arXiv subjects

Xiaochun Yang

Publications and source records attributed to Xiaochun Yang.

16 recordsLinked to original sources

arXiv2026

Enhancing Event Candidate Acquisition for Event Linking

Event linking associates event mentions in text with entries in a knowledge base (KB), or identifies them as out-of-KB events. Although existing methods use different architectures, candidate event acquisition can still be weakened by short ambiguous mentions, noisy arguments, and evidence that is unevenly useful for retrieval. We present MACE, a Multi-Agent Candidate Event acquisition method that refines event structure before linking. MACE uses evidence-specialized LLM agents to acquire time, location, participant, and event-type evidence, exposes intermediate queries to candidate-event lookup tools, and lets a coordinator revise the evidence set before final candidate construction. Experiments on two event linking benchmarks show that adding MACE to different event linking models consistently improves accuracy. These results show that MACE improves event linking through better candidate event acquisition without modifying the event linking model.

cs.AI↗

arXiv2026

LLM-Based Knowledge Graph Completion Combining Discrete Structural Coding with Similar Entity Information

Knowledge graph completion requires models to use both textual descriptions and relational structure. Existing LLM-based methods either encode KG structure as discrete tokens or refine a restricted set of candidate entities, and these two directions have largely been studied separately. We propose CoSC for LLM-based KGC, which combines discrete structural coding with similar entity information. Specifically, an LLM generates an initial candidate entity ranking from discrete structural codes, after which information from entities with structures similar to that of the query entity refines the ranking. Experiments on FB15k-237 show that CoSC outperforms existing baselines on MRR and Hits@10 while remaining competitive on Hits@1.

cs.AI↗

arXiv2026

Enhancing LLMs in Predictive Political QA with Semi-Structured Data

Predictive political question answering (QA), such as predicting how a political actor will vote, goes beyond factual lookup. External political resources offer rich historical evidence, but rarely contain the answer itself. Existing LLM augmentation methods, including actor-profile-based simulation and knowledge graph evidence injection, improve political reasoning but largely treat external resources as knowledge-based evidence, leaving prediction-relevant signals under-modeled. We identify two complementary signals for predictive political QA: actor stances that capture issue-specific preferences, and high-order structure signals that capture indirect dependencies among political actors. We propose PSL, a dual-view framework that converts semi-structured political records into inference-oriented evidence for LLMs. PSL extracts stance signals from question-relevant actor records in a semantic view, and learns structure-aware actor representations from an actor interaction graph in a vector view. Across three real-world datasets and multiple LLMs, PSL consistently outperforms baselines, with ablations confirming the complementary gains of stance and structure signals.

cs.AI↗

arXiv2026

Adapting Knowledge Graphs for Behavior Denoising in Sequential Recommendation

Sequential recommendation predicts the next item from a user's interaction history, but not every interaction is equally informative. Real logs combine persistent preferences with temporary needs, exploration, and incidental behavior, so some interactions can distort history representations or provide unreliable supervision. Existing denoising methods judge such interactions mainly from co-occurrence, order, or model predictions, without explicit evidence from relations between items. Knowledge graphs (KGs) offer this evidence, but item popularity, graph degree, uneven coverage, and widely shared entities can inflate connectivity and bias reliability estimates. Here we present AdaptedKG, which derives calibrated KG evidence for each training example without adding graph representations to the recommendation model. It first compares the observed context with structurally matched alternatives to identify relational paths that are unusually prominent and uses them to build a local KG view. It then compares each interaction with structurally matched reference items to calibrate its support within that view. The resulting retention coefficients gate historical representations and reweight target losses. All sample-specific scores are computed offline using training interactions and a fixed KG, so the backbone remains unchanged and no KG access is required at inference. Experiments show gains with a standard sequential recommender and multiple behavior-denoising sequential recommenders.

cs.IR↗

arXiv2026

OntoTKGE: Ontology-Enhanced Temporal Knowledge Graph Extrapolation

Temporal knowledge graph (TKG) extrapolation is an important task that aims to predict future facts through historical interaction information within KG snapshots. A key challenge for most existing TKG extrapolation models is handling entities with sparse historical interaction. The ontological knowledge is beneficial for alleviating this sparsity issue by enabling these entities to inherit behavioral patterns from other entities with the same concept, which is ignored by previous studies. In this paper, we propose a novel encoder-decoder framework OntoTKGE that leverages the ontological knowledge from the ontology-view KG (i.e., a KG modeling hierarchical relations among abstract concepts as well as the connections between concepts and entities) to guide the TKG extrapolation model's learning process through the effective integration of the ontological and temporal knowledge, thereby enhancing entity embeddings. OntoTKGE is flexible enough to adapt to many TKG extrapolation models. Extensive experiments on five data sets demonstrate that OntoTKGE not only significantly improves the performance of many TKG extrapolation models but also surpasses many state-of-the-art(SOTA) baseline methods.

