Xuzeng He

#### Latest findings in pre-training graphs and using them for link recommendation

<figure>
<img src="https://cdn-images-1.medium.com/max/894/0*r4KO-R53KSbl-SaM" />
<figcaption>Source: Generated using DeviantArt’s DreamUp</figcaption>
</figure>

### Author

· Xuzeng He (**ORCID:**
[0009--0005--7317--7426](https://orcid.org/0009-0005-7317-7426))

### Introduction

A graph, in short, is a description of items linked by relations, where
the items of a graph are called nodes (or vertices) and their relations
are called edges (or links). Examples of graphs can include social
networks (e.g. Instagram) or knowledge graphs (e.g. Wikipedia).

In Instagram for instance, a graph might be used to represent users as
nodes and their relationships(who follows whom) as edges. This graph can
then be used to construct a friend recommendation model to suggest users
one might want to follow based on the connections they have. In the case
of Wikipedia, a knowledge graph can use nodes to represent articles and
use edges to represent connections between these articles.

Nowadays, There is a rising trend in the research of using Machine
Learning techniques on graphs to solve various kinds of problems, such
as friend recommendations on a social network. In this post, we
introduce two latest findings about the pre-training of graphs and how
to use them for link recommendation.

### WAS

In recent years, graph pre-training has become very successful in
learning how to represent data in graphs. This method learns from large
amounts of data that don't have labels and uses this knowledge to help
with specific tasks that come later. In the case of social networks like
Instagram, pre-training enables the graph to learn from vast amounts of
untagged user interactions to enhance some specific tasks such as friend
recommendation or post recommendation.

However, existing methods only have focused on **importance weighing**
(how to weigh the importance of the selected tasks) and have all
overlooked **task selecting** (how to select an optimal combination of
different tasks from a given task pool based on task compatibility).
Importance weighting involves evaluating how important different tasks
are. In the scenario of the Instagram graph, one can weigh the "friend
recommendation" task as more important than the "post recommendation"
task to train the graph to become more suitable for friend
recommendation.

As for the compatibility issue from task selecting, it means the
possible conflicts between different tasks, which cannot be resolved by
simply weighing all tasks. For example, imagine two tasks:

- Task A: Identify influential users in the network.
- Task B: Recommend posts with high potential for virality. Task A might
 benefit from analyzing the entire network structure, identifying users
 with many connections, and high engagement. However, Task B might
 focus on specific features of posts themselves (hashtags, visuals) and
 user interactions with similar posts. While some overlap might exist,
 these tasks rely on partially independent data within the graph.
 Training for both simultaneously might not leverage the full potential
 of each task's specific data requirements and can deprive the existing
 methods of the ability to keep gaining performance growth as the task
 pool grows larger. In this case, we need to select the best sets of
 tasks we want considering their compatibility with each other.

To solve this issue, a novel framework, **Weigh And Select** (**WAS**)
for task selecting and importance weighing was proposed. The two
collaborative processes are combined in decoupled siamese networks (a
neural network uses the same weights while working in tandem on two
different input vectors to compute comparable output vectors), where:

1. An optimal combination of tasks is selected for each instance based
 on a sampling distribution calculated based on task compatibility.
2. Task weights are then calculated for the selected tasks according to
 their importance.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*pDWRzsZufPLRfq4x" />
<figcaption>Overall Workflow of WAS. Firstly, multiple teachers are
trained with different pre-training tasks. The teacher’s representations
are then passed to two modules (Selecting and Weighing) to get the
selecting results and initial weights for each instance. Only those
selected teachers are weighed to get weights and distil the integrated
distributions into the student.</figcaption>
</figure>

Extensive experiments on 16 datasets show that WAS can achieve
comparable performance to other leading counterparts for both node-level
and graph-level tasks, opening up exciting possibilities for future
research.

