Bridging Vision and Texts: An External Graph Framework for Enhanced Language Comprehension

1. Introduction

Recent advances in natural language understanding (NLU) and natural language generation (NLG) have significantly transformed the landscape of artificial intelligence. State-of-the-art models have achieved remarkable performance across various benchmarks [18,40,41], yet this progress has come with an ever-increasing demand for computational resources and a rapid escalation in the number of model parameters [8,11,30]. This surge in complexity has led to substantial financial, computational, and environmental costs, which pose serious challenges for both academia and industry [34].

Traditional approaches aimed at improving efficiency, such as model distillation [32] or enforcing parameter sharing [21], primarily focus on compressing or reorganizing the internal structure of language models. However, these techniques still necessitate the storage of vast amounts of information within the model parameters, limiting their scalability and flexibility in dynamic environments. By contrast, our proposed method advocates for the externalization of knowledge, thereby relieving language models from the need to memorize extensive amounts of information.

This work introduces the idea of augmenting language models with a dedicated external repository that houses a rich, multimodal knowledge graph. Such an approach permits the retrieval of additional contextual cues, both textual and visual, during the inference process. Consequently, models can leverage up-to-date and diversified data without overburdening their internal architectures, leading to enhanced performance and improved parameter efficiency. The concept of retrieving external information to support language understanding has been explored in prior research [27,29]. Yet, most existing efforts focus solely on textual data and often neglect the substantial benefits provided by visual cues. In our framework, these visual elements are integrated alongside multilingual textual descriptions, offering a more comprehensive representation of entities. This multimodal strategy not only enriches the information available to the language model but also bridges the gap between purely text-based representations and the complex, real-world scenarios in which these models are deployed.

Moreover, our framework, designated as AlphaKG, is designed to interface seamlessly with contemporary language models, providing them with external representations that are both visually and linguistically grounded. This integration enables the model to dynamically access and utilize supplementary information, thereby supporting more nuanced and context-aware decision-making processes. The external repository can be updated independently of the main language model, which allows for continuous learning and rapid adaptation to new information. Beyond the technical advantages, the externalization of knowledge offers significant practical benefits. By decoupling the storage of extensive background information from the core model, our approach facilitates a modular design. Such modularity allows individual components to be refined or replaced without necessitating a complete overhaul of the system. This flexibility is especially critical in applications where the underlying data evolves rapidly or where frequent updates are required to maintain high performance. Another notable aspect of our method is its potential for scalability. As the external knowledge graph is updated with additional data—ranging from emerging visual trends to newly available multilingual text—the language model benefits from a continuously expanding repository of relevant information. This ensures that the model remains effective even as the scope of real-world data broadens, without the need to increase its intrinsic parameter count.

In addition, our approach mitigates the challenges associated with overfitting that are common in large-scale language models. By offloading a significant portion of the required knowledge to an external graph, the model can focus on learning how to effectively integrate and interpret this supplementary information. This decoupling of knowledge storage from inference processes encourages more robust generalization and better performance across diverse tasks. Furthermore, the use of an external multimodal knowledge graph provides an innovative pathway for integrating disparate sources of information. The synergy between textual and visual data enhances the overall representational capacity of the system, paving the way for breakthroughs in tasks such as multilingual named entity recognition (NER) and visual verb sense disambiguation (VSD). By combining these modalities, the system is better equipped to capture subtle contextual cues that are often missed by models relying solely on one type of data.

In summary, the externalization of knowledge through a multimodal graph framework represents a significant shift in the design of language understanding systems. The AlphaKG model exemplifies how decoupling knowledge storage from the internal parameters of language models can lead to enhanced efficiency, improved scalability, and greater adaptability. This paradigm not only alleviates the growing burden of model size but also opens new avenues for future research in integrating multimodal information into language processing pipelines.