cs.AI↗

arXiv2026

Continuous Query for Top-$K$ Maximal Sum Intervals over Streaming Data

The continuous identification of top-$k$ maximal sum intervals using a sliding window over a data stream is a critical operation for applications in IoT and beyond. A maximal sum interval is a non-overlapping, contiguous subsequence with the maximal sum in a sequence of signed values. Existing algorithms are ill-suited for streaming contexts: they either exhaustively enumerate all intervals even for small $k$ values, or depend on indexes that require frequent and costly restructuring. We propose a novel partition-based strategy. Our core insight is a partitioning scheme that guarantees that any maximal sum interval is fully contained within a single partition, enabling independent and parallel processing. This design provides two key advantages: it enables safe pruning of partitions that cannot contribute to top-$k$ results, drastically narrowing the search space, and it enables efficient, incremental maintenance of the maximal sum intervals in each partition. We develop algorithms for partition construction, incremental partition updates, and partition-based top-$k$ maximal sum interval search. Extensive experiments on real and synthetic datasets demonstrate that our approach significantly improves efficiency.

cs.DB↗

arXiv2026

From Single to Multiple Attributes: Experimental Insights on Sampling-Based Distinct Combination Estimation in GROUP-BY Queries

Estimating the number of distinct combinations in multi-attribute GROUP-BY queries remains a significant yet underexplored challenge. Current cardinality estimation techniques primarily focus on SPJ queries (i.e., selections, projections, and joins) and neglect GROUP-BY operations; meanwhile, distinct value estimation research has mainly targeted the single-attribute setting. Although sampling-based methods, including recent approaches with learned models, can theoretically support multi-attribute estimation, their practical effectiveness remains unclear. A comprehensive empirical evaluation is thus lacking to address whether joint distribution information from samples alone is sufficient for accurate multi-attribute estimation, whether existing methods fully exploit single-attribute information and can be further optimized, and whether filtered GROUP-BY queries can be accurately estimated. To this end, we propose a specialized workload generator for multi-attribute GROUP-BY queries and generate both filtered and non-filtered queries over four real-world datasets. By evaluating existing methods across synthetic workloads and the multi-table TPC-H benchmark, we analyze the sources of GROUP-BY cardinality estimation errors and their impact on PostgreSQL's plan selection, offering key recommendations for future estimator design.

cs.DB↗

arXiv2026

Boosting Knowledge Graph Foundation Models via Enhanced Negative Sampling

Knowledge graphs (KGs) have become the core backbone of numerous downstream tasks such as question answering and recommender systems. However, despite all this, KGs are often very incomplete. To perform zero-shot knowledge graph completion in unseen KGs, which have different relational vocabularies from those used for pre-training, KG foundation models (KGFMs) receive a wide range of attention. Existing KGFMs often perform training using random negative triples, which are constructed by replacing the head or tail entity of a positive triple with a random entity. However, these negative triples are often constructed with limited quality, providing weak supervision for KGFM training. In this paper, we propose a simple yet effective adaptive negative sampling approach, KMAS, to enhance existing KGFMs. KMAS constructs hard negative triples through the updated relation embeddings generated from the existing KGFM's relation encoder. To further adaptively align with the evolving capability of the KGFM during the training process, KMAS adjusts the ratio of hard negative triples dynamically throughout the whole training process: after a warmup phrase, it increases the ratio linearly and then decreases linearly. Extensive experiments are conducted over 44 data sets. Experimental results demonstrate that our proposed negative sampling method can enhance many SOTA KGFMs without requiring excessive additional time or memory consumption.