### AIS

Online social networks (**OSNs**) are becoming an increasingly powerful
medium for disseminating useful content. An important scientific problem
related to OSN is **Influence Maximisation** (**IM**), which aims to
select a set of nodes in a social network as the sources of influence
spread to maximise the expected number of influenced nodes. For
instance, one can imagine selecting a popular Instagram influencer as a
source to post about a new product where the goal is to reach as many
followers as possible, who are the influenced nodes in this context. One
way to boost the influence spread in an OSN is by increasing the
connectivity among users using a recommendation system. For example, OSN
platforms like Twitter use "people recommendations" to increase
connectivity.

In contrast to the previous works of influence maximisation where the
network topology remains unchanged, a new algorithm called **AIS**
(**Augmenting the Influence of Seeds**) was proposed recently that
focuses on recommending links that can augment the social influence of a
target group of users. This method is inspired by the groundbreaking
[Reverse Influence Sampling (RIS)](https://hautahi.com/im_ris) method
and uses two newly developed innovative techniques, including an
efficient estimator (estimate the influence of the node) and an
accelerated sampling approach (sampling a set of nodes and trying to
maximise the influence on these nodes) tailored for this [Influence
Maximisation with Augmentation
(IMA)](https://www.sciencedirect.com/science/article/pii/S2590005622000972)
problem. These enhancements make the method more effective and quicker
than existing strategies, allowing it to spread influence more
efficiently across networks.

Extensive experiments on various datasets show that this algorithm
outperforms the baselines in terms of approximation ratio and running
time, and is the first method that can be applied to large datasets
without any compromise of theoretical assurance.

### Conclusion

The usage of graphs in Data Science witnessed significant advancement,
which can open multiple exciting possibilities for different research
directions. In this post, we discuss two latest findings about the
pre-training of graphs and how to use them for link recommendation.
Moreover, we have also shared some promising scopes that could be used
for future exploration of these approaches. Through continuous
investigation and refinement, we believe that the use of graphs can open
up exciting opportunities for us.

### References

- Fan, T., Wu, L., Huang, Y., Lin, H., Tan, C., Gao, Z., & Li, S. Z.
 (2024). Decoupling Weighing and Selecting for Integrating Multiple
 Graph Pre-training Tasks (Version 1). arXiv.
 <https://doi.org/10.48550/ARXIV.2403.01400>
- Chen, X., Song, Y., & Tang, J. (2024). Link Recommendation to Augment
 Influence Diffusion with Provable Guarantees (Version 1). arXiv.
 <https://doi.org/10.48550/ARXIV.2402.19189>

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Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training Tasks

Link Recommendation to Augment Influence Diffusion with Provable Guarantees

Latest findings in pre-training graphs and using them for link recommendation Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction A graph, in short, is a description of items linked by relations, where the items of a graph are called nodes (or vertices) and their relations are called edges (or links). Examples of graphs can include social networks (e.g. Instagram) or knowledge graphs (e.g. Wikipedia). In Instagram

Working with Graphs: Pre-training and Application

Australian National University

Amanda Kau

#### Techniques to integrate Knowledge Graphs into Language Models

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*jxTYxaRhYXh2sBji" />
<figcaption>Image generated with Google’s Gemini, 9
April 2024.</figcaption>
</figure>

### **Author**

- Amanda Kau **(ORCID:**
 [**0009--0004--4949--9284**](https://orcid.org/0009-0004-4949-9284)**)**

### Introduction

Both knowledge graphs (KGs) and pre-trained language models (PLMs) have
gained popularity due to their ability to comprehend world knowledge and
their broad applicability. KGs are instrumental in applications like
search engines, evident from Google's Knowledge Graph. On the other
hand, popular PLMs like BERT and GPT excel in a variety of natural
language tasks. More recently, large language models (LLMs), a type of
PLM, have taken the world by storm. PLMs are models pre-trained on large
datasets and LLMs are essentially PLMs which are characterised by their
larger size and complexity. For example, OpenAI's GPT-3 is a LLM
boasting 175 billion parameters. Throughout this article, we will
interchangeably use LLMs and PLMs to refer to these models.

