OpenRaiser/Intern-Atlas huggingface dataset:
Tracking Semantic Evolutionary Changes in Large-Scale Ontological Knowledge Bases
Authors: Zhao Liu, Chang Lu, Ghadah Alghamdi, Renate A. Schmidt, Yizheng Zhao
Venue: CIKM
Year: 2021

This paper is concerned with the problem of computing the semantic difference between different versions of large-scale ontological knowledge bases using a uniform interpolation (UI) approach. The semantic difference between two versions of an ontology are the axioms entailed by one version but not the other version, reflecting the evolutionary changes of the content of the ontology. In general, computing such axioms is not computationally feasible, since there are infinitely many of them. UI is an advanced reasoning technique that seeks to create restricted views of ontologies; it provides an effective means for computing a finite representation of the difference between two ontologies. While existing UI methods are designed for languages that are either more expressive or less expressive than the description logic ELH, the underlying language of typical large-scale ontologies, in this paper, we introduce a practical UI method tailored for the task of computing the semantic difference in large-scale ELH-ontologies. The method is terminating, sound, and can always compute UI results possibly including fresh definer symbols. Two case studies on different versions of the SNOMED CT terminology show that the method has overcome major limitations of existing UI methods and can be used to reveal modeling changes that have occurred over successive releases of SNOMED CT.
Graph Embedding Based on Euclidean Distance Matrix and its Applications
Authors: Zhihong Liu, Huiyu Li, Ruixin Li, Yong Zeng, Jianfeng Ma
Venue: CIKM
Year: 2021

Graph embedding converts a graph into a multi-dimensional space in which the graph structural information or graph properties are maximumly preserved. It is an effective and efficient way to provide users a deeper understanding of what is behind the data and thus can benefit a lot of useful applications. However, most graph embedding methods suffer from high computation and space costs. In this paper, we present a simple graph embedding method that directly embeds the graph into its Euclidean distance space. This method does not require the learned representations to be low dimensional, but it has several good characteristics. We find that the centrality of nodes/edges can be represented by the position of nodes or the length of edges when a graph is embedded. Besides, the edge length is closely related to the density of regions in a graph. We then apply this graph embedding method into graph analytics, such as community detection, graph compression, and wormhole detection, etc. Our evaluation shows the effectiveness and efficiency of this embedding method and contends that it yields a promising approach to graph analytics.
Concept-Aware Denoising Graph Neural Network for Micro-Video Recommendation
Authors: Yiyu Liu, Qian Liu, Yu Tian, Changping Wang, Yanan Niu, Yang Song, Chenliang Li
Venue: CIKM
Year: 2021

Recently, micro-video sharing platforms such as Kuaishou and Tiktok have become a major source of information for people's lives. Thanks to the large traffic volume, short video lifespan and streaming fashion of these services, it has become more and more pressing to improve the existing recommender systems to accommodate these challenges in a cost-effective way. In this paper, we propose a novel concept-aware denoising graph neural network (named Conde) for micro-video recommendation. Conde consists of a three-phase graph convolution process to derive user and micro-video representations: warm-up propagation, graph denoising and preference refinement. A heterogeneous tripartite graph is constructed by connecting user nodes with video nodes, and video nodes with associated concept nodes, extracted from captions and comments of the videos. To address the noisy information in the graph, we introduce a user-oriented graph denoising phase to extract a subgraph which can better reflect the user's preference. Despite the main focus of micro-video recommendation in this paper, we also show that our method can be generalized to other types of tasks. Therefore, we also conduct empirical studies on a well-known public E-commerce dataset. The experimental results suggest that the proposed Conde achieves significantly better recommendation performance than the existing state-of-the-art solutions.
Age Inference Using A Hierarchical Attention Neural Network
Authors: Yaguang Liu, Lisa Singh
Venue: CIKM
Year: 2021

