OpenRaiser/Intern-Atlas huggingface dataset:
Enhancing Graph Kernels via Successive Embeddings
Authors: Giannis Nikolentzos, Michalis Vazirgiannis
Venue: CIKM
Year: 2018

Graph kernels have recently emerged as a promising approach to perform machine learning on graph-structured data. A graph kernel implicitly embedds graphs in a Hilbert space and computes the inner product between these representations. However, the inner product operation greatly limits the representational power of kernels between graphs. In this paper, we propose to perform a series of successive embeddings in order to improve the performance of existing graph kernels and derive more expressive kernels. We first embed the input graphs in a Hilbert space using a graph kernel and then we embed them into another space by employing popular kernels for vector data (e.g., gaussian kernel). Our experiments on several datasets show that by composing kernels, we can achieve significant improvements in classification accuracy.
Knowledge Graph Completion by Context-Aware Convolutional Learning with Multi-Hop Neighborhoods
Authors: Byungkook Oh, Seungmin Seo, Kyong-Ho Lee
Venue: CIKM
Year: 2018

The main focus of relational learning for knowledge graph completion (KGC) lies in exploiting rich contextual information for facts. Many state-of-the-art models incorporate fact sequences, entity types, and even textual information. Unfortunately, most of them do not fully take advantage of rich structural information in a KG, i.e., connectivity patterns around each entity. In this paper, we propose a context-aware convolutional learning (CACL) model which jointly learns from entities and their multi-hop neighborhoods. Since we directly utilize the connectivity patterns contained in each multi-hop neighborhood, the structural role similarity among entities can be better captured, resulting in more informative entity and relation embeddings. Specifically, CACL collects entities and relations from the multi-hop neighborhood as contextual information according to their relative importance and uniquely maps them to a linear vector space. Our convolutional architecture leverages a deep learning technique to represent each entity along with its linearly mapped contextual information. Thus, we can elaborately extract the features of key connectivity patterns from the context and incorporate them into a score function which evaluates the validity of facts. Experimental results on the newest datasets show that CACL outperforms existing approaches by successfully enriching embeddings with neighborhood information.
User Group Analytics: Discovery, Exploration and Visualization
Authors: Behrooz Omidvar-Tehrani, Sihem Amer-Yahia
Venue: CIKM
Year: 2018

User data is becoming increasingly available in various domains from the social Web to patient health records. User data is characterized by a combination of demographics (e.g., age, gender, occupation) and user actions (e.g., rating a movie, following a diet). User data analytics is usually based on identifying group-level behaviors such as "countryside teachers who watch Woody Allen movies." User Group Analytics (UGA) addresses peculiarities of user data such as noise and sparsity. This tutorial reviews research on UGA and discusses different approaches and open challenges for group discovery, exploration, and visualization.
Label Propagation with Neural Networks
Authors: Aditya Pal, Deepayan Chakrabarti
Venue: CIKM
Year: 2018

Label Propagation (LP) is a popular transductive learning method for very large datasets, in part due to its simplicity and ability to parallelize. However, it has limited ability to handle node features, and its accuracy can be sensitive to the number of iterations. We propose an algorithm called LPNN that solves these problems by a loose-coupling of LP with a feature-based classifier. We experimentally establish the effectiveness of LPNN.
Recommending Serendipitous Items using Transfer Learning
Authors: Gaurav Pandey, Denis Kotkov, Alexander Semenov
Venue: CIKM
Year: 2018

