OpenRaiser/Intern-Atlas huggingface dataset:
FusionDB: conflict management system for small-science databases
Authors: Karim Ibrahim, Nathaniel Selvo, Mohamad El-Rifai, Mohamed Y. Eltabakh
Venue: CIKM
Year: 2013

In this paper, we demonstrate the FusionDB system; an extended relational database engine for managing conflicts in small-science databases. In small sciences, groups---each consists of few scientists---may share and exchange parts of their own databases among each other to foster collaboration. The goal of such sharing, especially when done at early stages of the discovery process, is not to build a warehouse or a unified schema, instead the goal is to compare and verify results, detect and assess conflicts, and possibly modify or re-design the discovery process. FusionDB is designed to meet the requirements and address the challenges of such sharing model. We will demonstrate the key functionalities of FusionDB including: (1) Detecting conflicts using a rule-based model over heterogeneous schemas, (2) Assessing conflicts and providing probabilistic estimates for values' correctness, (3) Extended querying capabilities in the presence of conflicts, and (4) Providing curation operations to help scientists resolve and investigate conflicts according to different priorities. FusionDB is realized on top of PostgreSQL DBMS.
The essence of knowledge (bases) through entity rankings
Authors: Evica Ilieva, Sebastian Michel, Aleksandar Stupar
Venue: CIKM
Year: 2013

We consider the task of automatically phrasing and computing top-k rankings over the information contained in common knowledge bases (KBs), such as YAGO or DBPedia. We assemble the thematic focus and ranking criteria of rankings by inspecting the present Subject, Predicate, Object (SPO) triples. Making use of numerical attributes contained in the KB we are also able to compute the actual ranking content, i.e., entities and their performances. We further discuss the integration of existing rankings into the ranking generation process for increased coverage and ranking quality. We report on first results obtained using the YAGO knowledge base.
Thoughts on the complex relation between linked data, semantic annotations, and ontologies
Authors: Krzysztof Janowicz, Pascal Hitzler
Venue: CIKM
Year: 2013

The relation between data, annotations, and schemata seems straightforward at first: Data are annotated with additional meta information according to some schemata in order to expose additional non-intrinsic characteristics relevant to the meaningful interpretation of said data. However, on closer examination, things are not as simple. Focusing on geo-information retrieval, we will try to disentangle the aforementioned relations. We will report from our own experience and from observations gathered by editing papers about ontologies and Linked Data for the Semantic Web journal.
Learning to rank for question routing in community question answering
Authors: Zongcheng Ji, Bin Wang
Venue: CIKM
Year: 2013

This paper focuses on the problem of Question Routing (QR) in Community Question Answering (CQA), which aims to route newly posted questions to the potential answerers who are most likely to answer them. Traditional methods to solve this problem only consider the text similarity features between the newly posted question and the user profile, while ignoring the important statistical features, including the question-specific statistical feature and the user-specific statistical features. Moreover, traditional methods are based on unsupervised learning, which is not easy to introduce the rich features into them. This paper proposes a general framework based on the learning to rank concepts for QR. Training sets consist of triples (q, asker, answerers) are first collected. Then, by introducing the intrinsic relationships between the asker and the answerers in each CQA session to capture the intrinsic labels/orders of the users about their expertise degree of the question q, two different methods, including the SVM-based and RankingSVM-based methods, are presented to learn the models with different example creation processes from the training set. Finally, the potential answerers are ranked using the trained models. Extensive experiments conducted on a real world CQA dataset from Stack Overflow show that our proposed two methods can both outperform the traditional query likelihood language model (QLLM) as well as the state-of-the-art Latent Dirichlet Allocation based model (LDA). Specifically, the RankingSVM-based method achieves statistical significant improvements over the SVM-based method and has gained the best performance.
Faceted models of blog feeds
Authors: Lifeng Jia, Clement T. Yu, Weiyi Meng
Venue: CIKM
Year: 2013

Faceted blog distillation aims at retrieving the blogs that are not only relevant to a query but also exhibit an interested facet. In this paper we consider personal and official facets. Personal blogs depict various topics related to the personal experiences of bloggers while official blogs deliver contents with bloggers' commercial influences. We observe that some terms, such as nouns, usually describe the topics of posts in blogs while other terms, such as pronouns and adverbs, normally reflect the facets of posts. Thus we present a model that estimates the probabilistic distributions of topics and those of facets in posts. It leverages a classifier to separate facet terms from topical terms in the posterior inference. We also observe that the posts from a blog are likely to exhibit the same facet. So we propose another model that constrains the posts from a blog to have the same facet distributions in its generative process. Experimental results using the TREC 2009-2010 queries over the TREC Blogs08 collection show the effectiveness of both models. Our results outperform the best known results for personal and official distillation.
Towards an enhanced and adaptable ontology by distilling and assembling online encyclopedias
Authors: Shan Jiang, Lidong Bing, Yan Zhang
Venue: CIKM
Year: 2013