While demographic attributes, such as age, gender, and location, have been extensively studied, most previous studies usually combine different sources of data, such as the user's biography, pictures, posts, and the user's network to obtain reasonable inference accuracies. However, it is not always practical to collect all those different forms of data. Therefore, in this paper, we consider methods for inferring age that only use Twitter posts (tweet text and emojis). We propose a hierarchical attention neural model that integrates independent linguistic knowledge gained from text and emojis when making a prediction. This hierarchical model is able to capture the intra-post relationship between these different post components, as well as the inter-post relationships of a user's posts. Our empirical evaluation using a data set generated from Wikidata demonstrates that our model achieves better performance than the state-of-the-art models, and still performs well when the number of posts per user is reduced in the training data set.
Learning Representations of Inactive Users: A Cross Domain Approach with Graph Neural Networks
Authors: Ziqi Liu, Yue Shen, Xiaocheng Cheng, Qiang Li, Jianping Wei, Zhiqiang Zhang, Dong Wang, Xiaodong Zeng, Jinjie Gu, Jun Zhou
Venue: CIKM
Year: 2021

Understanding inactive users is the key to user growth and engagement for many Internet companies. However, learning inactive users' representations and their preferences is still challenging because the features available are missing and the positive responses or labels are insufficient. In this paper, we propose a cross domain learning approach to exclusively recommend customized items to inactive users by leveraging the knowledge of active users. Particularly, we represent users, no matter active or inactive users, by their friends' browsing behaviors based on a graph neural network (GNN) layer atop of a heterogeneous graph defined on social networks (user-user friendships) and browsing behaviors (user-page clicks). We jointly optimize the learning tasks of active users in source domain and inactive users in target domain based on the domain invariant features extracted from the embedding of our GNN layer, where the domain invariant features that are learned to benefit both tasks on active/inactive users, and are indiscriminate with respect to the shift between the domains. Extensive experiments show that our approach can well capture the preference of inactive users using both public data and real-world data at Alipay.
Meta Hyperparameter Optimization with Adversarial Proxy Subsets Sampling
Authors: Yue Liu, Xin Wang, Xue Xu, Jianbo Yang, Wenwu Zhu
Venue: CIKM
Year: 2021

Hyperparameter optimization (HPO), aiming at automatically searching optimal hyperparameter configurations, has attracted increasing attention in the machine learning community. HPO generally suffers from high searching costs when dealing with large-scale real-world datasets since training the model with a certain hyperparameter configuration is time-consuming. Existing works suggest sampling subsets uniformly to represent the full dataset for HPO but ignoring the complex and dynamic distribution in real-world scenarios and the exploration of hyperparameter transfer. To tackle this problem, we propose a novel meta hyperparameter optimization model with an adversarial proxy subsets sampling strategy (Meta-HPO), which can transfer hyperparameters optimized on the sampled proxy subsets to the full dataset and further adapt to the new data in an out-of-sample updating manner. In particular, a perturbation-aware adversarial sampling strategy is designed to select the proxy subsets that significantly influence the model performance. With the searched hyperparameter configurations and corresponding performance scores on the proxy subsets, we propose a meta transfer framework, named "hp-learner'', to build the connection between the distribution of dataset and the optimal hyperparameter configuration. Our Meta-HPO provides a flexible and efficient hyperparameter optimization algorithm. Extensive experiments on real-world datasets validate the advantages of our proposed Meta-HPO model against existing state-of-the-art benchmarks.
State-Aware Meta-Evaluation of Evaluation Metrics in Interactive Information Retrieval
Authors: Jiqun Liu, Ran Yu
Venue: CIKM
Year: 2021

