Multimodal large language models (MLLMs) demonstrate strong capabilities in multimodal understanding, reasoning, and interaction but still face the fundamental limitation of hallucinations, where they generate erroneous or fabricated information. Most existing research induces hallucinations by manually perturbing visual or instruction inputs, then uses output differences or model-generated descriptions as references to mitigate hallucinations and improve responsevisual consistency. However, these methods are constrained by model capabilities and prone to hallucination propagation. We propose Visual Clue Guided Decoding (VCGD), a novel decoding strategy that introduces an auxiliary Caption Model to generate precise visual clues during decoding for guiding model generation. It further incorporates image confidence constraints to critically suppress hallucination propagation during generation, thereby significantly improving content reliability and visual consistency. Specifically, VCGD leverages high-quality visual descriptions to guide MLLMs in correcting perceptual biases while generating answers. Furthermore, we introduce a Reinforcement Learning-based training paradigm for the Caption Model, in which a Reward Agent provides feedback on the quality of visual clues, further enhancing the accuracy of visual information. Extensive experiments across multiple benchmark datasets and state-of-the-art MLLMs demonstrate that VCGD significantly reduces hallucination rates and improves cross-modal consistency. Our method exhibits strong generalizability and scalability, offering an effective decoding enhancement strategy that can be seamlessly integrated into existing multimodal frameworks.

Zero-Shot Relation Triplet Extraction (ZSRTE) aims to extract head-tail entity pairs and their corresponding relations from sentences, where the relations available during inference are not seen during training. Existing methods typically assume that entities are continuous; however, in practice, entities can be discontinuous, which poses challenges to these approaches. To address this issue, we are the first to discuss and study the ZSRTE task involving discontinuous entities, and propose an innovative BoG framework, which is based on our proposed Boundary Token Graph structure. This method first predicts and adds edges between boundary tokens of (dis)continuous entities to construct a token graph, and then innovatively transforms the relation triplet extraction task into a process of finding paths in the graph. Additionally, we design a Boundary Token-Aware Prompt for each relation to further enhance the interaction between boundary tokens and relation semantics. Experimental results on four ZSRTE datasets—with or without discontinuous entities—consistently demonstrate that our method outperforms previous approaches, achieving state-of-the-art results.

Guqin (古 琴) Jianzi (減 字) is an open and freely compositional tablature system that encodes performance actions rather than acoustic outcomes.Its automatic recognition remains largely unexplored, as conventional OCR assumes a closed and enumerable glyph set and struggles with Jianzi's unbounded composition and manuscript-level variability.We introduce Zero-shot Jianzi Recognition, which formulates Jianzi recognition as visionto-sequence prediction of canonical component sequences under a zero-shot split.To enable scalable supervision, we construct Synthetic-JZ from aligned online composition metadata.We then synthesize manuscriptlike training images via component-wise style recomposition and manuscript-domain noise modeling, and fine-tune a VLM for end-toend component sequence recognition.At inference time, a lightweight legality-guided correction module re-ranks decoding candidates, suppressing structural hallucinations without modifying the backbone.Experiments on two benchmarks show that our method achieves 63.02% sequence accuracy on Real-JZ, our manually annotated realworld Jianzi benchmark, surpassing Gemini-3-Pro by 35.11%.This result highlights the feasibility of reliable automated Jianzi recognition and its potential for large-scale digitization of historical Guqin Jianzi Pu manuscripts.