2. Related Work

• Retrieval Augmented Models

Another significant research direction involves retrieval augmented models, where external information is accessed by querying pre-indexed knowledge bases rather than relying solely on internal memory. In these approaches, the external repository is populated with data that extends far beyond the training corpus of the target task. For example, Lee et al. [22] introduced a framework for Open Retrieval Question Answering (ORQA) in which both the retrieval and answering components are jointly trained to leverage external data sources. Similarly, Karpukhin et al. [19] developed a dense passage retriever (DPR) that surpasses traditional sparse retrieval methods such as TF-IDF or BM25 by significantly enhancing retrieval quality, which in turn leads to improved performance on question answering tasks.

Additional work, such as REALM [14], incorporates a dense Wikipedia index and fine-tunes both the index and the language model simultaneously to tackle open-domain QA problems. In parallel, Petroni et al. [28] examined the effect of feeding BERT with contexts retrieved or generated through different techniques, revealing that external information can substantially influence unsupervised QA performance. Moreover, Lewis et al. [23] integrated a retrieval module into an encoder-decoder architecture to condition the generation process on factual data extracted from Wikipedia. While these models predominantly focus on text-based retrieval, our proposed AlphaKG framework expands upon this paradigm by incorporating structured multimodal information. Unlike conventional retrieval systems that treat facts as unstructured text, AlphaKG is designed to retrieve and leverage both visual and textual features that are inherently organized according to a knowledge graph’s structure.

• Multimodal Pretraining

Pretraining methods that jointly model vision and language have recently emerged as a powerful trend, achieving state-of-the-art results on various multimodal reasoning tasks [24,37,47]. These approaches generally adopt masked multimodal modeling techniques over image-text pairs to learn rich, joint representations that capture the intricate interactions between visual content and linguistic cues. Unlike end-to-end models that rely solely on raw paired data, these multimodal pretraining frameworks harness the synergy between modalities to better capture context and semantic nuances.

While many existing methods focus on implicitly learning cross-modal connections through large-scale data, our approach explicitly incorporates structured external knowledge into the model. The AlphaKG framework leverages a well-organized knowledge graph that contains not only multilingual textual descriptions but also visual representations of entities. This explicit modeling of entity-centric relationships enables a more precise retrieval of multimodal information, which is crucial for tasks requiring fine-grained reasoning. Furthermore, by structuring the external information, AlphaKG facilitates interpretable alignments between visual cues and textual semantics, providing an additional layer of robustness and control.

The trend in recent research is evident: external and structured knowledge sources are increasingly recognized as valuable complements to internal model representations. Memory networks, retrieval augmented models, and multimodal pretraining techniques all contribute unique perspectives on how best to integrate external information. Our AlphaKG framework builds on these insights by uniting the strengths of dynamic memory access, sophisticated retrieval mechanisms, and multimodal pretraining. This integration is expected to yield significant improvements in tasks that demand a deep understanding of both visual and textual data.

In conclusion, the body of work encompassing external memory augmentation, retrieval-based approaches, and multimodal pretraining offers a comprehensive foundation for advancing language understanding systems. By synthesizing these diverse methodologies, our proposed AlphaKG framework presents a novel approach to incorporating structured, multimodal knowledge into neural models, thereby addressing critical challenges in scalability, efficiency, and interpretability.

• Memory in Neural Networks

The idea of augmenting neural models with an external memory has a long-standing history in the literature. Early studies demonstrated that recurrent neural networks could be enriched with external memory mechanisms to capture context-free grammars and other complex structures [9,48]. More contemporary frameworks, such as memory networks [36,43] and neural Turing machines [13], further advanced this concept by enabling networks to dynamically read from and write to external storage. These architectures provide models with the ability to maintain long-term dependencies and to manipulate contextual information beyond the limitations of fixed internal representations.

In these systems, the memory access is typically managed via differentiable attention mechanisms. For example, a canonical approach to reading from memory involves computing a weighted sum over memory slots. Although such formulations are not the focus of our present work, they offer important insights into how external memory components can enhance neural computations. Recent research has also extended these techniques to multimodal contexts, where visual and textual data are stored in a unified memory system. For instance, Xiong et al. [45] adapted memory networks for both textual question answering and visual question answering by aligning visual features with textual queries, while Su et al. [35] and Wang et al. [42] demonstrated that incorporating a visual memory component improves performance in tasks like video captioning and visual QA. These advances underscore the importance of dynamic memory modules that can integrate heterogeneous information, a principle that underlies our proposed AlphaKG framework.