In interactive IR (IIR), users often seek to achieve different goals (e.g. exploring a new topic, finding a specific known item) at different search iterations and thus may evaluate system performances differently. Without state-aware approach, it would be extremely difficult to simulate and achieve real-time adaptive search evaluation and recommendation. To address this gap, our work identifies users' task states from interactive search sessions and meta-evaluates a series of online and offline evaluation metrics under varying states based on a user study dataset consisting of 1548 unique query segments from 450 search sessions. Our results indicate that: 1) users' individual task states can be identified and predicted from search behaviors and implicit feedback; 2) the effectiveness of mainstream evaluation measures (measured based upon their respective correlations with user satisfaction) vary significantly across task states. This study demonstrates the implicit heterogeneity in user-oriented IR evaluation and connects studies on complex search tasks with evaluation techniques. It also informs future research on the design of state-specific, adaptive user models and evaluation metrics.
BNN: Boosting Neural Network Framework Utilizing Limited Amount of Data
Authors: Amit Livne, Roy Dor, Bracha Shapira, Lior Rokach
Venue: CIKM
Year: 2021

Deep learning (DL) algorithms have played a major role in achieving state-of-the-art (SOTA) performance in various learning applications, including computer vision, natural language processing, and recommendation systems (RSs). However, these methods are based on a vast amount of data and do not perform as well when there is a limited amount of data available. Moreover, some of these applications (e.g., RSs) suffer from other issues such as data sparsity and the cold-start problem. While recent research on RSs used DL models based on side information (SI) (e.g., product reviews, film plots, etc.) to tackle these challenges, we propose boosting neural network (BNN), a new DL framework for capturing complex patterns, which requires just a limited amount of data. Unlike conventional boosting, BNN does not sum the predictions generated by its components. Instead, it uses these predictions as new SI features which enhances accuracy. Our framework can be utilized for many problems, including classification, regression, and ranking. In this paper, we demonstrate BNN's use for addressing a classification task. Comprehensive experiments conducted to illustrate BNN's effectiveness on three real-world datasets demonstrated its ability to outperform existing SOTA models for classification tasks (e.g., clickthrough rate prediction).
Mining Software Entities in Scientific Literature: Document-level NER for an Extremely Imbalance and Large-scale Task
Authors: Patrice Lopez, Caifan Du, Johanna Cohoon, Karthik Ram, James Howison
Venue: CIKM
Year: 2021

We present a comprehensive information extraction system dedicated to software entities in scientific literature. This task combines the complexity of automatic reading of scientific documents (PDF processing, document structuring, styled/rich text, scaling) with challenges specific to mining software entities: high heterogeneity and extreme sparsity of mentions, document-level cross-references, disambiguation of noisy software mentions and poor portability of Machine Learning approaches between highly specialized domains. While NER is a key component to recognize new and unseen software, considering this task as a simple NER application fails to address most of these issues. In this paper, we propose a multi-model Machine Learning approach where raw documents are ingested by a cascade of document structuring processes applied not to text, but to layout token elements. The cascading process further enriches the relevant structures of the document with a Deep Learning software mention recognizer adapted to the high sparsity of mentions. The Machine Learning cascade culminates with entity disambiguation to alleviate false positives and to provide software entity linking. A bibliographical reference resolution is integrated to the process for attaching references cited alongside the software mentions. Based on the first gold-standard annotated dataset developed for software mentions, this work establishes a new reference end-to-end performance for this task. Experiments with the CORD-19 publications have further demonstrated that our system provides practically usable performance and is scalable to the whole scientific corpus, enabling novel applications for crediting research software and for better understanding the impact of software in science.
Smoothing with Fake Label
Authors: Ziyang Luo, Yadong Xi, Xiaoxi Mao
Venue: CIKM
Year: 2021

Label Smoothing is a widely used technique in many areas. It can prevent the network from being over-confident. However, it hypotheses that the prior distribution of all classes is uniform. Here, we decide to abandon this hypothesis and propose a new smoothing method, called Smoothing with Fake Label. It shares a part of the prediction probability to a new fake class. Our experiment results show that the method can increase the performance of the models on most tasks and outperform the Label Smoothing on text classification and cross-lingual transfer tasks.
IMAS++: An Intelligent Medical Analysis System Enhanced with Deep Graph Neural Networks
Authors: Feng Luo, Yue Zhang, Xiaoli Wang
Venue: CIKM
Year: 2021