Document-level relation extraction (DocRE)aims to determine which relations hold between a given entity pair within a document.As a multi-label classification task, the most commonly adopted paradigm introduces a learnable threshold to distinguish positive and negative classes for an entity pair.Under this paradigm, existing losses decouple the optimization into independent positive and negative losses, which interact solely with a shared threshold.This leads to two inherent limitations: (i) threshold instability caused by conflicting gradient updates from the decoupled losses; and (ii) optimization bias exacerbated by the severe imbalance between limited positive samples and abundant negative samples inherent in DocRE, which makes the model more likely to predict that no relation exists.To address these issues, we propose the Adaptive-Threshold Global Loss (ATGL).Unlike prior work, ATGL integrates positive, negative, and threshold optimization into a unified logit space and explicitly enforces ranking constraints on their contributions to the objective.Furthermore, ATGL incorporates an imbalance-aware optimization mechanism, thereby effectively addressing the severe class imbalance in DocRE.Our ATGL serves as a general optimization objective that can be readily applied to different DocRE models.Experiments on four datasets show that ATGL outperforms other DocRE losses and achieves state-of-the-art results, while consistently improving the performance of existing DocRE models.

Cross-document Relation Extraction (CodRE) requires reasoning over scattered evidence to identify relations between target entities across multiple documents. Existing methods indiscriminately fuse target entities and the intermediate bridge entities that link them into a unified representation. This leads to intermediate evidence that often aligns with only one side of the entity pair, resulting in one-sided relation transfer contextual bias and incomplete reasoning chains. Moreover, these methods typically employ a global threshold to determine relation existence for all entity pairs, limiting the model’s reasoning performance.To address these issues, we propose DEBAR (Dual-stream Entity Bias Reduction), a framework designed to explicitly decouple and preserve bidirectional bridge evidence, combined with a novel dynamic loss optimization objective. Specifically, DEBAR employs a bridge-aware input construction strategy and a dual-stream graph reasoning network to separately encode head and tail contexts, preventing semantic interference while capturing global dependencies through iterative message passing. Furthermore, we introduce a curriculum-aware ranking optimization objective that progressively tightens classification constraints to stabilize training and enforce discriminative decision boundaries. Experiments on the CodRE benchmarks show that DEBAR achieves state-of-the-art performance while effectively mitigating cross-document contextual bias. Moreover, extensive experiments on our proposed loss across backbones confirm its generalization, suggesting it as a reliable replacement for existing CodRE losses. Code is available at https://github.com/newyuyou/DEBAR.

Multi-modal entity alignment aims to identify equivalent entities between two different multi-modal knowledge graphs.Current methods have made significant progress by improving embedding and cross-modal fusion.However, most of them depend on using loss functions to capture the relationship between modalities or adopt a one-time strategy to directly compute modality weights using attention mechanisms, which overlooks the relative interactions between modalities at the entity level and the accuracy of modality weights, thereby hindering the generalization to diverse entities.To address this challenge, we propose RICEA, a relative interaction and calibration framework for multi-modal entity alignment, which dynamically computes weights based on the relative interaction and recalibrates the weights according to their uncertainties.Among these, we propose a novel method called ADC that utilizes attention mechanisms to perceive the uncertainty of the weight for each modality, rather than directly calculating the weight of each modality as in previous works.Across 5 datasets and 23 settings, our proposed framework significantly outperforms other baselines.Our code and data are available at https://github.com/ChenxiaoLi-Joe/RICEA.

Multimodal large language models (MLLMs) demonstrate strong capabilities in multimodal understanding, reasoning, and interaction but still face the fundamental limitation of hallucinations, where they generate erroneous or fabricated information. To mitigate hallucinations, existing methods annotate pair-responses (one non-hallucination vs one hallucination) using manual methods or GPT-4V, and train alignment algorithms to improve the correspondence between images and text. More critically, an image description often involve multiple dimensions (e.g., object attributes, posture, and spatial relationships), making it challenging for the model to comprehensively learn multidimensional information from pair-responses. To this end, in this paper, we propose RRHFV, which is the first using rank-responses (one non-hallucination vs multiple ranking hallucinations) to mitigate multimodal hallucinations. Instead of using pair-responses to train the model, RRHF-V expands the number of hallucinatory responses, so that the responses with different scores in a rank-response enable the model to learn rich semantic information across various dimensions of the image. Further, we propose a scene graph-based approach to automatically construct rank-responses in a cost-effective and automatic manner. We also design a novel training objective based on rank loss and margin loss to balance the differences between hallucinatory responses within a rankresponse, thereby improving the model’s image comprehension. Experiments on two MLLMs of different sizes and four widely used benchmarks demonstrate that RRHF-V is effective in mitigating hallucinations and outperforms the DPO method based on pair-responses.

