OpenRaiser/Intern-Atlas huggingface dataset:
Decoupled Hyperbolic Graph Attention Network for Modeling Substitutable and Complementary Item Relationships
Authors: Zhiheng Zhou, Tao Wang, Linfang Hou, Xinyuan Zhou, Mian Ma, Zhuoye Ding
Venue: CIKM
Year: 2022

Modeling substitutable and complementary item relationships is a fundamental and important topic for recommendation in e-commerce online scenarios. In the real world, item relationships are usually coupled, heterogeneous and they also have abundant side information and hierarchical data structures. Recently, to take full advantage of both sides information and topological structure, graph neural networks are widely explored in relationship modeling. However, the existing methods are crude in decoupling heterogeneous relationships. Their model designs lack deep insight of relationships' coupling mode, i.e. neglects the prior knowledge of how relationships affect each other. In addition, many existing graph methods, regardless of how they handle coupled relationships, are deployed in Euclidean spaces, which distorts hierarchical data structure and limits the expressive power due to the non power law characteristic of Euclidean topology. In this paper, we propose a novel Decoupled Hyperbolic Graph Attention Network (DHGAN). The innovations of our DHGAN can be highlighted as two aspects. Firstly, we design metapaths in an adequate way following an algebraic perspective of relationships coupling mode, which helps achieving better model interpretability. Secondly, DHGAN maps heterogeneous relationships into separate hyperbolic spaces, which can better capture the hierarchical information of graph nodes and helps improving model's representational capacity. We conduct extensive experiments on three public real-world datasets, demonstrating DHGAN is superior to the state-of-the-art graph baselines. We release the codes at https://github.com/wt-tju/DHGAN.
Multi-task Learning with Adaptive Global Temporal Structure for Predicting Alzheimer's Disease Progression
Authors: Menghui Zhou, Yu Zhang, Tong Liu, Yun Yang, Po Yang
Venue: CIKM
Year: 2022

In this paper, we propose a multi-task learning approach for predicting the progression of Alzheimer's disease (AD), known as the most common form of dementia. The vital challenge is to identify how the tasks are related and build learning models to capture such task relatedness. Unlike previous methods that assume low-rank structure, chase the predefined local temporal relatedness or utilize local approximation, we propose a novel penalty termed L ongitudinal S tability A djustment (LSA) to adaptively capture the intrinsic global temporal correlation among multiple time points and thus utilize the accumulated disease progression information. We combine LSA with sparse group Lasso to present a novel multi-task learning formulation to identify biomarkers closely related to cognitive measurement and predict AD progression. Two efficient algorithms are designed for large-scale dataset. Experimental results conducted on two AD data sets demonstrate our framework outperforms competing methods in terms of overall and each task performances. We also perform stability selection to identify stable biomarkers from the MRI feature set and analyze their temporal patterns in disease progression.
Dialogue State Tracking Based on Hierarchical Slot Attention and Contrastive Learning
Authors: Yihao Zhou, Guoshuai Zhao, Xueming Qian
Venue: CIKM
Year: 2022

Dialogue state is a key information in traditional task-oriented dialogue systems, which represents the user's dialogue intention at each moment through a set of (slot, value). The recent methods model the slot and the dialogue context to keep track of the state, but there is a lack of refinement of context information. They do not consider the influence of dialogue context in different scenarios. Our proposed approach utilizes a fine-grained representation of each slot at multiple levels and incorporates an interaction mechanism to obtain a weight of past memory, present utterance and relevance of the slots. Besides, to address the problem that the dialogue utterance is semantically distant from the corresponding slot value, we introduce the contrastive learning to make the utterance embedding mapped under each slot name more suitable with the ground truth value and away from other slot values. This improves the accuracy of mapping between feature space and semantic space. In the predefined ontology-based approaches, our model achieves leading results with both MultiWOZ2.0 and MultiWOZ2.1 datasets.
ScheRe: Schema Reshaping for Enhancing Knowledge Graph Construction
Authors: Dongzhuoran Zhou, Baifan Zhou, Zhuoxun Zheng, Ahmet Soylu, Ognjen Savkovic, Egor V. Kostylev, Evgeny Kharlamov
Venue: CIKM
Year: 2022