This paper demonstrates an intelligent medical analysis system. We aim to address two main challenges: 1) medical data often contain heterogeneous information which are usually valuable but difficult to be modeled; 2) medical data are often lacking of large scale labeled data which usually require huge efforts to build. To resolve the first challenge, we propose a novel multi-modal heterogeneous graph model to represent the medical data. Based on this model, graph neural networks can be directly applied to effective medical case clustering. This helps to resolve the second challenge for label assignment in the same cluster. To further evaluate the practical use of the proposed model, the system also proposes an effective similar medical case retrieval framework based on a novel graph similarity learning model. We have implemented the system and the source codes are published at https://github.com/emmali808/ADDS. With our system, users can easily pinpoint valuable historical medical information they are interested in and obtain closely relevant medical cases for further diagnosis.
LEReg: Empower Graph Neural Networks with Local Energy Regularization
Authors: Xiaojun Ma, Hanyue Chen, Guojie Song
Venue: CIKM
Year: 2021

Researches on analyzing graphs with Graph Neural Networks (GNNs) have been receiving more and more attention because of the great expressive power of graphs. GNNs map the adjacency matrix and node features to node representations by message passing through edges on each convolution layer. However, the message passed through GNNs is not always beneficial for all parts in a graph. Specifically, as the data distribution is different over the graph, the receptive field (the farthest nodes that a node can obtain information from) needed to gather information is also different. Existing GNNs treat all parts of the graph uniformly, which makes it difficult to adaptively pass the most informative message for each unique part. To solve this problem, we propose two regularization terms that consider message passing locally: (1) Intra-Energy Reg and (2) Inter-Energy Reg. Through experiments and theoretical discussion, we first show that the speed of smoothing of different parts varies enormously and the topology of each part affects the way of smoothing. With Intra-Energy Reg, we strengthen the message passing within each part, which is beneficial for getting more useful information. With Inter-Energy Reg, we improve the ability of GNNs to distinguish different nodes. With the proposed two regularization terms, GNNs are able to filter the most useful information adaptively, learn more robustly and gain higher expressiveness. Moreover, the proposed LEReg can be easily applied to other GNN models with plug-and-play characteristics. Extensive experiments on several benchmarks verify that GNNs with LEReg outperform or match the state-of-the-art methods. The effectiveness and efficiency are also empirically visualized with elaborate experiments.
Learning Sparse Binary Code for Maximum Inner Product Search
Authors: Changyi Ma, Fangchen Yu, Yueyao Yu, Wenye Li
Venue: CIKM
Year: 2021

Maximum inner product search (MIPS), combined with the hashing method, has become a standard solution to similarity search problems. It often achieves an order of magnitude speedup over nearest neighbor search (NNS) under similar settings. Motivated by the work and achievements along this line, in this paper, we developed a sparse binary hashing method for MIPS to preserve the pairwise similarities with the support of two asymmetric hash functions. We proposed a simple and efficient algorithm that learns two hash functions for the query database and the search database respectively. We conducted experiments to evaluate the proposed method, relying on image retrieval tasks on four benchmark datasets. The empirical results clearly demonstrated the algorithm's promising potential on practical applications in terms of search accuracy and scalability.
IR From Bag-of-words to BERT and Beyond through Practical Experiments
Authors: Craig Macdonald, Nicola Tonellotto, Sean MacAvaney
Venue: CIKM
Year: 2021