Multimodal Entity Alignment (MMEA) aims to identify equivalent entities across different multimodal knowledge graphs (MMKGs) by integrating structural information, entity attributes, and visual data, thereby promoting knowledge sharing and deep multimodal data integration. However, existing methods often overlook the deeper connections between multimodal data. They primarily focus on the interactions between neighboring entities in the structural modality while neglecting the interactions between entities in the visual and attribute modalities. To address this, we propose a structure-guided multimodal entity alignment method (SGMEA), which prioritizes structural information from knowledge graphs to enhance the visual and attribute modalities. By fusing multimodal representations, SGMEA improves the accuracy of entity alignment. Experimental results demonstrate that SGMEA achieves stateof-the-art performance across multiple datasets, validating its effectiveness and superiority in practical applications.

Multi-modal entity alignment aims to identify equivalent entities between two different multi-modal knowledge graphs, which consist of structural triples and images associated with entities. Unfortunately, prior works fuse the multi-modal knowledge of all entities only via solely one single fusion strategy. Therefore, the impact of the fusion strategy on individual entities could be largely ignored. To solve this challenge, we propose AMF2SEA, an adaptive multi-modal feature fusion strategy for entity alignment, which dynamically selects the optimal entity-level feature fusion strategy. Additionally, we build a new dataset based on DBP15K, which includes a full set of entity images from multiple inconsistent web sources, making it more representative of the real world. Experimental results demonstrate that our model achieves state-of-the-art (SOTA) performance compared to models using the same modality on DBP15K and its variants with richer image sources and styles. Our code and data are available at https://github.com/ChenxiaoLiJoe/AMFFSEA.

Zero-shot Relation Triplet Extraction (ZSRTE) aims to extract triplets from the context where the relation patterns are unseen during training. Due to the inherent challenges of the ZSRTE task, existing extractive ZSRTE methods often decompose it into named entity recognition and relation classification, which overlooks the interdependence of two tasks and may introduce error propagation. Motivated by the intuition that crucial entity attributes might be implicit in the relation labels, we propose a Relation-Centric joint ZSRTE method named Re-Cent. This approach uses minimal information, specifically unseen relation labels, to extract triplets in one go through a unified model. We develop two span-based extractors to identify the subjects and objects corresponding to relation labels, forming span-pairs. Additionally, we introduce a relation-based correction mechanism that further refines the triplets by calculating the relevance between span-pairs and relation labels. Experiments demonstrate that Re-Cent achieves state-of-the-art performance with fewer parameters and does not rely on synthetic data or manual labor.

Entity Alignment (EA) is a critical task in Knowledge Graph (KG) integration, aimed at identifying and matching equivalent entities that represent the same real-world objects. While EA methods based on knowledge representation learning have shown strong performance on synthetic benchmark datasets such as DBP15K, their effectiveness significantly decline in real-world scenarios which often involve data that is highly heterogeneous, incomplete, and domain-specific, as seen in datasets like DOREMUS and AGROLD. Addressing this challenge, we propose DAEA, a novel EA approach with Domain Adaptation that leverages the data characteristics of synthetic benchmarks for improved performance in real-world datasets. DAEA introduces a multi-source KGs selection mechanism and a specialized domain adaptive entity alignment loss function to bridge the gap between real-world data and optimal benchmark data, mitigating the challenges posed by aligning entities across highly heterogeneous KGs. Experimental results demonstrate that DAEA outperforms state-of-the-art models on real-world datasets, achieving a 29.94% improvement in Hits@1 on DOREMUS and a 5.64% improvement on AGROLD. Code is available at https://github.com/yangxiaoxiaoly/DAEA.