3. Methodology

3.1. Overview and Motivation

AlphaKG [1] represents a state-of-the-art multilingual and multimodal knowledge graph (KG) constructed by leveraging BabelNet v4.0 [26] and ImageNet [31]. Unlike many traditional KGs that focus solely on textual or structured data, AlphaKG integrates visual information by associating multiple images with each node. Each node corresponds to a synset—a set of synonymous terms that describe a specific concept—and is enriched with descriptions in several languages. For example, the synset representing the concept of dog may be accompanied by the gloss “The dog is a mammal in the order Carnivora,” along with several illustrative images. This rich, multimodal integration makes AlphaKG particularly suitable for bridging the gap between vision and language tasks.

The design of AlphaKG was motivated by the need for high-quality, well-curated data that is directly applicable in modern neural pipelines for vision-and-language research. To ensure visual relevance, nodes are selected based on criteria that include both their linguistic descriptions and the presence of strong visual features. The knowledge graph covers a wide range of topics and includes 13 distinct relation types that emphasize visual components. These relation types include: is-a, has-part, related-to, used-for, used-by, subject-of, receives-action, made-of, has-property, gloss-related, synonym, part-of, and located-at. Such a diverse set of relations enables the KG to capture intricate semantic connections and nuanced visual relationships among concepts.

To our knowledge, AlphaKG is the only publicly available multimodal KG that has been specifically designed for seamless integration into neural model pipelines. Although our experiments focus on AlphaKG, the underlying framework we propose, AlphaKG, can be extended to any similar knowledge repository, thereby broadening its applicability across various research domains.

3.2. Graph Structure and Mathematical Notation

We formalize the AlphaKG KG as a directed graph $\mathcal{G}=(\mathcal{V},\mathcal{E})$, where $\mathcal{V}$ denotes the set of nodes (or synsets) and $\mathcal{E}$ represents the set of directed edges corresponding to typed semantic relations between these nodes. Each edge $e_r\in \mathcal{E}$ is associated with a relation type r from the predefined set of 13 relation categories.

For any given node $v_i\in \mathcal{V}$, we define its local neighborhood ${\mathcal{N}}_i$ as:

$${\mathcal{N}}_i=\{v_j\in \mathcal{V}\mid (v_i,e_r,v_j)\in \mathcal{E}\mathrm{for}\mathrm{some}{e}_r\}.$$

This neighborhood function is instrumental in many graph-based learning algorithms where information from adjacent nodes is aggregated to learn robust representations.

We denote the set of all valid relational triples (or factual tuples) in the KG as

$$\mathcal{D}=\{(v_i,e_r,v_j)\mid v_i,v_j\in \mathcal{V},e_r\in \mathcal{E}\}.$$

To facilitate training of embedding models, a set of corrupted triples ${\mathcal{D}}^{\prime }$ is generated by randomly substituting either the head or tail node such that the corrupted tuple $(v_i,e_r,v_j^{\prime })$ does not exist in $\mathcal{G}$. Such negative sampling is common in contrastive learning and ranking-based loss formulations.

Our representation learning approach in the AlphaKG framework involves constructing two key embedding matrices:

$$TV\in {\mathbb{R}}^{\left|\mathcal{V}\right|\times d_n}\mathrm{and}TE\in {\mathbb{R}}^{\left|\mathcal{E}\right|\times d_r},$$

where $d_n$ and $d_r$ are the dimensionalities of node and relation embeddings, respectively. The embedding for node $v_i$ is denoted by the row vector $T{v}_i=TV[i,:]$, and for a relation $e_r$, the embedding is given by $T{e}_r=TE[r,:]$. These embeddings are learned such that they preserve both the structural and semantic properties of the KG.

