OpenRaiser/Intern-Atlas huggingface dataset:
A user-centric model to semantically annotate and retrieve scientific documents
Authors: Hélène de Ribaupierre, Gilles Falquet
Venue: CIKM
Year: 2013

When scientists are looking for information in document collections, they generally have a precise objective in mind: They want to find all the definitions of a concept, all the results for a particular problem, or to compare the scientific conclusions of multiples articles. In this research, we have developed a user-centric annotation model based on discourse elements. The model has been formally defined as an OWL ontology and used to annotate a corpus of scientific articles in gender studies. We show how complex queries, proposed by scientists, can be expressed in this model and solved by a description logic reasoner.
Discrimination aware classification for imbalanced datasets
Authors: Goce Ristanoski, Wei Liu, James Bailey
Venue: CIKM
Year: 2013

The problem of learning a discrimination aware model has recently received attention in the data mining community. Various methods and improved models have been proposed, with the main approach being the detection of a discrimination sensitive attribute. Once the discrimination sensitive attribute is identified, the methods aim to develop a strategy that will include the useful information from that attribute without causing any additional discrimination. Our work focuses on an aspect often overlooked in the discrimination aware classification - the scenario of an imbalanced dataset, where the number of samples from one class is disproportionate to the other. We also investigate a strategy that is directly minimizing discrimination and is independent of the class balance. Our empirical results indicate additional concerns that need to be considered when developing discrimination aware classifiers, and our proposed strategy shows promise in overcoming these concerns.
Diffusion of innovations revisited: from social network to innovation network
Authors: Xin Rong, Qiaozhu Mei
Venue: CIKM
Year: 2013

The spreading of innovations among individuals and organizations in a social network has been extensively studied. Although the recent studies among the social computing and data mining communities have produced various insightful conclusions about the diffusion process of innovations by focusing on the properties and evolution of social network structures, less attention has been paid to the interrelationships among the multiple innovations being diffused, such as the competitive and collaborative relationships between innovations. In this paper, we take a formal quantitative approach to address how different pieces of innovations socialize with each other and how the interrelationships among innovations affect users' adoption behavior, which provides a novel perspective of understanding the diffusion of innovations. Networks of innovations are constructed by mining large scale text collections in an unsupervised fashion. We are particularly interested in the following questions: what are the meaningful metrics on the network of innovations? What effects do these metrics exert on the diffusion of innovations? Do these effects vary among users with different adoption preferences or communication styles? While existing studies primarily address social influence, we provide a detailed discussion of how innovations interrelate and influence the diffusion process.
Computational frameworks for semantic analysis and wikification: keynote abstract
Authors: Dan Roth
Venue: CIKM
Year: 2013

Computational approaches to problems in Natural Language Understanding and Information Extraction are often modeled as structured predictions - predictions that involve assigning values to sets of interdependent variables. Over the last few years, one of the most successful approaches to studying these problems involves Constrained Conditional Models (CCMs), an Integer Learning Programming formulation that augments probabilistic models with declarative constraints as a way to support such decisions.
Feature-based models for improving the quality of noisy training data for relation extraction
Authors: Benjamin Roth, Dietrich Klakow
Venue: CIKM
Year: 2013

Supervised relation extraction from text relies on annotated data. Distant supervision is a scheme to obtain noisy training data by using a knowledge base of relational tuples as the ground truth and finding entity pair matches in a text corpus. We propose and evaluate two feature-based models for increasing the quality of distant supervision extraction patterns.
Parallel motif extraction from very long sequences
Authors: Majed Sahli, Essam Mansour, Panos Kalnis
Venue: CIKM
Year: 2013

Motifs are frequent patterns used to identify biological functionality in genomic sequences, periodicity in time series, or user trends in web logs. In contrast to a lot of existing work that focuses on collections of many short sequences, modern applications require mining of motifs in one very long sequence (i.e., in the order of several gigabytes). For this case, there exist statistical approaches that are fast but inaccurate; or combinatorial methods that are sound and complete. Unfortunately, existing combinatorial methods are serial and very slow. Consequently, they are limited to very short sequences (i.e., a few megabytes), small alphabets (typically 4 symbols for DNA sequences), and restricted types of motifs.
Combining dictionaries and ontologies for drug name recognition in biomedical texts
Authors: Daniel Sánchez-Cisneros, Paloma Martínez, Isabel Segura-Bedmar
Venue: CIKM
Year: 2013