Automatic knowledge graph (KG) construction is widely used for e.g. data integration, question answering and semantic search. There are many approaches of automatic KG construction. Among which, an important approach is to map the raw data to a given domain KG schema, e.g., domain ontology or conceptual graph, and construct the entities and properties according to the domain KG schema. However, the existing approaches to construct KGs are not always efficient enough and the resulting KGs are not sufficiently application and user-friendly. The main challenge arises from the trade-off: the domain KG schema should be domain-generic and knowledge-oriented, to reflect the general domain knowledge rather than data particularities; while a KG schema should be data-oriented, to cover all data features. If the former is directly used for KG construction, this can cause issues like a high load of blank nodes, which are technical nodes in the KGs that represent unknown entities. To this end, we propose our ScheRe system in the demo, which relies on a schema reshaping algorithm and other two semantic modules for enhancing KG construction. The demo attendees will use ScheRe to reshape a domain KG schema to data specific KG schema, build KGs with industrial data, and experience more user-friendly querying.
SASNet: Stage-aware Sequential Matching for Online Travel Recommendation
Authors: Fanwei Zhu, Zulong Chen, Fan Zhang, Jiazhen Lou, Hong Wen, Shui Liu, Qi Rao, Tengfei Yuan, Shenghua Ni, Jinxin Hu, Fuzhen Sun, Quan Lu
Venue: CIKM
Year: 2022

Sequential matching, which aims to predict the item a user will next interact with in the sequential context of the user's historical behaviors, is widely adopted in recommender systems. Existing works mainly characterize the sequential context as the dependencies of user interactions, which is less effective for online travel recommendation where users' behaviors are highly correlated with theirstages in the travel life cycle. Specifically, users on an online travel platform (OTP) usually go through different stages (e.g., exploring a destination, planning an itinerary), and make several correlated interactions (e.g., booking a flight, reserving a hotel, renting a car) at each stage. In this paper, we propose to capture the deep sequential context by modeling the evolving of user stages, and develop a novel stage-aware deep sequential matching network (SASNet) that incorporates inter-stage and intra-stage dependencies over stage-augmented interaction sequence for more accurate and interpretable recommendation. Extensive experiments on real-world datasets validate the superiority of our model for both online travel recommendation and general next-item recommendation. Our model has been successfully deployed at Fliggy, one of the most popular OTPs in China, and shows good performance in serving online traffic.
Spherical Graph Embedding for Item Retrieval in Recommendation System
Authors: Wenqiao Zhu, Yesheng Xu, Xin Huang, Qiyang Min, Xun Zhou
Venue: CIKM
Year: 2022

One of the challenging problems in large-scale recommendation systems is to retrieve relevant candidates accurately and efficiently. Graph-based retrievals have been widely deployed in industrial recommendation systems. Previous graph-based methods depend on integrated graph infrastructures because of inherent data dependency in graph learning. However, it could be expensive to develop a graph infrastructure. In this paper, we present a simple and effective graph-based retrieval method, which does not need any graph infrastructures. We conduct extensive offline evaluations and online tests in a real-world recommendation system. The results show that the proposed method outperforms the existing methods. The source code of our algorithm is available online.
Breast Cancer Early Detection with Time Series Classification
Authors: Haoren Zhu, Pengfei Zhao, Yiu-Pong Chan, Hong Kang, Dik Lun Lee
Venue: CIKM
Year: 2022