In addition, each node $v_i\in \mathcal{V}$ is augmented with two types of auxiliary data:

• Multilingual Glosses: A set of textual descriptions ${\mathcal{T}}_i$, where each gloss $t\in {\mathcal{T}}_i$ provides language-specific information about the concept.
• Visual Images: A collection ${\mathcal{I}}_i$ of images that visually depict the corresponding concept.

We adopt the notation $[Tx;Ty]$ to represent the concatenation of vectors $Tx$ and $Ty$, and $Tx\odot Ty$ to denote their element-wise product. These operations play a vital role in our subsequent fusion and gating mechanisms.

Furthermore, to quantify the connectivity of the graph, we define the degree of a node $v_i$ as:

$$deg\left(v_i\right)=\left|\{v_j\in \mathcal{V}\mid (v_i,e_r,v_j)\in \mathcal{E}\mathrm{or}(v_j,e_r,v_i)\in \mathcal{E}\}\right|.$$

This measure is crucial for understanding the distribution of node connectivity and for designing neighborhood aggregation strategies in graph-based models.

3.3. Statistical Properties and Integration Details

The scale of AlphaKG is significant: it comprises over 100,000 nodes and nearly 2 million relations, along with more than 1.5 million images. Such a large-scale dataset offers a rich testbed for learning multimodal representations that can capture the interplay between visual and textual modalities. Although a detailed statistical summary (including comparisons with other multimodal KGs such as WN9-IMG and FB15-IMG) was provided in earlier studies [1], here we briefly summarize some key properties:

• Node Count: $\left|\mathcal{V}\right|\approx {10}^5$
• Relation Count: The KG encompasses $\left|\mathcal{E}\right|\approx 1.9\times {10}^6$ edges, distributed across 13 distinct relation types.
• Image Associations: Each node is linked to multiple images, leading to an overall count of approximately $1.5\times {10}^6$ images.

These statistics underscore the comprehensive nature of AlphaKG and its suitability as a foundation for multimodal learning tasks. The intricate structure of AlphaKG is exploited by the AlphaKG framework to retrieve and integrate both textual and visual cues during model inference. In particular, the embedding matrices $TV$ and $TE$ are optimized not only to reconstruct the observed relational structure in $\mathcal{D}$ but also to effectively incorporate multimodal signals from ${\mathcal{T}}_i$ and ${\mathcal{I}}_i$.

To further elucidate the embedding learning process, consider a scoring function for a valid triple $(v_i,e_r,v_j)$ given by

$$\varphi (v_i,e_r,v_j)=f(T{v}_i,T{e}_r,T{v}_j),$$

where $f(·)$ is a function that measures the compatibility of the node and relation embeddings. In many models, this function might be defined as a simple dot product, a bilinear form, or even a more complex neural network function. The training objective is to maximize the score for true triples while minimizing it for corrupted ones. This objective is often formalized using a margin-based ranking loss:

$$\mathcal{L}=\sum _{(v_i,e_r,v_j)\in \mathcal{D}}\sum _{(v_i,e_r,v_j^{\prime })\in {\mathcal{D}}^{\prime }}max\left(0,\gamma +\varphi (v_i,e_r,v_j^{\prime })-\varphi (v_i,e_r,v_j)\right),$$

where $\gamma$ is a margin hyperparameter. Although this specific loss function is common in knowledge graph embedding literature, our overall framework, AlphaKG, builds upon such principles while introducing novel multimodal integration strategies.

In summary, AlphaKG is not only a repository of extensive visual and textual information but also a well-structured graph that captures rich semantic relationships among concepts. Its integration into the AlphaKG framework provides a powerful external knowledge source that enhances the capabilities of neural models in processing multimodal information.

4. Experiments

In this section, we present a comprehensive evaluation of the proposed AlphaKG framework on the link prediction task and two downstream applications: named entity recognition (NER) and crosslingual visual verb sense disambiguation (VSD). We merge all experimental analyses into this single section, detailing our experimental setup, reporting extensive quantitative results, and providing thorough discussions on the impact of incorporating additional multimodal features. In our experiments, we compare several baseline models and our hybrid architectures based on graph neural networks augmented with a DistMult layer. We also explore the effect of adding multilingual gloss (text) and image features into node and edge representations via gating mechanisms.