Two approaches have been commonly used for recognizing Drug Name Entities in biomedical texts: machine learning-based and domain specific resources-based approaches. In this work we focus on the second one by combining (1) a dictionary-based approach that collects terms from different pharmacological data sources such as DrugBank, MeSH, RxNorm and ATC index; and (2) an ontology-based approach that maps each text unit of a source text into one or more domain-specific concepts, providing rich semantic knowledge of domain name entities using Metamap and Mgrep analyzer. The aim is to take advantage of the best of each resource used. The combined system obtains an F1 measure of 0, 667 over exact matching span evaluation.
Strategies for setting time-to-live values in result caches
Authors: Fethi Burak Sazoglu, Berkant Barla Cambazoglu, Rifat Ozcan, Ismail Sengör Altingövde, Özgür Ulusoy
Venue: CIKM
Year: 2013

In web query result caching, staleness of queries are often bounded via a time-to-live (TTL) mechanism, which expires the validity of cached query results at some point in time. In this work, we evaluate the performance of three alternative TTL mechanisms: time-based TTL, frequency-based TTL, and click-based TTL. Moreover, we propose hybrid approaches obtained by pair-wise combination of these mechanisms. Our results indicate that combining time-based TTL with frequency-based TTL yields superior performance (i.e., lower stale query traffic and less redundant computation) than using a particular mechanism in isolation.
"All roads lead to Rome": optimistic recovery for distributed iterative data processing
Authors: Sebastian Schelter, Stephan Ewen, Kostas Tzoumas, Volker Markl
Venue: CIKM
Year: 2013

Executing data-parallel iterative algorithms on large datasets is crucial for many advanced analytical applications in the fields of data mining and machine learning. Current systems for executing iterative tasks in large clusters typically achieve fault tolerance through rollback recovery. The principle behind this pessimistic approach is to periodically checkpoint the algorithm state. Upon failure, the system restores a consistent state from a previously written checkpoint and resumes execution from that point.
Fast parameterless density-based clustering via random projections
Authors: Johannes Schneider, Michail Vlachos
Venue: CIKM
Year: 2013

Clustering offers significant insights in data analysis. Density based algorithms have emerged as flexible and efficient techniques, able to discover high-quality and potentially irregularly shaped- clusters. We present two fast density-based clustering algorithms based on random projections. Both algorithms demonstrate one to two orders of magnitude speedup compared to equivalent state-of-art density based techniques, even for modest-size datasets. We give a comprehensive analysis of both our algorithms and show runtime of O(dNlog2 N), for a d-dimensional dataset. Our first algorithm can be viewed as a fast variant of the OPTICS density-based algorithm, but using a softer definition of density combined with sampling. The second algorithm is parameter-less, and identifies areas separating clusters.
PAGE: a partition aware graph computation engine
Authors: Yingxia Shao, Junjie Yao, Bin Cui, Lin Ma
Venue: CIKM
Year: 2013

Graph partitioning is one of the key components in parallel graph computation, and the partition quality significantly affects the overall computing performance. In the existing graph computing systems, ``good'' partition schemes are preferred as they have smaller edge cut ratio and hence reduce the communication cost among working nodes. However, in an empirical study on Giraph[1], we found that the performance over well partitioned graph might be even two times worse than simple partitions. The cause is that the local message processing cost in graph computing systems may surpass the communication cost in several cases.
GAPfm: optimal top-n recommendations for graded relevance domains
Authors: Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha A. Larson, Alan Hanjalic
Venue: CIKM
Year: 2013

Recommender systems are frequently used in domains in which users express their preferences in the form of graded judgments, such as ratings. Current ranking techniques are based on one of two sub-optimal approaches: either they optimize for a binary metric such as Average Precision, which discards information on relevance levels, or they optimize for Normalized Discounted Cumulative Gain (NDCG), which ignores the dependence of an item's contribution on the relevance of more highly ranked items. We address the shortcomings of existing approaches by proposing GAPfm, the Graded Average Precision factor model, which is a latent factor model for top-N recommendation in domains with graded relevance data. The model optimizes the Graded Average Precision metric that has been proposed recently for assessing the quality of ranked results lists for graded relevance. GAPfm's advantages are twofold: it maintains full information about graded relevance and also addresses the limitations of models that optimize NDCG. Experimental results show that GAPfm achieves substantial improvements on the top-N recommendation task, compared to several state-of-the-art approaches.
Probabilistic semantic similarity measurements for noisy short texts using Wikipedia entities
Authors: Masumi Shirakawa, Kotaro Nakayama, Takahiro Hara, Shojiro Nishio
Venue: CIKM
Year: 2013

This paper describes a novel probabilistic method of measuring semantic similarity for real-world noisy short texts like microblog posts. Our method adds related Wikipedia entities to a short text as its semantic representation and uses the vector of entities for computing semantic similarity. Adding related entities to texts is generally a compound problem that involves the extraction of key terms, finding related entities for each key term, and the aggregation of related entities. Explicit Semantic Analysis (ESA), a popular Wikipedia-based method, solves these problems by summing the weighted vectors of related entities. However, this heuristic weighting highly depends on the rule of majority decision and is not suited to short texts that contain few key terms but many noisy terms. The proposed probabilistic method synthesizes these procedures by extending naive Bayes and achieves robust estimates of related Wikipedia entities for short texts. Experimental results on short text clustering using Twitter data indicated that our method outperformed ESA for short texts containing noisy terms.
Re-ranking for joint named-entity recognition and linking
Authors: Avirup Sil, Alexander Yates
Venue: CIKM
Year: 2013

