model that supports user's decisions on cleaning data to be automatically reapplied in subsequent integration processes. By reproducing previous decisions, the user may focus only on new inconsistencies originated from source modified data. The reproducibility provided by PrInt is based on logging, and by incorporating data provenance in the integration process.
A cross-lingual framework for monolingual biomedical information retrieval
Authors: Dolf Trieschnigg, Djoerd Hiemstra, Franciska de Jong, Wessel Kraaij
Venue: CIKM
Year: 2010

An important challenge for biomedical information retrieval (IR) is dealing with the complex, inconsistent and ambiguous biomedical terminology. Frequently, a concept-based representation defined in terms of a domain-specific terminological resource is employed to deal with this challenge. In this paper, we approach the incorporation of a concept-based representation in monolingual biomedical IR from a cross-lingual perspective. In the proposed framework, this is realized by translating and matching between text and concept-based representations. The approach allows for deployment of a rich set of techniques proposed and evaluated in traditional cross-lingual IR. We compare six translation models and measure their effectiveness in the biomedical domain. We demonstrate that the approach can result in significant improvements in retrieval effectiveness over word-based retrieval. Moreover, we demonstrate increased effectiveness of a CLIR framework for monolingual biomedical IR if basic translations models are combined. Â© 2010 ACM.
Utilizing re-finding for personalized information retrieval
Authors: Sarah K. Tyler, Jian Wang, Yi Zhang
Venue: CIKM
Year: 2010

Individuals often use search engines to return to web pages they have previously visited. This behaviour, called re-finding, accounts for about 38% of all queries. While researchers have shown how re-finding differs from traditionally studied new-findings, research on how to predict and utilize re-finding is limited. In this paper we explore re-finding for personalized search. We compared three machine learning algorithms (decision trees, Bayesian multinomial regression and support vector machines) to identify re-findings. We then propose several re-ranking methods to utilize the prediction, including promoting predicted re-finding URLs and combining re-finding prediction with relevance estimation. The experimental results demonstrate that using re-finding predictions can improve retrieval performance for personalized search.
Crawling the web for structured documents
Authors: Julián Urbano, Juan Lloréns, Yorgos Andreadakis, Mónica Marrero
Venue: CIKM
Year: 2010

Structured Information Retrieval is gaining a lot of interest in recent years, as this kind of information is becoming an invaluable asset for professional communities such as Software Engineering. Most of the research has focused on XML documents, with initiatives like INEX to bring together and evaluate new techniques focused on structured information. Despite the use of XML documents is the immediate choice, the Web is filled with several other types of structured information, which account for millions of other documents. These documents may be collected directly using standard Web search engines like Google and Yahoo, or following specific search patterns in online repositories like SourceForge. This demo describes a distributed and focused web crawler for any kind of structured documents, and we show with it how to exploit general-purpose resources to gather large amounts of real-world structured documents off the Web. This kind of tool could help building large test collections of other types of documents, such as Java source code for software-oriented search engines or RDF for semantic searching.
Semantic annotations in clinical documentation: exploring potentials for future information retrieval
Authors: Sumithra Velupillai
Venue: CIKM
Year: 2010

Clinical documentation pose an interesting challenge to the Information Retrieval community, as it contains a large amount of knowledge and experience documented in both free text and structured information. In this position paper, we discuss the use of semantic annotations for this type of data, such as certainty and pain levels, and the potentials for exploiting these in information access settings such as for hypothesis generation and adverse event identification.
Intelligent sales forecasting engine using genetic algorithms
Authors: M. Vijayalakshmi, Bernard Menezes, Rohit Menon, Aniket Divecha, Rajesh Ravindran, Kamal Mehta
Venue: CIKM
Year: 2010

Times series techniques have been extensively used for Sales forecasting. Research has established that a combination forecast works better than a single forecast. Our research attempts to design an Intelligent Forecasting Engine which will use a combination forecasting technique. This design is based on use of Genetic Algorithms, for selecting the best methods to combine for forecasting. Early results demonstrate that Genetic Algorithms have the potential to become a powerful tool for time series sales forecasting.
A metamodel approach to flexible semantic web service discovery
Authors: Roberto De Virgilio, Devis Bianchini
Venue: CIKM
Year: 2010

In this paper we describe an approach for service discovery supported by semantic annotations. We propose a metamodel representation of both the WSDL documents and the associated semantic annotations. Based on this metamodel, effective service discovery is achieved by a Datalog engine implementing flexible matchmaking techniques that allow both exact and partial matches among search results. The metamodel is supported by a storage system that ensures scalability of the entire process. Finally we illustrate experiments on a public dataset of semantic service descriptions.
Search by strategy
Authors: Arjen P. de Vries, Wouter Alink, Roberto Cornacchia
Venue: CIKM
Year: 2010

This position statement advocates that the integration of information retrieval and databases, a topic that has been studied for many years (see e.g. [3]), is now in a state where the technology is ready to be brought out of the laboratory, and that this technology is especially a good match for the meaningful, semantic annotations that are the topic of this workshop.
Hierarchical service analytics for improving productivity in an enterprise service center
Authors: Chunye Wang, Ram Akella, Srikant Ramachandran
Venue: CIKM
Year: 2010

Modern day service centers are the building blocks for highly efficient and productive business systems in a knowledge economy. In these service systems, accurate and timely delivery of pertinent information to service representatives becomes the cornerstone for delivering efficient customer service. There are two main steps in achieving this objective. The first step concerns efficient text mining to extract critical and pertinent information from the very long service request (SR) documents in the historical database. The second step concerns matching new service requests with previously stored service requests. Both lead to efficiencies by minimizing time spent by service personnel in extracting Intellectual Capital (IC). In this paper we present our text analytics system, the Service Request Analyzer and Recommender (SRAR), which is designed to improve the productivity in an enterprise service center for computer network diagnostics and support. SRAR unifies a text preprocessor, a hierarchical classifier, and a service request recommender, to deliver critical, pertinent, and categorized knowledge for improved service efficiency. The novel feature we report here is identifying the components of the diagnostic process underlying the creation of the original text documents. This identification is crucial in the successful design and prototyping of SRAR and its hierarchical classifier element. Equally, the use of domain knowledge and human expertise to generate features are indispensable synergistic elements in improving the accuracy of the text analysis toward identifying the components of the diagnostic process. The evaluation and comparison of SRAR with other benchmark approaches in the literature demonstrate the effectiveness of our framework and algorithms. This framework can be generalized to be applicable in many service industries and business functions that mine textual data to achieve increased efficiency in their service delivery. We observe significant service time responsiveness improvements during the first step of IC extraction in network service center context at Cisco.