4.1. Experimental Setup and Training Details

We evaluate all models on the link prediction task, i.e., to identify whether a given pair of head and tail nodes in the knowledge graph are connected by a relation. For each observed triplet $(v_i,e_r,v_j)$ in the dataset, we generate k corrupted triplets by substituting the tail $v_j$ (or, equivalently, the head $v_i$) with a random node such that the resulting triplet is not part of the original graph. We experiment with two settings for the number of corrupted examples, $k\in \{100,1000\}$. Details on the architectures for the hybrid models (GraphSage+DistMult and GAT+DistMult) are provided in Appendix .

• Negative Sampling and Loss Function

All models are trained using negative sampling [25] with the goal of maximizing the probability of positive triplets while minimizing the probability of corrupted triplets. The overall loss function is given by:

$$\begin{array}{c}\hfill \mathcal{L}={\displaystyle \frac{1}{\left|\mathcal{D}\right|}}\sum _{(v_i,e_r,v_j)\in \mathcal{D}}[-log\sigma \left(\varphi (v_i,e_r,v_j)\right)\\ \hfill -\sum _{(v_i,e_r,v_j^{\prime })\in {\mathcal{D}}^{\prime }}log\sigma (-\varphi (v_i,e_r,v_j^{\prime }))],\end{array}$$

(1)

where $\sigma \left(x\right)={\displaystyle \frac{1}{1+exp(-x)}}$ is the sigmoid function, $\mathcal{D}$ is the set of positive triplets, and ${\mathcal{D}}^{\prime }$ denotes the set of corrupted (negative) triplets. For models such as TransE and DistMult, we employ both head-corrupted $(v_i^{\prime },e_r,v_j)$ and tail-corrupted triplets $(v_i,e_r,v_j^{\prime })$.

• Scoring Function

Let $\varphi (v_i,e_r,v_j)$ be the scoring function for a triplet. For graph-based models, we compute the score using a simple dot product between the final hidden states of the head and tail nodes, i.e.,

$$\varphi (v_i,e_r,v_j)=T{h}_i^{\top }T{h}_j,$$

which does not involve any learned relation parameters. For hybrid models, however, the score is computed as:

$$\varphi (v_i,e_r,v_j)=T{h}_i\odot T{e}_r\odot T{h}_j,$$

where ⊙ denotes element-wise multiplication and $T{e}_r$ is the learned embedding for relation $e_r$ contained in the matrix $TE$.

When multimodal features are incorporated, the input node embedding $T{v}_i$ is replaced by either $T{v}_i^t$ (using text features), $T{v}_i^m$ (using image features), or $T{v}_i^{t,m}$ (using both modalities). In the hybrid models, the relation embedding is similarly updated to $T{e}_r^t$, $T{e}_r^m$, or $T{e}_r^{t,m}$ respectively.

• Additional Training Details and Hyperparameters

We perform an extensive hyperparameter search for all models. In addition to the learning rate, batch size, and embedding dimensions, we also tune the dropout rate and the number of graph convolution layers. An additional regularization term is added in some experiments to constrain the norm of the node and relation embeddings:

$${\mathcal{L}}_{reg}=\lambda \left(\sum _{v_i\in \mathcal{V}}\parallel T{v}_i{\parallel }^2+\sum _{e_r\in \mathcal{E}}{\parallel T{e}_r\parallel }^2\right),$$

where $\lambda$ is a regularization coefficient. All final results are averaged over 5 independent runs, and model selection is performed based on the best validation MRR.

• Evaluation Metrics

For link prediction, we use standard metrics: Mean Reciprocal Rank (MRR) and Hits@$\{1,3,10\}$. MRR is computed as the mean of the reciprocal rank of the correct triplet, while Hits@k measures the proportion of correct triplets ranked within the top-k predictions. Increasing the number of negative examples k typically renders the task more challenging.