Zero-shot Relation Extraction (ZSRE) aims to predict novel relations from sentences with given entity pairs, where the relations have not been encountered during training. Prototypebased methods, which achieve ZSRE by aligning the sentence representation and the relation prototype representation, have shown great potential. However, most existing works focus solely on improving the quality of prototype representations, neglecting sentence representations and lacking interaction between different types of relation side information. In this paper, we propose a novel ZSRE framework named CE-DA, which includes two modules: Custom Embedding and Dynamic Aggregation. We employ a two-stage approach to obtain customized embeddings of sentences. In the first stage, we train a sentence encoder through unsupervised contrastive learning, and in the second stage, we highlight the potential relations between entities in sentences using carefully designed entity emphasis prompts to further enhance sentence representations. Additionally, our dynamic aggregation method assigns different weights to different types of relation side information through a learnable network to enhance the quality of relation prototype representations. In contrast to traditional methods that treat the importance of all side information equally, our dynamic aggregation method further strengthen the interaction between different types of relation side information. Our method demonstrates competitive performance across various metrics on two ZSRE datasets.

Large Language Models (LLMs) have shown impressive capabilities in language understanding and generation, leading to growing interest in zero-shot relation triplet extraction (Ze-roRTE), a task that aims to extract triplets for unseen relations without annotated data.However, existing methods typically depend on costly fine-tuning and lack the structured semantic guidance required for accurate and interpretable extraction.To overcome these limitations, we propose FrameRTE, a novel Ze-roRTE framework that adopts a "frame first, then extract" paradigm.Rather than extracting triplets directly, FrameRTE first constructs high-quality Relation Semantic Frames (RSFs) through a unified pipeline that integrates frame retrieval, synthesis, and enhancement.These RSFs serve as structured and interpretable knowledge scaffolds that guide frozen LLMs in the extraction process.Building upon these RSFs, we further introduce a human-inspired three-stage reasoning pipeline consisting of semantic frame evocation, frame-guided triplet extraction, and core frame elements validation to achieve semantically constrained extraction.Experiments demonstrate that FrameRTE achieves competitive zero-shot performance on multiple benchmarks.Moreover, the RSFs we construct serve as high-quality semantic resources that can enhance other extraction methods, showcasing the synergy between linguistic knowledge and foundation models. Frame ( Work )An Agent expends effort towards achieving a Goal.Alternatively, a Salient_entity involved in the Goal can be expressed in place of a Goal expression.Definition Agent: The Agent puts effort into reaching Goal. Core Frame Elements Goal:The Goal is what the Agent expends effort to achieve.Salient_entity: An entity that is centrally involved in the Goal that the Agent is attempting to acheive.Circumstances, Degree,

Large visual-language models (LVLMs) have demonstrated remarkable performance in visual-language tasks.However, object hallucination remains a significant challenge for LVLMs.Existing studies attribute object hallucinations in LVLMs mainly to linguistic priors and data biases.We further explore the causes of object hallucinations from the perspective of frequency domain and reveal that insufficient frequency information in images amplifies these linguistic priors, increasing the likelihood of hallucinations.To mitigate this issue, we propose the Multi-Frequency Contrastive Decoding (MFCD) method, a simple yet trainingfree approach that removes the hallucination distribution in the original output distribution, which arises from LVLMs neglecting the highfrequency information or low-frequency information in the image input.Without compromising the general capabilities of LVLMs, the proposed MFCD effectively mitigates the object hallucinations in LVLMs.Our experiments demonstrate that MFCD significantly mitigates object hallucination across diverse large-scale vision-language models, without requiring additional training or external tools.In addition, MFCD can be applied to various LVLMs without modifying model architecture or requiring additional training, demonstrating its generality and robustness.Codes are available at https://github.com/liubq-dev/mfcd.