Recognizing names and linking them to structured data is a fundamental task in text analysis. Existing approaches typically perform these two steps using a pipeline architecture: they use a Named-Entity Recognition (NER) system to find the boundaries of mentions in text, and an Entity Linking (EL) system to connect the mentions to entries in structured or semi-structured repositories like Wikipedia. However, the two tasks are tightly coupled, and each type of system can benefit significantly from the kind of information provided by the other. We present a joint model for NER and EL, called NEREL, that takes a large set of candidate mentions from typical NER systems and a large set of candidate entity links from EL systems, and ranks the candidate mention-entity pairs together to make joint predictions. In NER and EL experiments across three datasets, NEREL significantly outperforms or comes close to the performance of two state-of-the-art NER systems, and it outperforms 6 competing EL systems. On the benchmark MSNBC dataset, NEREL provides a 60% reduction in error over the next-best NER system and a 68% reduction in error over the next-best EL system.
Incremental shared nearest neighbor density-based clustering
Authors: Sumeet Singh, Amit Awekar
Venue: CIKM
Year: 2013

Shared Nearest Neighbor Density-based clustering (SNN-DBSCAN) is a robust graph-based clustering algorithm and has wide applications from climate data analysis to network intrusion detection. We propose an incremental extension to this algorithm IncSNN-DBSCAN, capable of finding clusters on a dataset to which frequent inserts are made. For each data point, the algorithm maintains four properties: nearest neighbor list, strengths of shared links, total connection strength and topic property. Algorithm only targets points that undergo change to their properties. We prove that, to obtain the exact clustering it is sufficient to re-compute properties for only the targeted points, followed by possible cluster mergers on newly formed links and cluster splits on the deleted links. Experiments on KDD Cup 1999 and Mopsi search engine 2012 datasets respectively demonstrate 75% and 99% reduction in the size of the set of points involved in property re-computations. By avoiding most of the redundant property computations, algorithm generates speedup up to 250 and 1000 times respectively on those datasets, while generating the exact same clustering as the non-incremental algorithm. We experimentally verify our claim for up to 2500 inserts on both datasets. However, speedup comes at the cost of up to 48 times more memory usage.
Automated probabilistic modeling for relational data
Authors: Sameer Singh, Thore Graepel
Venue: CIKM
Year: 2013

Probabilistic graphical model representations of relational data provide a number of desired features, such as inference of missing values, detection of errors, visualization of data, and probabilistic answers to relational queries. However, adoption has been slow due to the high level of expertise expected both in probability and in the domain from the user. Instead of requiring a domain expert to specify the probabilistic dependencies of the data, we present an approach that uses the relational DB schema to automatically construct a Bayesian graphical model for a database. This resulting model contains customized distributions for the attributes, latent variables that cluster the records, and factors that reflect and represent the foreign key links, whilst allowing efficient inference. Experiments demonstrate the accuracy of the model and scalability of inference on synthetic and real-world data.
URL tree: efficient unsupervised content extraction from streams of web documents
Authors: Borut Sluban, Miha Grcar
Venue: CIKM
Year: 2013

The Web represents the largest, and an increasingly growing, source of information. Extracting meaningful content from Web pages presents a challenging problem, already extensively addressed in the offline setting. In this work, we focus on content extraction from streams of HTML documents. We present an infrastructure that converts continuously acquired HTML documents into a stream of plain text documents. The presented pipeline consists of RSS readers for data acquisition from different Web sites, a duplicate removal component, and a novel content extraction algorithm which is efficient, unsupervised, and language-independent. Our content extraction approach is based on the observation that HTML documents from the same source normally share a common template. The core of the proposed content extraction algorithm is a simple data structure called URL Tree. The performance of the algorithm was evaluated in a stream setting on a time-stamped semi-automatically annotated dataset which was made publicly available. We compared the performance of URL Tree with that of several open source content extraction algorithms. The evaluation results show that our stream-based algorithm already starts outperforming the other algorithms after only 10 to 100 documents from a specific domain.
Discovering and managing quantitative association rules
Authors: Chunyao Song, Tingjian Ge
Venue: CIKM
Year: 2013