The task of adhoc search is undergoing a renaissance, sparked by advances in natural language processing. In particular, pre-trained contextualized language models (such as BERT and T5) have consistently shown to be a highly-effective foundation upon which to build ranking models. These models are equipped with a far deeper understanding of language than the capabilities of bag-of-words (BoW) models. Applying these techniques to new tasks can be tricky, however, as they require knowledge of deep learning frameworks, and significant scripting and data munging. In this full-day tutorial, we build up from foundational retrieval principles to the latest neural ranking techniques. We first provide foundational background on classical bag-of-words methods. We then show how feature-based Learning to Rank methods can be used to re-rank these results. Finally, we cover contemporary approaches, such as BERT, doc2query, and dense retrieval. Throughout the process, we demonstrate how these can be easily experimentally applied to new search tasks in a declarative style of conducting experiments exemplified by the PyTerrier and OpenNIR search toolkits. This tutorial is interactive in nature for participants. It is broken into sessions, each of which mixes explanatory presentation with hands-on activities using prepared Jupyter notebooks running on the Google Colab platform. These activities give participants experience applying the techniques covered in the tutorial on the TREC COVID benchmark test collection. The tutorial is broken into four sessions. In the first session, we cover foundational retrieval concepts, including inverted indexing, retrieval, and scoring. We also demonstrate how evaluation can be conducted in a declarative fashion within PyTerrier, encapsulating ideas such as significance testing, and multiple correction, as promoted as IR best practices. In the second session, we build upon the core retrieval concepts to demonstrate how to re-write queries (e.g., using RM3) and re-rank documents (e.g., using learning-to-rank). In the third session, we introduce contextualized language models, such as BERT and show how they can be utilized for document re-ranking (e.g, using Vanilla/monoBERT and EPIC). Finally, in session four, we move beyond re-ranking and cover how approaches that modify documents (e.g., DeepCT) as well as efforts to replace the traditional inverted index with an embedding-based index (e.g., ANCE, ColBERT, and ColBERT-PRF). By the end of the tutorial, participants will have experience conducting IR experiments from classical bag-of-words models to contemporary BERT models and beyond.
Generating Compositional Color Representations from Text
Authors: Paridhi Maheshwari, Nihal Jain, Praneetha Vaddamanu, Dhananjay Raut, Shraiysh Vaishay, Vishwa Vinay
Venue: CIKM
Year: 2021

We consider the cross-modal task of producing color representations for text phrases. Motivated by the fact that a significant fraction of user queries on an image search engine follow an (attribute, object) structure, we propose a generative adversarial network that generates color profiles for such bigrams. We design our pipeline to learn composition - the ability to combine seen attributes and objects to unseen pairs. We propose a novel dataset curation pipeline from existing public sources. We describe how a set of phrases of interest can be compiled using a graph propagation technique, and then mapped to images. While this dataset is specialized for our investigations on color, the method can be extended to other visual dimensions where composition is of interest. We provide detailed ablation studies that test the behavior of our GAN architecture with loss functions from the contrastive learning literature. We show that the generative model achieves lower Frechet Inception Distance than discriminative ones, and therefore predicts color profiles that better match those from real images. Finally, we demonstrate improved performance in image retrieval and classification, indicating the crucial role that color plays in these downstream tasks.
GDFM: Gene Vectors Embodied Deep Attentional Factorization Machines for Interaction prediction
Authors: Sameen Mansha, Tayyab Khalid, Faisal Kamiran, Masroor Hussain, Syed Fawad Hussain, Hongzhi Yin
Venue: CIKM
Year: 2021

Gene Network Graphs (GNGs) are comprised of biomedical data. Deriving structural information from these graphs remains a prime area of research in the domain of biomedical and health informatics. In this paper, we propose Gene Vectors Embodied Deep Attentional Factorization Machines (GDFMs) for the gene to gene interaction prediction. We first initialize GDFM with vector embeddings learned from gene locality configuration and an expression equivalence criterion that preserves their innate similar traits. GDFM uses an attention-based mechanism that manipulates different positions, to learn the representation of sequence, before calculating the pairwise factorized interactions. We further use hidden layers, batch normalization, and dropout to stabilize the performance of our deep structured architecture. An extensive comparison with several state-of-the-art approaches, using Ecoli and Yeast datasets for gene-gene interaction prediction shows the significance of our proposed framework.