In this paper, we investigate the problem of making better use of semantic knowledge obtained from different encyclopedia sources. We propose a framework to integrate different encyclopedias and reorganize the information. We also utilize Learning to Rank models to distill out more functional knowledge from the encyclopedic information and then align the knowledge with a WordNet-like ontology. Finally as a demonstration, a Chinese semantic knowledge repository named JNet is constructed based on this framework. Experiments show that the proposed methods work well and the three steps reinforce each other towards a more powerful ontology.
Learning open-domain comparable entity graphs from user search queries
Authors: Ziheng Jiang, Lei Ji, Jianwen Zhang, Jun Yan, Ping Guo, Ning Liu
Venue: CIKM
Year: 2013

A frequent behavior of internet users is to compare among various comparable entities for decision making. As an instance, a user may compare among iPhone 5, Lumia 920 etc. products before deciding which cellphone to buy. However, it is a challenging problem to know what entities are generally comparable from the users' viewpoints in the open domain Web. In this paper, we propose a novel solution, which is known as Comparable Entity Graph Mining (CEGM), to learn an open-domain comparable entity graph from the user search queries. CEGM firstly mine seed comparable entity pairs from user search queries automatically using predefined query patterns. Next, it discovers more entity pairs with a confidence classifier in a bootstrapping fashion. Newly discovered entity pairs are organized into an open-domain comparable entity graph. Based on our empirical study over 1 billion queries of a commercial search engine, we build a comparable entity graph which covers 73.4% queries in the top 50 million unique queries of a commercial search engine. Through manual labeling in sampled sub-graphs, the average precision of comparable entities is 89.4%. As applications of the learned entity graph, the entity recommendation in Web search is empirically studied.
SportSense: using motion queries to find scenes in sports videos
Authors: Ihab Al Kabary, Heiko Schuldt
Venue: CIKM
Year: 2013

We present SportSense, a system for interactive sports video retrieval using sketch-based motion queries. SportSense is based on sports videos of games, enriched with an overlay of metadata that incorporates spatio-temporal information about various events and movements. We present how sketch-based motion queries are formulated and executed, as well as the use of various intuitive input interfaces to acquire the query object. The system uses spatio-temporal index structures to facilitate interactive response times.
Spatio-temporal meme prediction: learning what hashtags will be popular where
Authors: Krishna Yeswanth Kamath, James Caverlee
Venue: CIKM
Year: 2013

In this paper, we tackle the problem of predicting what online memes will be popular in what locations. Specifically, we develop data-driven approaches building on the global footprint of 755 million geo-tagged hashtags spread via Twitter. Our proposed methods model the geo-spatial propagation of online information spread to identify which hashtags will become popular in specific locations. Concretely, we develop a novel reinforcement learning approach that incrementally updates the best geo-spatial model. In experiments, we find that the proposed method outperforms alternative linear regression based methods.
Recall oriented search on the web using semantic annotations
Authors: Rianne Kaptein, Egon L. van den Broek, Gijs Koot, Mirjam A. A. Huis in &apos, t Veld
Venue: CIKM
Year: 2013

Web search engines are optimized for early precision, which makes it difficult to perform recall oriented tasks with them. In this article, we propose several ways to leverage semantic annotations and, thereby, increase the efficiency of recall oriented search tasks, with a focus on forensic investigation. Semantic annotations, such as temporal annotations, named entities, and domain context, can be used to rerank, and cluster search result sets. In addition, domain context can be used to improve recall.
User intent and assessor disagreement in web search evaluation
Authors: Gabriella Kazai, Emine Yilmaz, Nick Craswell, Seyed M. M. Tahaghoghi
Venue: CIKM
Year: 2013

Preference based methods for collecting relevance data for information retrieval (IR) evaluation have been shown to lead to better inter-assessor agreement than the traditional method of judging individual documents. However, little is known as to why preference judging reduces assessor disagreement and whether better agreement among assessors also means better agreement with user satisfaction, as signaled by user clicks. In this paper, we examine the relationship between assessor disagreement and various click based measures, such as click preference strength and user intent similarity, for judgments collected from editorial judges and crowd workers using single absolute, pairwise absolute and pairwise preference based judging methods. We find that trained judges are significantly more likely to agree with each other and with users than crowd workers, but inter-assessor agreement does not mean agreement with users. Switching to a pairwise judging mode improves crowdsourcing quality close to that of trained judges. We also find a relationship between intent similarity and assessor-user agreement, where the nature of the relationship changes across judging modes. Overall, our findings suggest that the awareness of different possible intents, enabled by pairwise judging, is a key reason of the improved agreement, and a crucial requirement when crowdsourcing relevance data.
Ontology authoring with FORZA
Authors: C. Maria Keet, Muhammad Tahir Khan, Chiara Ghidini
Venue: CIKM
Year: 2013