Although association rule mining has been studied in the literature for quite a while and numerical attributes are prevalent, perhaps surprisingly, the state-of-the-art quantitative association rule mining is rather inefficient and ineffective in discovering all useful rules. In this paper, we propose a novel divide and conquer two-phase algorithm, which is guaranteed to find all good rules efficiently. We further devise an optimization technique for performance. Moreover, we discuss a few issues with managing and using the discovered quantitative association rules. We perform a comprehensive experimental study which shows that our algorithm is one to two orders of magnitude faster than the state-of-the-art one. In addition, we discover significantly more rules that are useful for prediction.
Query execution timing: taming real-time anytime queries on multicore processors
Authors: Chunyao Song, Zheng Li, Tingjian Ge, Jie Wang
Venue: CIKM
Year: 2013

Answering real-time queries, especially over probabilistic data, is becoming increasingly important for service providers. We study anytime query processing algorithms, and extend the traditional query execution plan with a timing component. Our focus is how to determine this timing component, given the queries' deadline constraints. We consider the common multicore processors. Specifically, we propose two query optimization modes: offline periodic optimization and online optimization. We devise efficient algorithms for both offline and online cases followed by a competitive analysis to show the power of our online optimization. Finally, we perform a systematic experimental evaluation using real-world datasets to verify our approaches.
TellMyRelevance!: predicting the relevance of web search results from cursor interactions
Authors: Maximilian Speicher, Andreas Both, Martin Gaedke
Venue: CIKM
Year: 2013

It is crucial for the success of a search-driven web application to answer users' queries in the best possible way. A common approach is to use click models for guessing the relevance of search results. However, these models are imprecise and waive valuable information one can gain from non-click user interactions. We introduce TellMyRelevance!---a novel automatic end-to-end pipeline for tracking cursor interactions at the client, analyzing these and learning according relevance models. Yet, the models depend on the layout of the search results page involved, which makes them difficult to evaluate and compare. Thus, we use a Random Mouse Cursor as an extension to our pipeline for generating layout-dependent baselines. Based on these, we can perform evaluations of real-world relevance models. A large-scale interaction log analysis showed that we can learn relevance models whose predictions compare favorably to predictions of an existing state-of-the-art click model.
Proximity2-aware ranking for textual, temporal, and geographic queries
Authors: Jannik Strötgen, Michael Gertz
Venue: CIKM
Year: 2013

Temporal and geographic information needs are frequent and important but not well served by standard IR systems. Recent approaches address such needs by extracting and normalizing temporal and geographic expressions from documents. They calculate specific scores for the temporal and/ or geographic parts of a query. However, all approaches assume independence between the different query parts. In this paper, we present a new model to rank documents according to combined textual, temporal, and geographic queries. The independence assumption between the query parts is eliminated by calculating proximity scores. Thus, documents are regarded to be more relevant if terms and expressions satisfying the different query parts occur close to each other in a document. As our evaluations based on the NTCIR-GeoTime data show, our proposed model outperforms baseline models that do not use proximity information.
SRbench-a benchmark for soundtrack recommendation systems
Authors: Aleksandar Stupar, Sebastian Michel
Venue: CIKM
Year: 2013

In this work, a benchmark to evaluate the retrieval performance of soundtrack recommendation systems is proposed. Such systems aim at finding songs that are played as background music for a given set of images. The proposed benchmark is based on preference judgments, where relevance is considered a continuous ordinal variable and judgments are collected for pairs of songs with respect to a query (i.e., set of images). To capture a wide variety of songs and images, we use a large space of possible music genres, different emotions expressed through music, and various query-image themes. The benchmark consists of two types of relevance assessments: (i) judgments obtained from a user study, that serve as a ``gold standard'' for (ii) relevance judgments gathered through Amazon's Mechanical Turk. We report on the performance of two state-of-the-art soundtrack recommendation systems using the proposed benchmark.
PIKM 2013: the 6th ACM workshop for ph.d. students in information and knowledge management
Authors: Fabian M. Suchanek, Anisoara Nica
Venue: CIKM
Year: 2013

The PIKM workshop gives Ph.D. students an opportunity to present their dissertation proposals at a global stage. Similarly to the CIKM, the PIKM workshop covers a wide range of topics in the areas of databases, information retrieval and knowledge management. Interdisciplinary work across these tracks is particularly encouraged.
AKBC 2013: third workshop on automated knowledge base construction
Authors: Fabian M. Suchanek, Sebastian Riedel, Sameer Singh, Partha Pratim Talukdar
Venue: CIKM
Year: 2013

The AKBC 2013 workshop aims to be a venue of excellence and vision in the area of knowledge base construction. This year's workshop will feature keynotes by ten leading researchers in the field, including from Google, Microsoft, Stanford, and CMU. The submissions focus on visionary ideas instead of on experimental evaluation. Nineteen accepted papers will be presented as posters, with nine exceptional papers also highlighted as spotlight talks. Thereby, the workshop aims provides a vivid forum of discussion about the field of automated knowledge base construction.