Most recommender algorithms are designed to suggest relevant items, but suggesting these items does not always result in user satisfaction. Therefore, the efforts in recommender systems recently shifted towards serendipity, but generating serendipitous recommendations is difficult due to the lack of training data. To the best of our knowledge, there are many large datasets containing relevance scores (relevance oriented) and only one publicly available dataset containing a relatively small number of serendipity scores (serendipity oriented). This limits the learning capabilities of serendipity oriented algorithms. Therefore, in the absence of any known deep learning algorithms for recommending serendipitous items and the lack of large serendipity oriented datasets, we introduce SerRec our novel transfer learning method to recommend serendipitous items. SerRec uses transfer learning to firstly train a deep neural network for relevance scores using a large dataset and then tunes it for serendipity scores using a smaller dataset. Our method shows benefits of transfer learning for recommending serendipitous items as well as performance gains over the state-of-the-art serendipity oriented algorithms
tHoops: A Multi-Aspect Analytical Framework for Spatio-Temporal Basketball Data
Authors: Evangelos E. Papalexakis, Konstantinos Pelechrinis
Venue: CIKM
Year: 2018

During the past few years advancements in sports information systems and technology has allowed the collection of a number of detailed spatio-temporal data that capture various aspects of basketball. For example, shot charts, that is, maps capturing locations of (made or missed) shots, and spatio-temporal trajectories for the players on the court can capture information about the offensive and defensive tendencies, as well as, schemes used by a team. Characterization of these processes is important for player and team comparisons, scouting, game preparation etc. Team and player tendencies have traditionally been compared in a heuristic manner, which inevitably can lead to subtle but crucial information being ignored. Recently automated ways for these comparisons have appeared in the sports analytics literature. However, these approaches are almost exclusively focused on the spatial distribution of the underlying actions (usually shots taken), ignoring a multitude of other parameters that can affect the action studied. In this study, we propose a framework based on tensor decomposition for obtaining a set of prototype spatio-temporal patterns based on the core spatio-temporal information and contextual meta-data. At the epicenter of our work is a 3D tensor $\tensor$, whose dimensions represent the entity under consideration (team, player, possession etc.), the location on the court and time. We make use of the PARAFAC decomposition and we decompose the tensor into several interpretable patterns, that can be thought of as prototype patterns of the process examined (e.g., shot selection, offensive schemes etc.). We also introduce an approach for choosing the number of components to be considered. Using the tensor components, we can then express every entity as a weighted combination of these components. Finally, the framework introduced in this paper has applications that go beyond purely pattern analysis. In particular, it can facilitate a variety of tasks in the work-flow of a franchise's basketball operations as well as in the sports analytics research community.
A Dynamical System on Bipartite Graphs
Authors: Kishore Papineni, Pratik Worah
Venue: CIKM
Year: 2018

This paper poses a non-linear dynamical system on bipartite graphs and shows its stability under certain conditions. The dynamical system changes the weights on the nodes of the graph in each time step. The underlying weight transformation is non-linear, motivated by information gain in a document retrieval setting. Stability analysis of this problem is therefore more involved than that of PageRank-like algorithms. We show convergence using methods from Lyapunov theory and also provide some examples of how the algorithm performs when ranking keywords and sentences in a set of documents.
AQuPR: Attention based Query Passage Retrieval
Authors: Parth Pathak, Mithun Das Gupta, Niranjan Nayak, Harsh Kohli
Venue: CIKM
Year: 2018

Search queries issued over the Web increasingly look like questions, especially as the domain becomes more specific. Finding good response to such queries amounts to finding relevant passages from Web documents. Traditional information retrieval based Web search still matches the query to the words in the entire document. With the advent of machine reading comprehension techniques, Web search is moving more towards identifying the best sentence / group of sentences in the document. We present AQuPR an A ttention based Qu ery P assage R etrieval system to find human acceptable answer containing passages to technology queries issued over the Web. We train character level embeddings for the query and passage pairs, train a deep recurrent network with a novel simplified attention mechanism and incorporate additional signals present in Web documents to improve the performance of such a system. We collect a database of human issued queries along with their answer passages and learn an end to end system to enable automated query resolution. We present results for answering human issued search queries which show considerable promise against basic versions of current generation question answering systems.
Automatic Conversational Helpdesk Solution using Seq2Seq and Slot-filling Models
Authors: Mayur Patidar, Puneet Agarwal, Lovekesh Vig, Gautam Shroff
Venue: CIKM
Year: 2018