Generic, reusable ontology elements, such as a foundational ontology's categories and part-whole relations, are essential for good and interoperable knowledge representation. Ontology developers, which include domain experts and novices, face the challenge to figure out which category or relationship to choose for their ontology authoring task. To reduce this bottleneck, there is a need to have guidance to handle these Ontology-laden entities. We solve this with a generic approach and realize it with the Foundational Ontology and Reasoner-enhanced axiomatiZAtion (FORZA) method, containing DOLCE, a decision diagram for DOLCE categories, part-whole relations, and an automated reasoner that is used during the authoring process to propose feasible axioms. This fusion has been integrated in the MoKi ontology development tool to validate its implementability.
Generalizing diversity detection in blog feed retrieval
Authors: Mostafa Keikha, Fabio Crestani, W. Bruce Croft
Venue: CIKM
Year: 2013

The goal of a blog retrieval system is to retrieve and rank blogs, as collections of documents, in response to a given query. Previous studies have shown that diversity among the top retrieved posts from a blog is a positive feature for indicating relevance of the blog to the query. However, existing methods capture the diversity of a blog using post-level properties that limits their application to a specific category of retrieval methods. In this paper, we propose a blog-level diversity measure where there is no assumption made about the underlying blog-ranking technique. The proposed measure enables us to integrate diversity in any existing blog retrieval method. Our experimental results show that the proposed method, while being more general, produces comparable results to the post-level diversity detection methods.
Flexible and adaptive subspace search for outlier analysis
Authors: Fabian Keller, Emmanuel Müller, Andreas Wixler, Klemens Böhm
Venue: CIKM
Year: 2013

There exists a variety of traditional outlier models, which measure the deviation of outliers with respect to the full attribute space. However, these techniques fail to detect outliers that deviate only w.r.t. an attribute subset. To address this problem, recent techniques focus on a selection of subspaces that allow: (1) A clear distinction between clustered objects and outliers; (2) a description of outlier reasons by the selected subspaces. However, depending on the outlier model used, different objects in different subspaces have the highest deviation. It is an open research issue to make subspace selection adaptive to the outlier score of each object and flexible w.r.t. the use of different outlier models.
Scalable diversification of multiple search results
Authors: Hina A. Khan, Marina Drosou, Mohamed A. Sharaf
Venue: CIKM
Year: 2013

The explosion of big data emphasizes the need for scalable data diversification, especially for applications based on web, scientific, and business databases. However, achieving effective diversification in a multi-user environment is a rather challenging task due to the inherent high processing costs of current data diversification techniques. In this paper, we address the concurrent diversification of multiple search results using various approximation techniques that provide orders of magnitude reductions in processing cost, while maintaining comparable quality of diversification as compared to sequential methods. Our extensive experimental evaluation shows the scalability exhibited by our proposed methods under various workload settings.
Using historical click data to increase interleaving sensitivity
Authors: Eugene Kharitonov, Craig Macdonald, Pavel Serdyukov, Iadh Ounis
Venue: CIKM
Year: 2013

Interleaving is an online evaluation method to compare two alternative ranking functions based on the users' implicit feedback. In an interleaving experiment, the results from two ranking functions are merged in a single result list and presented to the users. The users' click feedback on the merged result list is analysed to derive preferences over the ranking functions. An important property of interleaving methods is their sensitivity, i.e. their ability to reliably derive the comparison outcome with a relatively small amount of user behaviour data. This allows testing of changes in the search engine ranking functions frequently and, as a result, rapid iterations in developing search quality improvements can be achieved. In this paper we propose a novel approach to further improve interleaving sensitivity by using pre-experimental user behaviour data. In particular, the click history is used to train a click model, which is then used to predict which interleaved result pages are likely to contribute to the experiment outcome. The probabilities of presenting these interleaved result pages to the users are then optimised, such that the sensitivity of interleaving is maximised. In order to evaluate the proposed approach, we re-use data from six actual interleaving experiments, previously performed by a commercial search engine. Our results demonstrate that the proposed approach outperforms a state-of-the-art baseline, achieving up to a median of 48% reduction in the number of impressions for the same level of confidence.
Mining causal topics in text data: iterative topic modeling with time series feedback
Authors: Hyun Duk Kim, Malú Castellanos, Meichun Hsu, ChengXiang Zhai, Thomas A. Rietz, Daniel Diermeier
Venue: CIKM
Year: 2013