cs.AI↗

arXiv2026

Low-Latency Out-of-Core ANN Search in High-Dimensional Space

In-memory graph-based approximate nearest neighbor (ANN) search has superior search performance but incurs significant memory footprint. Disk-based methods reduce memory usage but suffer from high disk access latency. A common challenge is how to achieve low-latency search while significantly reducing memory footprint. In this paper, we propose SkipDisk, a disk-memory hybrid ANN search that significantly reduces memory footprint while achieving search latency comparable to or lower than in-memory method HNSW. By analyzing existing disk-based methods, we observed that disk access remains the primary bottleneck, and existing lower bound based filtering methods are two loose to effectively reduce disk access. Therefore, we design SkipDisk to achieve tight lower bound with low memory footprint to reduce the search latency. First, we design a dedicated pivot for each point to improve the lower bound of the triangle inequality for effective filtering. We further design an estimation-based approach based on this lower bound. Second, to reduce the memory footprint, we employ a three-level data pruning strategy to preserve informative data in memory. Third, to further reduce search latency, we design an asynchronous I/O strategy based on the decoupling of in-memory search and disk access by storing neighbor nodes in memory. Experiments show that our method achieves a latency of 85 of HNSW's latency with approximately 10 memory footprint, and a latency to 63 of HNSW's with a slightly higher memory footprint of around 20.

cs.DB↗

arXiv2026

Joint Knowledge Base Completion and Question Answering by Combining Large Language Models and Small Language Models

Knowledge Bases (KBs) play a key role in various applications. As two representative KB-related tasks, knowledge base completion (KBC) and knowledge base question answering (KBQA) are closely related and inherently complementary with each other. Thus, it will be beneficial to solve the task of joint KBC and KBQA to make them reinforce each other. However, existing studies usually rely on the small language model (SLM) to enhance them jointly, and the large language model (LLM)'s strong reasoning ability is ignored. In this paper, by combining the strengths of the LLM with the SLM, we propose a novel framework JCQL, which can make these two tasks enhance each other in an iterative manner. To make KBC enhance KBQA, we augment the LLM agent-based KBQA model's reasoning paths by incorporating an SLM-trained KBC model as an action of the agent, alleviating the LLM's hallucination and high computational costs issue in KBQA. To make KBQA enhance KBC, we incrementally fine-tune the KBC model by leveraging KBQA's reasoning paths as its supplementary training data, improving the ability of the SLM in KBC. Extensive experiments over two public benchmark data sets demonstrate that JCQL surpasses all baselines for both KBC and KBQA tasks.

cs.AI↗

arXiv2026

DiagLink: A Dual-User Diagnostic Assistance System by Synergizing Experts with LLMs and Knowledge Graphs

The global shortage and uneven distribution of medical expertise continue to hinder equitable access to accurate diagnostic care. While existing intelligent diagnostic system have shown promise, most struggle with dual-user interaction, and dynamic knowledge integration -- limiting their real-world applicability. In this study, we present DiagLink, a dual-user diagnostic assistance system that synergizes large language models (LLMs), knowledge graphs (KGs), and medical experts to support both patients and physicians. DiagLink uses guided dialogues to elicit patient histories, leverages LLMs and KGs for collaborative reasoning, and incorporates physician oversight for continuous knowledge validation and evolution. The system provides a role-adaptive interface, dynamically visualized history, and unified multi-source evidence to improve both trust and usability. We evaluate DiagLink through user study, use cases and expert interviews, demonstrating its effectiveness in improving user satisfaction and diagnostic efficiency, while offering insights for the design of future AI-assisted diagnostic systems.

cs.HC↗

arXiv2025

FLASH Viterbi: Fast and Adaptive Viterbi Decoding for Modern Data Systems

The Viterbi algorithm is a key operator for structured sequence inference in modern data systems, with applications in trajectory analysis, online recommendation, and speech recognition. As these workloads increasingly migrate to resource-constrained edge platforms, standard Viterbi decoding remains memory-intensive and computationally inflexible. Existing methods typically trade decoding time for space efficiency, but often incur significant runtime overhead and lack adaptability to various system constraints. This paper presents FLASH Viterbi, a Fast, Lightweight, Adaptive, and Hardware-Friendly Viterbi decoding operator that enhances adaptability and resource efficiency. FLASH Viterbi combines a non-recursive divide-and-conquer strategy with pruning and parallelization techniques to enhance both time and memory efficiency, making it well-suited for resource-constrained data systems. To further decouple space complexity from the hidden state space size, we present FLASH-BS Viterbi, a dynamic beam search variant built on a memory-efficient data structure. Both proposed algorithms exhibit strong adaptivity to diverse deployment scenarios by dynamically tuning internal parameters. To ensure practical deployment on edge devices, we also develop FPGA-based hardware accelerators for both algorithms, demonstrating high throughput and low resource usage. Extensive experiments show that our algorithms consistently outperform existing baselines in both decoding time and memory efficiency, while preserving adaptability and hardware-friendly characteristics essential for modern data systems. All codes are publicly available at https://github.com/Dzh-16/FLASH-Viterbi.