Helpdesk is a key component of any large IT organization, where users can log a ticket about any issue they face related to IT infrastructure, administrative services, human resource services, etc. Normally, users have to assign appropriate set of labels to a ticket so that it could be routed to right domain expert who can help resolve the issue. In practice, the number of labels are very large and organized in form of a tree. It is non-trivial to describe the issue completely and attach appropriate labels unless one knows the cause of the problem and the related labels. Sometimes domain experts discuss the issue with the users and change the ticket labels accordingly, without modifying the ticket description. This results in inconsistent and badly labeled data, making it hard for supervised algorithms to learn from. In this paper, we propose a novel approach of creating a conversational helpdesk system, which will ask relevant questions to the user, for identification of the right category and will then raise a ticket on users' behalf. We use attention based seq2seq model to assign the hierarchical categories to tickets. We use a slot filling model to help us decide what questions to ask to the user, if the top-k model predictions are not consistent. We also present a novel approach to generate training data for the slot filling model automatically based on attention in the hierarchical classification model. We demonstrate via a simulated user that the proposed approach can give us a significant gain in accuracy on ticket-data without asking too many questions to users. Finally, we also show that our seq2seq model is as versatile as other approaches on publicly available datasets, as state of the art approaches.
StuffIE: Semantic Tagging of Unlabeled Facets Using Fine-Grained Information Extraction
Authors: Radityo Eko Prasojo, Mouna Kacimi, Werner Nutt
Venue: CIKM
Year: 2018

Recent knowledge extraction methods are moving towards ternary and higher-arity relations to capture more information about binary facts. An example is to include the time, the location, and the duration of a specific fact. These relations can be even more complex to extract in advanced domains such as news, where events typically come with different facets including reasons, consequences, purposes, involved parties, and related events. The main challenge consists in first finding the set of facets related to each fact, and second tagging those facets to the relevant category. In this paper, we tackle the above problems by proposing StuffIE, a fine-grained information extraction approach which is facet-centric. We exploit the Stanford dependency parsing enhanced by lexical databases such as WordNet to extract nested triple relations. Then, we exploit the syntactical dependencies to semantically tag facets using distant learning based on Oxford dictionary. We have tested the accuracy of the extracted facets and their semantic tags using DUC'04 dataset. The results show the high accuracy and coverage of our approach with respect to ClausIE, OLLIE, SEMAFOR SRL and Illinois SRL.
Holistic Crowd-Powered Sorting via AID: Optimizing for Accuracies, Inconsistencies, and Difficulties
Authors: Shreya Rajpal, Aditya G. Parameswaran
Venue: CIKM
Year: 2018

We revisit the fundamental problem of sorting objects using crowdsourced pairwise comparisons. Prior work either treats these comparisons as independent tasks -- in which case the resulting judgments may end up being inconsistent, or fails to capture the accuracies of workers or difficulties of the pairwise comparisons -- in which case the resulting judgments may end up being consistent with each other, but ultimately more inaccurate. We adopt a holistic approach that constructs a graph across the set of objects respecting consistency constraints. Our key contribution is a novel method of encoding difficulty of comparisons in the form of constraints on edges. We couple that with an iterative E-M-style procedure to uncover information about latent variables and constraints, along with the graph structure. We show that our approach predicts edge directions as well as difficulty values more accurately than baselines on both real and simulated data, across graphs of various sizes.
Fouilla: Navigating DBpedia by Topic
Authors: Tanguy Raynaud, Julien Subercaze, Frédérique Laforest
Venue: CIKM
Year: 2018