Many applications require analyzing textual topics in conjunction with external time series variables such as stock prices. We develop a novel general text mining framework for discovering such causal topics from text. Our framework naturally combines any given probabilistic topic model with time-series causal analysis to discover topics that are both coherent semantically and correlated with time series data. We iteratively refine topics, increasing the correlation of discovered topics with the time series. Time series data provides feedback at each iteration by imposing prior distributions on parameters. Experimental results show that the proposed framework is effective.
LR-PPR: locality-sensitive, re-use promoting, approximate personalized pagerank computation
Authors: Jung Hyun Kim, K. Selçuk Candan, Maria Luisa Sapino
Venue: CIKM
Year: 2013

Personalized PageRank (PPR) based measures of node proximity have been shown to be highly effective in many prediction and recommendation applications. The use of personalized PageRank for large graphs, however, is difficult due to its high computation cost. In this paper, we propose a Locality-sensitive, Re-use promoting, approximate personalized PageRank (LR-PPR) algorithm for efficiently computing the PPR values relying on the localities of the given seed nodes on the graph: (a) The LR-PPR algorithm is locality sensitive in the sense that it reduces the computational cost of the PPR computation process by focusing on the local neighborhoods of the seed nodes. (b) LR-PPR is re-use promoting in that instead of performing a monolithic computation for the given seed node set using the entire graph, LR-PPR divides the work into localities of the seeds and caches the intermediary results obtained during the computation. These cached results are then reused for future queries sharing seed nodes. Experiment results for different data sets and under different scenarios show that LR-PPR algorithm is highly-efficient and accurate.
Understanding how people interact with web search results that change in real-time using implicit feedback
Authors: Jin Young Kim, Mark Cramer, Jaime Teevan, Dmitry Lagun
Venue: CIKM
Year: 2013

The way a searcher interacts with query results can reveal a lot about what is being sought. Considerable research has gone into using implicit relevance feedback to identify relevant con-tent in real-time, but little is known about how to best present this newly identified relevant content to users. In this paper we compare a traditional search interface with one that dynamical-ly re-ranks and recommends search results as the user interacts with it in order to build a picture of how and when users should be offered dynamically identified relevant content. We present several studies that compare logged behavior for hun-dreds of thousands of users and millions of queries as well as self-reported measures of success across the two interaction models. Compared to traditional web search, users presented with dynamically ranked results exhibit higher engagement and find information faster, particularly during exploratory tasks. These findings have implications for how search engines might best exploit implicit feedback in real-time in order to help users identify the most relevant results as quickly as possible.
Modeling dynamics of meta-populations with a probabilistic approach: global diffusion in social media
Authors: Minkyoung Kim, David Newth, Peter Christen
Venue: CIKM
Year: 2013

Increasingly, diverse online social networks are locally and globally interconnected by sharing information in the Web ecosystem. Accordingly, emergent macro-level phenomena have been observed, such as global spread of news across different types of social media. Such real-world diffusion is hard to define with a single social platform alone since dynamic influences between heterogeneous social networks are not negligible. Also, the underlying structural property of networks is important, as it drives the diffusion process in a stochastic way. In this paper, we propose a macro-level diffusion model with a probabilistic approach by combining both heterogeneity and structural connectivity of social networks. As real-world phenomena, we take cases from news diffusion across News, social networking sites (SNS), and Blog media using the ICWSM'11 Spinn3r dataset which contains over 386 million Web documents covering a one-month period in early 2011. We find that influence between different media types is varied by context of information. News media are the most influential in the Arts and Economy categories, while SNS and Blog media are in the Politics and Culture categories, respectively. Also, controversial topics such as political protests and multiculturalism failure tend to spread concurrently across social media, while entertainment topics such as film releases and celebrities are likely driven by internal interactions within single social platforms. We expect that the proposed model applies to a wider class of diffusion phenomena in diverse fields including the social sciences, marketing, and neuroscience, and that it provides a way of interpreting dynamics of meta-populations in terms of strength and directionality of influences among them.
Hierarchical subtopic mining for topic annotation
Authors: Se-Jong Kim, Ki-Young Shin, Jong-Hyeok Lee
Venue: CIKM
Year: 2013

Some authors of web documents attached keywords as metadata to the documents. These keywords reflected authors' intents for the main topics. To automatically acquire topic-related keywords, this paper proposes a method that mines subtopics as topic-related keywords using simple patterns and the hierarchical structure of candidate strings based on the clusters of relevant documents using web documents for the Japanese and English languages. We created alternative partial-topics from the original topic, and extracted various candidate strings using simple patterns based on each partial-topic and POS tags. We constructed the hierarchical structure of the candidate strings according to the proposed process, and ranked them using this structure and the frequency information for each group of the candidate strings in the area satisfying the diversity requirement of the hierarchical structure. Our method outperformed the baselines, and the results will be useful in various topic annotations.