Breast cancer has become the leading cause of women cancer death worldwide. Despite the consensus that breast cancer early detection can significantly reduce treatment difficulty and cancer mortality, people still are reluctant to go to hospital for regular checkups due to the high costs incurred. A timely, private, affordable, and effective household breast cancer early detection solution is badly needed. In this paper, we propose a household solution that utilizes pairs of sensors embedded in the bra to measure the thermal and moisture time series data (BTMTSD) of the breast surface and conduct time series classification (TSC) to diagnose breast cancer. Three main challenges are encountered when doing BTMTSD classification, (1) small supervised dataset, which is a common limitation of medical research, (2) noisy time series with unique noise patterns, and (3) complex interplay patterns across multiple time series dimensions. To mitigate these problems, we incorporate multiple data augmentation and transformation techniques with various deep learning TSC approaches and compare their performances for the BTMTSD classification task. Experimental results validate the effectiveness of our framework in providing reliable breast cancer early detection.
SEERa: A Framework for Community Prediction
Authors: Soroush Ziaeinejad, Saeed Samet, Hossein Fani
Venue: CIKM
Year: 2022

Online user communities exhibit distinct temporal dynamics in response to popular topics or breaking events. Despite abundant community detection libraries, there is yet to be one that provides access to the possible user communities in future time intervals. To bridge this gap, we contribute SEERa, an open-source end-to-end community prediction framework to identify future user communities in a text streaming social network. SEERa incorporates state-of-the-art temporal graph neural networks to model inter-user topical affinities at each time interval via streams of temporal graphs. This all takes place while users' topics of interest and hence their inter-user topical affinities are changing over time. SEERa predicts yet-to-be-seen user communities on the final positions of users' vectors in the latent space. Notably, our framework serves as a one-stop-shop to future user communities for Social Information Retrieval and Social Recommendation systems.
Interpretable Natural Language Understanding
Authors: Yulan He
Venue: CIKM
Year: 2023

In recent years, we have witnessed the shift of paradigms in Natural Language Processing (NLP) from fine-tuning large-scale pre-trained language models (PLMs) on task-specific data to prompt-based learning. In the latter, the task description is embedded into the PLM input, enabling the same model to handle multiple tasks. While both approaches have demonstrated impressive performance in various NLP tasks, their opaque nature makes comprehending their inner workings and decision-making processes challenging for humans. In this talk, I will share the research undertaken in my group to address the interpretability concerns surrounding neural models in language understanding. This includes a hierarchical interpretable text classifier going beyond word-level interpretations, uncertainty interpretation of text classifiers built on PLMs, explainable recommender systems by harnessing information across diverse modalities, and explainable student answer scoring. I will conclude my talk by offering insights into potential future developments in interpretable language understanding.
FCT-GAN: Enhancing Global Correlation of Table Synthesis via Fourier Transform
Authors: Zilong Zhao, Robert Birke, Lydia Y. Chen
Venue: CIKM
Year: 2023

An alternative method for sharing knowledge while complying with strict data access regulations, such as the European General Data Protection Regulation (GDPR), is the emergence of synthetic tabular data. Mainstream table synthesizers utilize methodologies derived from Generative Adversarial Networks (GAN). Although several state-of-the-art (SOTA) tabular GAN algorithms inherit Convolutional Neural Network (CNN)-based architectures, which have proven effective for images, they tend to overlook two critical properties of tabular data: (i) the global correlation across columns, and (ii) the semantic invariance to the column order. Permuting columns in a table does not alter the semantic meaning of the data, but features extracted by CNNs can change significantly due to their limited convolution filter kernel size. To address the above problems, we propose FCT-GAN the first conditional tabular GAN to adopt Fourier networks into table synthesis. FCT-GAN enhances permutation invariant GAN training by strengthening the learning of global correlations via Fourier layers. Extensive evaluation on benchmarks and real-world datasets show that FCT-GAN can synthesize tabular data with better (up to 27.8%) machine learning utility (i.e. a proxy of global correlations) and higher (up to 26.5%) statistical similarity to real data. FCT-GAN also has the least variation on synthetic data quality among 7 SOTA baselines on 3 different training-data column orders.
GranCATs: Cross-Lingual Enhancement through Granularity-Specific Contrastive Adapters
Authors: Meizhen Liu, Jiakai He, Xu Guo, Jianye Chen, Siu Cheung Hui, Fengyu Zhou
Venue: CIKM
Year: 2023