Multi-modal entity alignment (MMEA) aims to identify equivalent entities between two multimodal knowledge graphs (MMKGs).Existing methods have made substantial advancements in enhancing multi-modal fusion.However, the intrinsic noise within modalities, such as the inconsistency in visual modality and redundant attributes, has not been thoroughly investigated.Excessive noise not only weakens semantic representation but also increases the risk of overfitting in attention-based fusion methods.To address this, we propose LGEA (LLM-Guided Entity Alignment), a novel LLM-guided MMEA framework that prioritizes noise reduction before fusion.Specifically, LGEA introduces two key strategies: (1) fine-grained visual filtering to remove irrelevant images at the semantic level, and (2) contextual summarization of attribute information to enhance entity semantics.To our knowledge, we are the first work to apply LLMs for both visual filtering and attribute-level semantic enhancement in MMEA.Experiments on multiple benchmarks, including the noisy FBYG dataset, show that LGEA sets a new state-of-the-art (SOTA) in robust multi-modal alignment, highlighting the potential of noiseaware strategies as a promising direction for future MMEA research 1 .

The goal of document-level relation extraction (DocRE) is to identify relations between entities from multiple sentences. As a multi-label classification task, a common approach is to determine whether there are relations for an entity pair by selecting a multi-label classification threshold, with scores of relations above the threshold predicted as positive and the rest as negative. However, we find that predicting multiple relations for entity pairs causes the decrease of predicted scores in positive classes. This could lead to many positive classes being incorrectly predicted as negative. Additionally, our analysis suggests that fitting the distribution of predicted relations to the prior distribution of relations can help improve prediction performance. However, previous studies have not explored or leveraged the prior distribution of relations. To address these issues and findings, we for the first time propose the idea of incorporating the relational prior distribution into the loss calculation in DocRE tasks. We innovatively propose an Adaptive Relational Prior Distribution Loss (ARPDL), which can adaptively adjust relation prediction scores based on the relational prior distribution. Our designed relational prior distribution component can also be integrated as an adapter into other threshold-based losses to improve prediction performance. Experimental results demonstrate that ARPDL consistently improves the performance of existing DocRE models, achieving new state-of-the-art results. Furthermore, integrating our relational prior distribution adapter into other losses significantly enhances their performance in DocRE tasks, validating the effectiveness and generality of our approach. Code is available at https://github.com/xhm-code/ARPDL.

Fu Zhang, Xinlong Jin, Jingwei Cheng, Hongsen Yu, Huangming Xu. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Document-level Relation Extraction (DocRE) aims to extract relations between entity pairs in a document and poses many challenges as it involves multiple mentions of entities and crosssentence inference.However, several aspects that are important for DocRE have not been considered and explored.Existing work ignore bidirectional mention interaction when generating relational features for entity pairs.Also, sophisticated neural networks are typically designed for cross-sentence evidence extraction to further enhance DocRE.More interestingly, we reveal a noteworthy finding: If a model has predicted a relation between an entity and other entities, this relation information may help infer and predict more relations between the entity's adjacent entities and these other entities.Nonetheless, none of existing methods leverage secondary reasoning to exploit results of relation prediction.To this end, we propose a novel Secondary Reasoning Framework (SRF) for DocRE.In SRF, we initially propose a DocRE model that incorporates bidirectional mention fusion and a simple yet effective evidence extraction module (incurring only an additional learnable parameter overhead) for relation prediction.Further, for the first time, we elaborately design and propose a novel secondary reasoning method to discover more relations by exploring the results of the first relation prediction.Extensive experiments show that SRF achieves SOTA performance and our secondary reasoning method is both effective and general when integrated into existing models. 1