4.2. Results on Link Prediction Without Additional Features

• Tuple-based Models

We first compare tuple-based models on the link prediction task using the full set of negative samples. Table 1 presents results for TransE, DistMult, and TuckER on the AlphaKG test set. As can be seen, TuckER significantly outperforms both TransE and DistMult. In particular, TuckER achieves an MRR of 6.1, Hits@1 of 3.4, Hits@3 of 6.3, and Hits@10 of 11.1, roughly twice the performance of the other two models. These findings are consistent with previous literature [4].

• Graph-based vs. Hybrid Models

We now compare the performance of graph-based models and their hybrid counterparts that incorporate a DistMult layer to learn relation embeddings. We evaluate vanilla GAT and GraphSage as well as the hybrid models GAT+DistMult and GraphSage+DistMult. Results with 100 negative examples per positive triplet are summarized in Table 2. Note that the column labeled R indicates whether relation features are learned (3) or not (7). Although vanilla GAT and GraphSage perform relatively poorly compared to TuckER, their hybrid variants show marked improvements. In particular, GraphSage+DistMult attains an MRR of 78.4, Hits@1 of 56.8, and perfect scores (Hits@3 and Hits@10 at 100.0) under this setting, clearly outperforming TuckER.

4.3. Results on Link Prediction with Additional Multimodal Features

In this set of experiments, we study the impact of incorporating additional multimodal features from AlphaKG—specifically, textual features from multilingual glosses (${\mathcal{T}}_i$) and visual features from images (${\mathcal{I}}_i$)—on link prediction performance. These features are integrated into the model through node and edge gating modules.

Table 3 shows a comprehensive comparison of different feature combinations under two settings: using 100 negative examples and 1000 negative examples per positive triplet. For each model, we evaluate configurations with (i) no additional features, (ii) only visual features, (iii) only textual features, and (iv) both textual and visual features. In many cases, the hybrid models benefit considerably from the additional modalities. For instance, when using GraphSage+DistMult with both modalities and 1000 negatives, the best configuration achieves an MRR of 61.6 and Hits@1 of 50.6, outperforming models that use only one type of feature or none at all.

• Discussion on Multimodal Integration

Our results indicate that incorporating multimodal features via the gating mechanisms in AlphaKG generally leads to significant improvements in link prediction performance, particularly for the hybrid models. Although the gains are more pronounced in certain configurations (e.g., GraphSage+DistMult with both features at 1000 negatives), the overall trend is clear: both textual and visual cues contribute complementary information that enhances the learned representations. The improvement in MRR and Hits@k metrics suggests that the additional external features help the model better capture the semantic relationships and visual context embedded in AlphaKG.

4.4. Downstream Task Evaluation

To assess the practical utility of the representations learned via AlphaKG, we integrate them into two downstream tasks: Named Entity Recognition (NER) and Crosslingual Visual Verb Sense Disambiguation (VSD). In both cases, the pretrained AlphaKG node representations are used as external knowledge to augment the base models, and we explore different strategies for integrating these features.

4.4.1. Named Entity Recognition (NER)

• Datasets and Experimental Model

We evaluate on two NER datasets: GermEval 2014 for German and WNUT-17 for English. For WNUT-17, we use a pretrained English BERT model (bert-large-cased), while for GermEval 2014 we use a multilingual BERT model (bert-base-multilingual-cased). Our baseline NER system is a standard BERT-based classifier where the final token representations $T{z}_i$ are fed to a softmax layer:

$$\begin{array}{cc}\hfill T{\widehat{y}}_i& =\mathtt{softmax}\left(T{W}^{n}T{z}_i\right),\hfill \end{array}$$

(2)

with $T{W}^n$ as the classification weight matrix.