cs.DC↗

arXiv2025

Denoising, segmentation and volumetric rendering of optical coherence tomography angiography (OCTA) image using deep learning techniques: a review

Optical coherence tomography angiography (OCTA) is a non-invasive imaging technique widely used to study vascular structures and micro-circulation dynamics in the retina and choroid. OCTA has been widely used in clinics for diagnosing ocular disease and monitoring its progression, because OCTA is safer and faster than dye-based angiography while retaining the ability to characterize micro-scale structures. However, OCTA data contains many inherent noises from the devices and acquisition protocols and suffers from various types of artifacts, which impairs diagnostic accuracy and repeatability. Deep learning (DL) based imaging analysis models are able to automatically detect and remove artifacts and noises, and enhance the quality of image data. It is also a powerful tool for segmentation and identification of normal and pathological structures in the images. Thus, the value of OCTA imaging can be significantly enhanced by the DL-based approaches for interpreting and performing measurements and predictions on the OCTA data. In this study, we reviewed literature on the DL models for OCTA images in the latest five years. In particular, we focused on discussing the current problems in the OCTA data and the corresponding design principles of the DL models. We also reviewed the state-of-art DL models for 3D volumetric reconstruction of the vascular networks and pathological structures such as the edema and distorted optic disc. In addition, the publicly available dataset of OCTA images are summarized at the end of this review. Overall, this review can provide valuable insights for engineers to develop novel DL models by utilizing the characteristics of OCTA signals and images. The pros and cons of each DL methods and their applications discussed in this review can be helpful to assist technicians and clinicians to use proper DL models for fundamental research and disease screening.

eess.IV↗

arXiv2025

An Adaptive Cost-Sensitive Learning and Recursive Denoising Framework for Imbalanced SVM Classification

Category imbalance is one of the most popular and important issues in the domain of classification. Emotion classification model trained on imbalanced datasets easily leads to unreliable prediction. The traditional machine learning method tends to favor the majority class, which leads to the lack of minority class information in the model. Moreover, most existing models will produce abnormal sensitivity issues or performance degradation. We propose a robust learning algorithm based on adaptive cost-sensitivity and recursive denoising, which is a generalized framework and can be incorporated into most stochastic optimization algorithms. The proposed method uses the dynamic kernel distance optimization model between the sample and the decision boundary, which makes full use of the sample's prior information. In addition, we also put forward an effective method to filter noise, the main idea of which is to judge the noise by finding the nearest neighbors of the minority class. In order to evaluate the strength of the proposed method, we not only carry out experiments on standard datasets but also apply it to emotional classification problems with different imbalance rates (IR). Experimental results show that the proposed general framework is superior to traditional methods in Accuracy, G-mean, Recall and F1-score.

cs.CV↗

arXiv2023

From Large Language Models to Databases and Back: A discussion on research and education

This discussion was conducted at a recent panel at the 28th International Conference on Database Systems for Advanced Applications (DASFAA 2023), held April 17-20, 2023 in Tianjin, China. The title of the panel was "What does LLM (ChatGPT) Bring to Data Science Research and Education? Pros and Cons". It was moderated by Lei Chen and Xiaochun Yang. The discussion raised several questions on how large language models (LLMs) and database research and education can help each other and the potential risks of LLMs.

cs.DB↗

arXiv2012

ALAE: Accelerating Local Alignment with Affine Gap Exactly in Biosequence Databases

We study the problem of local alignment, which is finding pairs of similar subsequences with gaps. The problem exists in biosequence databases. BLAST is a typical software for finding local alignment based on heuristic, but could miss results. Using the Smith-Waterman algorithm, we can find all local alignments in O(mn) time, where m and n are lengths of a query and a text, respectively. A recent exact approach BWT-SW improves the complexity of the Smith-Waterman algorithm under constraints, but still much slower than BLAST. This paper takes on the challenge of designing an accurate and efficient algorithm for evaluating local-alignment searches, especially for long queries. In this paper, we propose an efficient software called ALAE to speed up BWT-SW using a compressed suffix array. ALAE utilizes a family of filtering techniques to prune meaningless calculations and an algorithm for reusing score calculations. We also give a mathematical analysis and show that the upper bound of the total number of calculated entries using ALAE could vary from 4.50mn0.520 to 9.05mn0.896 for random DNA sequences and vary from 8.28mn0.364 to 7.49mn0.723 for random protein sequences. We demonstrate the significant performance improvement of ALAE on BWT-SW using a thorough experimental study on real biosequences. ALAE guarantees correctness and accelerates BLAST for most of parameters.

cs.DB↗