Multilingual language models (MLLMs) have demonstrated remarkable success in various cross-lingual downstream tasks, facilitating the transfer of knowledge across numerous languages, whereas this transfer is not universally effective. Our study reveals that while existing MLLMs like mBERT can capturephrase-level alignments across the language families, they struggle to effectively capturesentence-level andparagraph-level alignments. To address this limitation, we propose GranCATs, Granularity-specific Contrastive AdapTers. We collect a new dataset that observes each sample at three distinct levels of granularity and employ contrastive learning as a pre-training task to train GranCATs on this dataset. Our objective is to enhance MLLMs' adaptation to a broader range of cross-lingual tasks by equipping them with improved capabilities to capture global information at different levels of granularity. Extensive experiments show that MLLMs with GranCATs yield significant performance advancements across various language tasks with different text granularities, including entity alignment, relation extraction, sentence classification and retrieval, and question-answering. These results validate the effectiveness of our proposed GranCATs in enhancing cross-lingual alignments across various text granularities and effectively transferring this knowledge to downstream tasks.
Exploring Low-Dimensional Manifolds of Deep Neural Network Parameters for Improved Model Optimization
Authors: Ke Lu, Xiaotong He, Ze Qin, Xinyao Li, Zhekai Du
Venue: CIKM
Year: 2023

Manifold learning techniques have significantly enhanced the comprehension of massive data by exploring the geometric properties of the data manifold in low-dimensional subspaces. However, existing research on manifold learning primarily focuses on understanding the intricate data, overlooking the explosive growth of the scale and complexity of deep neural networks (DNNs), which presents a significant challenge for model optimization. In this work, we propose to explore the intrinsic low-dimensional manifold of network parameters for efficient model optimization. Specifically, we analyze parameter distributions in a deep model and perform sampling to map them onto a low-dimensional parameter manifold using the local tangent space alignment (LTSA). Since our focus is on studying parameter manifolds to guide model optimization, we therefore select dynamic optimal training trajectories for sampling and approximate tangent spaces to obtain low-dimensional representations of DNNs. By applying manifold learning techniques and employing a two-step alternate optimization method, we achieve a fixed subspace that reduces training time and resource costs for commonly used deep networks. The trained low-dimensional network can be mapped back to the original parameter space for further use. We demonstrate the benefits of learning low-dimensional parameterization of DNNs on both noisy label learning and federated learning tasks. Extensive experimental results on various benchmarks show the effectiveness of our method concerning both superior accuracy and reduced resource consumption.
How Discriminative Are Your Qrels? How To Study the Statistical Significance of Document Adjudication Methods
Authors: David Otero, Javier Parapar, Nicola Ferro
Venue: CIKM
Year: 2023

Creating test collections for offline retrieval evaluation requires human effort to judge documents' relevance. This expensive activity motivated much work in developing methods for constructing benchmarks with fewer assessment costs. In this respect, adjudication methods actively decide both which documents and the order in which experts review them, in order to better exploit the assessment budget or to lower it. Researchers evaluate the quality of those methods by measuring the correlation between the known gold ranking of systems under the full collection and the observed ranking of systems under the lower-cost one. This traditional analysis ignores whether and how the low-cost judgements impact on the statistically significant differences among systems with respect to the full collection. We fill this void by proposing a novel methodology to evaluate how the low-cost adjudication methods preserve the pairwise significant differences between systems as the full collection. In other terms, while traditional approaches look for stability in answering the question "is system A better than system B?", our proposed approach looks for stability in answering the question "is system A significantly better than system B?", which is the ultimate questions researchers need to answer to guarantee the generalisability of their results. Among other results, we found that the best methods in terms of ranking of systems correlation do not always match those preserving statistical significance.
MORPHER: Structural Transformation of Ill-formed Rows
Authors: Mazhar Hameed, Gerardo Vitagliano, Felix Naumann
Venue: CIKM
Year: 2023