To incorporate external knowledge, we retrieve the top-k closest nodes from AlphaKG using its sentence retrieval model. Two strategies are investigated:

• Concatenation (concat): The retrieved node representation $T{h}_i^{RET}$ is concatenated with the token representation:

  $$\begin{array}{cc}\hfill T{\widehat{y}}_i& =\mathtt{softmax}\left(T{W}^n[T{z}_i;T{W}^{RET}T{h}_i^{RET}]\right),\hfill \end{array}$$

  (3)

  where $T{W}^{RET}$ projects the retrieved node to the appropriate dimension.
• Attention (attend): An attention mechanism is applied over the top-5 retrieved nodes, with $T{z}_i$ serving as the query:

  $$\begin{array}{cc}\hfill Ta& =\mathtt{Attention}(T{z}_i,{\left\{T{h}_i^{RET}\right\}}_{k=1}^5),\hfill \end{array}$$

  (4)

  $$\begin{array}{cc}\hfill T{\widehat{y}}_i& =\mathtt{softmax}\left(T{W}^n[T{z}_i;T{W}^{RET}Ta]\right).\hfill \end{array}$$

  (5)

• Quantitative Results

Table 4 reports the NER performance on the WNUT-17 (EN) and GermEval (DE) test sets. For English, the baseline achieves an F1 score of 47.4. With the addition of AlphaKG representations via the attention mechanism over the top-5 retrieved nodes (using node features without any additional multimodal data), the F1 score improves to 48.1, a 0.7% absolute gain. For German, the baseline already performs well with an F1 of 86.1, and integrating the AlphaKG representations via concatenation slightly boosts the score to 86.4. These improvements, though moderate, validate the benefit of enriching NER systems with structured external knowledge.

4.4.2. Crosslingual Visual Verb Sense Disambiguation (VSD)

Dataset and Task Description

We evaluate on the MultiSense dataset [12], which comprises 9,504 images associated with 55 English verbs and their corresponding translations in German (154 unique German verbs). Each sample in the dataset includes an ambiguous English verb, a textual context describing the verb, and an image that visually illustrates the action. The task is to disambiguate the correct translation in German based on both textual and visual context.

Baseline Model and Integration of AlphaKG

Our baseline model encodes the visual modality using a pretrained ResNet-152 [17], extracting the 2048-dimensional activation from the pool5 layer as visual features $T{z}_i^m$. Concurrently, the ambiguous English verb along with its context is encoded using a pretrained BERT model (bert-large-cased), with the resulting token embedding serving as the textual feature $T{z}_i^t$. These features are projected to lower dimensions via learned projection matrices $T{W}^m$ and $T{W}^t$, respectively, and then concatenated and passed through a hidden layer with ReLU activation:

$$\begin{array}{cc}\hfill T{h}_i& =\mathtt{ReLU}\left(T{W}^h[T{W}^{m}T{z}_i^m;T{W}^{t}T{z}_i^t]\right),\hfill \end{array}$$

(6)

followed by a final projection to the output space:

$$\begin{array}{cc}\hfill T{\widehat{y}}_i& =\mathtt{softmax}\left(T{W}^{o}T{h}_i\right).\hfill \end{array}$$

(7)

To integrate external knowledge, we retrieve the top-1 nearest node representation $T{h}_i^{RET}$ from the AlphaKG using a sentence retrieval model that processes the concatenation of the English verb and its textual context. The hidden layer is then redefined as:

$$T{h}_i=\mathtt{ReLU}\left(T{W}^h[T{W}^{m}T{z}_i^m;T{W}^{t}T{z}_i^t;T{h}_i^{RET}]\right),$$

(8)

where the hidden layer size is adjusted to maintain a comparable number of parameters with and without the additional feature.

Quantitative Results and Analysis

Table 5 summarizes the accuracy on the MultiSense test set (German). Our baseline model attains an accuracy of 94.4%, significantly outperforming the earlier reported results of 55.6% in [12], which we attribute to improvements in model design and data preprocessing. When integrating AlphaKG representations, we observe that augmenting with node features (+$T{h}_i^{\mathrm{NODE}}$) raises the accuracy to 96.8%, while incorporating image features (+$T{h}_i^{\mathrm{IMG}}$) further boosts the accuracy to 97.2%. These gains indicate that the additional multimodal and structured information provided by AlphaKG can enhance crosslingual VSD performance, especially when the base model is already highly competitive.