The mission of commonsense knowledge graph completion (CKGC) is to infer missing facts from known commonsense knowledge.CKGC methods can be roughly divided into two categories: triple-based methods and text-based methods.Due to the imbalanced distribution of entities and limited structural information, triple-based methods struggle with long-tail entities.Text-based methods alleviate this issue, but require extensive training and fine-tuning of language models, which reduces efficiency.To alleviate these problems, we propose ATAP, the first CKGC framework that utilizes automatically generated continuous prompt templates combined with pre-trained language models (PLMs).Moreover, ATAP uses a carefully designed new prompt template training strategy, guiding PLMs to generate optimal prompt templates for CKGC tasks.Combining the rich knowledge of PLMs with the template automatic augmentation strategy, ATAP effectively mitigates the long-tail problem and enhances CKGC performance.Results on benchmark datasets show that ATAP achieves state-of-theart performance overall. 1

Entity alignment (EA) refers to the task of linking entities in different knowledge graphs (KGs). Existing EA methods rely heavily on structural isomorphism. However, in real-world KGs, aligned entities usually have non-isomorphic neighborhood structures, which paralyses the application of these structure-dependent methods. In this paper, we investigate and tackle the problem of entity alignment between heterogeneous KGs. First, we propose two new benchmarks to closely simulate real-world EA scenarios of heterogeneity. Then we conduct extensive experiments to evaluate the performance of representative EA methods on the new benchmarks. Finally, we propose a simple and effective entity alignment framework called Attr-Int, in which innovative attribute information interaction methods can be seamlessly integrated with any embedding encoder for entity alignment, improving the performance of existing entity alignment techniques. Experiments demonstrate that our framework outperforms the state-of-the-art approaches on two new benchmarks.

Knowledge graph completion (KGC, also referred to as link prediction) aims at predicting missing entities and relations in knowledge graphs (KGs). Knowledge graph embedding (KGE) techniques have been proven to be effective for link prediction. Currently, a series of convolutional neural networks (CNNs) based models (e.g., ConvE and its extended models) have attained excellent results for link prediction. However, several aspects that are important for link prediction using CNNs have not been considered and enhanced simultaneously, which significantly limit the performance of these models. In this paper we explore an effective KGE model based on CNNs. We investigate and discover four extremely important aspects that have a strong influence on ConvE: entity and relation embeddings, entity-to-relation interaction approaches, CNN structure, and loss function. Based on the optimization of the above four aspects, we propose a novel KGE method called ConvEICF. Through extensive experiments, we find that ConvEICF outperforms the previous state-of-the-art link prediction baselines on FB15k-237 and WN18RR datasets. In particular, ConvEICF achieves a Hits@10 score that is 11.2% and 6.5% better than ConvE on FB15k-237 and WN18RR datasets respectively. Additionally, through in-depth experiments we observe an interesting phenomenon and important finding that the very common 1-N scoring technique in KGE can be considerably improved by just adding a dropout operation. Our code is available at https://github.com/NEU-IDKE/ConvEICF.

Currently, named entity recognition (NER) is mainly evaluated on standard and well-annotated data sets. However, the construction of a well-annotated data set will consume a lot of manpower and time. In lots of applications of NER, data sets may contain a lot of noise, and a large part of noise comes from unlabeled entities. At present, the training process of most models treat unlabeled entities as nonentities, which causes these models to lean toward predicting most words of an input context as nonentities and greatly affects their performances. In this paper, as the first attempt, we innovatively propose an adaptive positive-unlabeled (adaPU) learning technology, and integrate the adaPU into a machine reading comprehension (MRC) framework for NER, which can still perform well on data sets with a large proportion of unlabeled entities. In our framework, to leverage the above problem that a model may predict most words of an input context as nonentities, we propose an adaPU learning technology by adjusting a loss coefficient of positive and negative samples. Moreover, instead of just constructing a fixed query for each entity type as input to MRC, we propose a new method of dynamically constructing multiple queries for each entity type, which also brings slight performance improvement for NER. Accordingly, we explore new training and entity inference strategies for our learning framework. The experimental results show that our framework is effective on data sets that contain a large number of unlabeled entities. When the proportion of unlabeled entities reaches 50%, our framework still can keep from losing effectiveness and maintain more than 80 F1-scores on several data sets. Also, the experiments show that our framework can achieve better or competitive performance on standard data sets. The ablation experiments further fully demonstrate our MRC framework with adaPU learning and dynamic query construction method can improve the performance of NER.