Open data portals contain a plethora of data files, with comma-separated value (CSV) files being particularly popular with users and businesses due to their flexible standard. However, this flexibility comes with much responsibility for data consumers, as many files contain various structural problems, e.g., a different number of cells across data rows, multiple value formats within the same column, different variants of quoted fields due to user specifications, etc. We refer to rows that contain such structural inconsistencies as ill-formed. Consequently, ingesting them into a host system, such as a database or an analytics platform, often requires prior data preparation steps. We propose to demonstrate MORPHER, a desktop-based system that incorporates our state-of-the-art error detection system, SURAGH and extends it to also clean the files at hand. MORPHER facilitates ingesting CSV files by automatically identifying and cleaning ill-formed rows while preserving all data. It comprises three key components: 1) The pattern modeler, which generates syntax-based patterns for each row of the input file. The system uses these patterns to classify rows into ill-formed and well-formed. 2) The pattern classifier obtains row patterns for ill-formed rows and uses them to distinguish ill-formed but wanted rows from ill-formed unwanted rows. 3) The pattern wrangler transforms the identified wanted rows into well-formed rows, effectively repairing a wide range of formatting problems.
HEProto: A Hierarchical Enhancing ProtoNet based on Multi-Task Learning for Few-shot Named Entity Recognition
Authors: Wei Chen, Lili Zhao, Pengfei Luo, Tong Xu, Yi Zheng, Enhong Chen
Venue: CIKM
Year: 2023

Few-shot Named Entity Recognition (NER) task, which aims to identify and classify entities from different domains with limited training samples, has long been treated as a basic step for knowledge graph (KG) construction. Great efforts have been made on this task with competitive performance, however, they usually treat the two subtasks, namely span detection and type classification, as mutually independent, and the integrity and correlation between subtasks have been largely ignored. Moreover, prior arts may fail to absorb the coarse-grained features of entities, resulting in a semantic-insufficient representation of entity types. To that end, in this paper, we propose a Hierarchical Enhancing ProtoNet (HEProto) based on multi-task learning, which is utilized to jointly learn these two subtasks and model their correlation. Specifically, we adopt contrastive learning to enhance the span boundary information and the type semantic representations in these two subtasks. Then, the hierarchical prototypical network is designed to leverage the coarse-grained information of entities in the type classification stage, which could help the model to better learn the fine-grained semantic representations. Along this line, we construct a similarity margin loss to reduce the similarity between fine-grained entities and other irrelevant coarse-grained prototypes. Finally, extensive experiments on the Few-NERD dataset prove that our solution outperforms competitive baseline methods. The source code of HEProto is available at \hrefhttps://github.com/fanshu6hao/HEProto https://github.com/fanshu6hao/HEProto.
Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and Placement
Authors: Hang Dong, Jiaoyan Chen, Yuan He, Ian Horrocks
Venue: CIKM
Year: 2023

Mentions of new concepts appear regularly in texts and require automated approaches to harvest and place them into Knowledge Bases (KB), e.g., ontologies and taxonomies. Existing datasets suffer from three issues, (i) mostly assuming that a new concept is pre-discovered and cannot support out-of-KB mention discovery; (ii) only using the concept label as the input along with the KB and thus lacking the contexts of a concept label; and (iii) mostly focusing on concept placement w.r.t a taxonomy of atomic concepts, instead of complex concepts, i.e., with logical operators. To address these issues, we propose a new benchmark, adapting MedMentions dataset (PubMed abstracts) with SNOMED CT versions in 2014 and 2017 under the Diseases sub-category and the broader categories of Clinical finding, Procedure, and Pharmaceutical / biologic product. We provide usage on the evaluation with the dataset for out-of-KB mention discovery and concept placement, adapting recent Large Language Model based methods.