4.5. Summary of Experimental Findings

Across our experiments, the proposed AlphaKG framework consistently improves link prediction performance on AlphaKG as well as downstream task performance on both NER and crosslingual VSD. In link prediction, hybrid models that combine graph neural network architectures with a DistMult layer (notably GraphSage+DistMult) yield substantial gains when augmented with additional multimodal features. For downstream tasks, even modest improvements in F1 and accuracy metrics demonstrate the practical benefits of integrating structured external knowledge into state-of-the-art models.

Furthermore, the incorporation of additional textual and visual features through well-designed gating mechanisms enables the models to capture richer semantic and visual context, leading to better generalization and improved task performance. The experimental results indicate that leveraging a multimodal KG such as AlphaKG within the AlphaKG framework is a promising avenue for enhancing various natural language processing and computer vision applications.

Overall, our comprehensive evaluation validates the effectiveness of AlphaKG in both intrinsic (link prediction) and extrinsic (NER, VSD) tasks, setting the stage for future research on integrating external multimodal knowledge into neural architectures.

5. Conclusions and Future Directions

In this work, we presented a systematic investigation comparing various tuple-based and graph-based architectures for learning robust multimodal representations for the AlphaKGknowledge graph. Our study revealed that integrating the rich visual information (illustrative images) and descriptive textual glosses available at each node significantly enhances the quality of node and entity embeddings, as measured on the link prediction task. In particular, our best-performing method—AlphaKG, a hybrid approach that merges the strengths of both tuple- and graph-based paradigms—demonstrated its efficacy by yielding substantial improvements in downstream applications. For example, on crosslingual visual verb sense disambiguation, AlphaKG improved accuracy by 2.5% compared to a strong baseline, while in multilingual named entity recognition, performance gains ranged from 0.3% to 0.7% in F1 score. These results were achieved using relatively simple downstream architectures, suggesting that further gains might be obtained by exploring more sophisticated integration strategies.

Beyond our empirical findings, we introduced an enhanced training objective that combines standard negative sampling with an auxiliary regularization term designed to encourage smoothness in the learned embedding spaces. This additional refinement underscores the potential of carefully designed loss functions to further improve the quality of multimodal representations in the AlphaKG framework.

Our findings motivate several promising avenues for future research. First, it would be valuable to extend our evaluation to a broader set of downstream tasks. For instance, integrating AlphaKG with vision-centric tasks—such as object detection or scene understanding—could reveal further benefits of leveraging structured multimodal knowledge. Additionally, challenging generative tasks like image captioning, where the fusion of visual and textual modalities is critical, may also benefit from the rich representations produced by AlphaKG.

Another promising direction involves applying our framework to other knowledge graphs that encode different types of information. For example, incorporating commonsense knowledge from resources such as ConceptNet could enable AlphaKG to handle even more diverse and complex reasoning scenarios. In this case, one could adapt our hybrid training objective to jointly optimize for multiple types of semantic relationships, thereby learning a unified representation that captures both factual and commonsense dimensions.

Furthermore, an exciting line of inquiry is the integration of structured knowledge graph representations within large-scale retrieval-based language models. By dynamically retrieving and incorporating external structured knowledge during inference, such models could achieve enhanced contextual understanding without the need to store all knowledge implicitly within their parameters. This could be realized through a modular approach where AlphaKG serves as an external memory component that interfaces with large pretrained models via attention-based mechanisms.

In summary, our work demonstrates that the AlphaKG framework effectively bridges visual and textual modalities within a structured knowledge graph, yielding improved performance across both intrinsic (link prediction) and extrinsic (NER, VSD) evaluation tasks. The encouraging results and the modular nature of our approach open up a wide spectrum of future work, ranging from the incorporation of additional data modalities and knowledge sources to the integration with large-scale language models. We anticipate that these directions will not only further enhance the efficiency and accuracy of multimodal representation learning but also contribute to the development of more robust and adaptable AI systems.