Fuzzy ontologies are deemed as useful formalisms for dealing with vagueness in the Semantic Web community. Description logics (DLs) are the logical foundations of standard web ontology languages. Conjunctive queries are deemed as an expressive reasoning service for DLs. DL reasoners can be enriched by a conjunctive query service. In this study, we focus on fuzzy (threshold) conjunctive queries over knowledge bases encoding in fuzzy DL ALC(G), the well known fuzzy DL with customized fuzzy data type support. We provide a tableau-based algorithm for deciding query entailment of ALC(G). Our algorithm is applicable to more expressive DLs and arbitrary conforming fuzzy data type group.

How to quickly and cheaply construct Web ontologies has become a key technology to enable the Semantic Web. Classical ontologies are not sufficient for handling imprecise and uncertain information that is commonly found in many application domains. In this paper, we propose an approach for constructing fuzzy ontologies from fuzzy UML models, in which the fuzzy ontology consists of fuzzy ontology structure and instances. Firstly, the fuzzy UML model is investigated in detail, and a kind of formal definition of fuzzy UML models is proposed. Then, a kind of fuzzy ontology called fuzzy OWL DL ontology is introduced. Furthermore, we consider the fuzzy UML model and the corresponding fuzzy UML instantiations (i.e., object diagrams) simultaneously, and translate them into the fuzzy ontology structure and the fuzzy ontology instances, respectively. In addition, since a fuzzy OWL DL ontology is equivalent to a fuzzy Description Logic f-SHOIN(D) knowledge base, how the reasoning problems of fuzzy UML models (e.g., consistency, subsumption, equivalence, and redundancy) may be reasoned through reasoning mechanism of f-SHOIN(D) is investigated, which can help to construct fuzzy ontologies more exactly.

Rules have been playing an increasingly important role in the Semantic Web. However, general rule languages are not capable of representing much imprecise and uncertain knowledge in the Semantic Web, nor are if-then rules. Combining if-then rules with OWL DL in the framework of fuzzy sets and possibility distribution, we propose fuzzy Semantic Web if-then Rule Language (f-SW-if-then-RL), and investigate its syntax and semantics. Considering nonmonotonicity, we employ unless rules to extend f-SW-if-then-RL, and f-SW-if-then-unless-RL appears. Two kinds of negation are introduced to express semantics of unless rules. And we extend rule interchange format R2ML to encode nonmonotonic fuzzy rules.

In the real world, human knowledge and natural language have a big deal of imprecision and uncertainty. Imprecision and uncertainty play in the semantic Web context, as well as to many applications that use description logics (DLs) to capture, represent and perform reasoning with domain knowledge. In this paper, a fuzzy extension of description logic languageSIis presented, which combines vague sets with SI. Its syntax, semantics and inference problems are investigated in the paper. Also the tableau algorithm is developed for reasoning in the vagueSI.

The Semantic Web is expected to process concept knowledge and data information in an intelligent and automatic way. Recent research has shown that OWL has a serious limitation on data types; i.e., it does not support customized data types and customized data type predicates. Furthermore, it canpsilat process imprecise and uncertain information which widely exists in the Semantic Web and Ontology. These issues are being addressed by W3C Semantic Web Best Practices and Development Working Group. In the current paper, we make the following two contributions to solve the above limitations: (i) present a new kind of fuzzy description logic F-SHOIQ(G) which can not only support the representation and reasoning of fuzzy concept knowledge, but also support fuzzy data information with customized fuzzy data types and customized fuzzy data type predicates; (ii) give the tableau algorithm for F-SHOIQ(G) and prompt a flexible reasoning architecture for fuzzy data type reasoning, also the design for fuzzy data type reasoner is discussed here. The example in paper witnesses the representation and reasoning capabilities of F-SHOIQ(G) go clearly beyond the other DLs.