Navigating large knowledge bases made of billions of triples is very challenging. In this demonstration, we showcase Fouilla, a topical Knowledge Base browser that offers a seamless navigational experience of DBpedia. We propose an original approach that leverages both structural and semantic contents of Wikipedia to enable a topic-oriented filter on DBpedia entities. We devise an approach to drastically reduce the query time and to ensure a seamless browsing experience to the end user. We demonstrate how our system offers a novel and meaningful experience of DBpedia browsing by challenging the user to search for relevant information within the Knowledge Base in different use cases.
Beacon in the Dark: A System for Interactive Exploration of Large Email Corpora
Authors: Tim Repke, Ralf Krestel, Jakob Edding, Moritz Hartmann, Jonas Hering, Dennis Kipping, Hendrik Schmidt, Nico Scordialo, Alexander Zenner
Venue: CIKM
Year: 2018

The large amount of heterogeneous data in these email corpora renders experts' investigations by hand infeasible. Auditors or journalists, e.g., who are looking for irregular or inappropriate content or suspicious patterns, are in desperate need for computer-aided exploration tools to support their investigations. We present our Beacon system for the exploration of such corpora at different levels of detail. A distributed processing pipeline combines text mining methods and social network analysis to augment the already semi-structured nature of emails. The user interface ties into the resulting cleaned and enriched dataset. For the interface design we identify three objectives expert users have: gain an initial overview of the data to identify leads to investigate, understand the context of the information at hand, and have meaningful filters to iteratively focus onto a subset of emails. To this end we make use of interactive visualisations based on rearranged and aggregated extracted information to reveal salient patterns.
Shifting Information Interactions
Authors: Maarten de Rijke
Venue: CIKM
Year: 2018

Modern information retrieval systems, such as search engines, recommender systems, and conversational agents, are best thought of as interactive systems, that is, systems that interact with and learn from user behavior. The ways in which people interact with information continue to change, with different devices, different presentation formats, and different information seeking scenarios. These changes give rise to new algorithmic and conceptual questions. For instance, how can we learn to rank good results if the display preferences are not known? How might we automatically generate questions to elicit a user's preferences so that an information retrieval system can adjust its results as efficiently as possible? And how should we understand information seeking dialogues? The talk is based on joint work with Claudio Di Ciccio, Julia Kiseleva, Harrie Oosterhuis, Filip Radlinski, Kate Revoredo, Anna Sepliarskaia, and Svitlana Vakulenko.
Unsupervised Evaluation of Text Co-clustering Algorithms Using Neural Word Embeddings
Authors: François Role, Stanislas Morbieu, Mohamed Nadif
Venue: CIKM
Year: 2018

Text clustering, which allows to divide a dataset into groups of similar documents, plays an important role at various stages of the information retrieval process. Co-clustering is an extension of one-side clustering, and consists in simultaneously clustering the rows and columns of a data matrix. However, while co-clustering algorithms consider both dimensions of a document-term matrix, they are usually evaluated on the quality of the obtained document clusters alone. In this paper, we therefore propose an evaluation scheme that accounts for the two-dimensional nature of co-clustering algorithms, thus allowing for a more precise evaluation of their performance. Another important benefit of the proposed approach is that it does not require the use of any prior labels. This is achieved by leveraging large, public domain embedding matrices (GloVe, word2vec, FastText) to compute comparable representations of both document and term clusters. Experiments carried out on several textual datasets show that the proposed measures are both reliable and stable, and can even provide hints to improve co-clustering performance.
Using Word Embeddings for Information Retrieval: How Collection and Term Normalization Choices Affect Performance
Authors: Dwaipayan Roy, Debasis Ganguly, Sumit Bhatia, Srikanta Bedathur, Mandar Mitra
Venue: CIKM
Year: 2018

Neural word embedding approaches, due to their ability to capture semantic meanings of vocabulary terms, have recently gained attention of the information retrieval (IR) community and have shown promising results in improving ad hoc retrieval performance. It has been observed that these approaches are sensitive to various choices made during the learning of word embeddings and their usage, often leading to poor reproducibility. We study the effect of varying following two parameters, viz., i) the term normalization and ii) the choice of training collection, on ad hoc retrieval performance with word2vec and fastText embeddings. We present quantitative estimates of similarity of word vectors obtained under different settings, and use embeddings based query expansion task to understand the effects of these parameters on IR effectiveness.
Exploring Diversified Similarity with Kundaha
Authors: Lúcio F. D. Santos, Gustavo Blanco, Daniel de Oliveira, Agma J. M. Traina, Caetano Traina Jr., Marcos V. N. Bedo
Venue: CIKM
Year: 2018

Exploring large medical image sets by means of traditional similarity query criteria (e.g., neighborhood) can be fruitless if retrieved images are too similar among themselves. This demonstration introduces Kundaha, an exploration tool that assists experts in retrieving and navigating on results from a diversified similarity perspective of user-posed queries. Its implementation includes a wide set of metrics, descriptors, and indexes for enhancing query execution. Users can combine such features with diversified similarity criteria for the organized exploration of result sets and also employ relevance feedback cycles for finding new query-based viewpoints.
Exploring Neural Translation Models for Cross-Lingual Text Similarity
Authors: Kazuhiro Seki
Venue: CIKM
Year: 2018

This paper explores a neural network-based approach to computing similarity of two texts written in different languages. Such similarity can be useful for a variety of applications including cross-lingual information retrieval and cross-lingual text classification. To compute similarity, we focus on neural machine translation models and examine the utility of their intermediate states. Through experiments on an English-Japanese translation corpus, it is demonstrated that the intermediate states of input texts are indeed beneficial for computing cross-lingual text similarity, outperforming other approaches including a strong machine translation-based baseline.
Improving the Efficiency of Inclusion Dependency Detection
Authors: Nuhad Shaabani, Christoph Meinel
Venue: CIKM
Year: 2018

The detection of all inclusion dependencies (INDs) in an unknown dataset is at the core of any data profiling effort. Apart from the discovery of foreign key relationships, INDs can help perform data integration, integrity checking, schema (re-)design, and query optimization. With the advent of Big Data, the demand increases for efficient INDs discovery algorithms that can scale with the input data size. To this end, we propose S-indd++ as a scalable system for detecting unary INDs in large datasets. S-indd++ applies a new stepwise partitioning technique that helps discard a large number of attributes in early phases of the detection by processing the first partitions of smaller sizes. S-indd++ also extends the concept of the attribute clustering to decide which attributes to be discarded based on the clustering result of each partition. Moreover, in contrast to the state-of-the-art, S-indd++ does not require the partition to fit into the main memory- which is a highly appreciable property in the face of the ever growing datasets. We conducted an exhaustive evaluation of S-indd ++ by applying it to large datasets with thousands attributes and more than 266 million tuples. The results show the high superiority of S-indd++ over the state-of-the-art. S-indd++ reduced up to 50~% of the runtime in comparison with Binder, and up to 98~% in comparison with S-indd.
Investigating Rumor News Using Agreement-Aware Search
Authors: Jingbo Shang, Jiaming Shen, Tianhang Sun, Xingbang Liu, Anja Gruenheid, Flip Korn, Ádám D. Lelkes, Cong Yu, Jiawei Han
Venue: CIKM
Year: 2018

Recent years have witnessed a widespread increase of rumor news generated by humans and machines. Therefore, tools for investigating rumor news have become an urgent necessity. One useful function of such tools is to see ways a specific topic or event is represented by presenting different points of view from multiple sources. In this paper, we propose Maester, a novel agreement-aware search framework for investigating rumor news. Given an investigative question, Maester will retrieve related articles to that question, assign and display top articles from agree, disagree, and discuss categories to users. Splitting the results into these three categories provides the user a holistic view towards the investigative question. We build Maester based on the following two key observations: (1) relatedness can commonly be determined by keywords and entities occurring in both questions and articles, and (2) the level of agreement between the investigative question and the related news article can often be decided by a few key sentences. Accordingly, we use gradient boosting tree models with keyword/entity matching features for relatedness detection, and leverage recurrent neural network to infer the level of agreement. Our experiments on the Fake News Challenge (FNC) dataset demonstrate up to an order of magnitude improvement of Maester over the original FNC winning solution, for agreement-aware search.