In our [previous
article](https://medium.com/@researchgraph/automated-knowledge-graph-construction-with-large-language-models-150512d1bc22)
titled "Automated Knowledge Graph Construction with Large Language
Models," we discussed the complementary relationship between KGs and
PLMs to harness the strengths of both sides. KGs explicitly capture
information about entities and their relationships, while PLMs contain
implicit knowledge learnt from their vast and diverse training datasets.
Combining them into a single model gives us knowledge graph enhanced
pre-trained language models (**KGPLMs**).

KGPLMs can be categorised into three types: before-training enhancement,
during-training enhancement, and post-training enhancement models. These
are distinguished by the stage at which KGs are integrated into the
pre-training phases of PLMs. We will delve deeper into these three
models in the following sections.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*d64sChmy6DWZ-C4H" />
<figcaption>KGPLMs are categorised into before-training,
during-training, and post-training enhancement models. Source: Yang et
al., 2023 <a
href="https://doi.org/10.48550/ARXIV.2306.11489">https://doi.org/10.48550/ARXIV.2306.11489</a></figcaption>
</figure>

### Before-training Enhancement Models

These models convert text data and KG triples into the same format for
input into PLMs. There are two challenges when attempting to do so: the
heterogeneous embedding space and knowledge noise. The former refers to
the incompatible formats of text and KG data, while the latter involves
inserting unrelated knowledge that changes the intended meaning of the
text. For domains with limited training data, these methods are
effective in supporting PLMs, at the cost of increased computational
resources, pre-training time, and the possible introduction of knowledge
noise. Nonetheless, they can improve the reasoning capabilities of PLMs
without augmenting PLM characteristics like model size. Below, we
discuss four techniques utilised by before-training enhancement models.

#### **1. Expand Input Structures** {#expand-input-structures}

<figure>
<img src="https://cdn-images-1.medium.com/max/878/0*txQl_SN-cgT1YS1s" />
<figcaption>The structure of K-BERT, which converts input sentences to a
graph structure and adds in supporting knowledge from KGs. Source: Liu
et al., 2020 <a
href="https://doi.org/10.1609/aaai.v34i03.5681">https://doi.org/10.1609/aaai.v34i03.5681</a></figcaption>
</figure>

This technique converts text input into a graph structure which can be
merged with an existing KG. The merged KG can subsequently be converted
into text for PLM input or encoded in another way. An example is in
**K-BERT**, which converts input sentences into sentence trees by
injecting relevant knowledge from a KG through the knowledge layer. The
sentence tree is encoded into tokens and a visibility matrix controls
knowledge noise by preventing the influence of irrelevant neighbouring
tokens. By changing the supporting KG in the knowledge layer, K-BERT
performed well in specific domains like medicine.

#### **2. Enrich Input Information** {#enrich-input-information}

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*ulHcA-Ix6K5gXE9k" />
<figcaption>Given an input sentence “Beyonce lives in Los Angeles”, LUKE
is trained to predict masked words (“lives” and “Angeles”) and entities
(“Los Angeles”). It subsequently outputs a vector representation for
each word or entity. Source: Yamada et al., 2020 <a
href="https://doi.org/10.48550/arXiv.2010.01057">https://doi.org/10.48550/arXiv.2010.01057</a></figcaption>
</figure>

In this method, entity embeddings are combined with text embeddings in
the model. The example shown above is of a transformer utilising
**LUKE** (Language Understanding with Knowledge-based Embeddings). It is
an extension of [BERT](https://huggingface.co/blog/bert-101), a masked
language model that randomly masks tokens by replacing words with the
\[MASK\] token, and encodes the tokens and positions of the given
sentence. Since BERT only encodes words, but entities like ""Los
Angeles" can span multiple words, LUKE was trained to mask and predict
entities as well. In this case, input information was enriched to
contain entities in addition to words.

#### **3. Generate New Data** {#generate-new-data}

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*ITvjsYCSMVQ4aMSB" />
<figcaption>An example of a subset of ATOMIC, which focuses on if-then
relations. Source: Sap et al., 2019 <a
href="https://doi.org/10.1609/aaai.v33i01.33013027">https://doi.org/10.1609/aaai.v33i01.33013027</a></figcaption>
</figure>

Here, KG knowledge is used to generate synthetic text which is then
injected into a PLM. For example, **ATOMIC** (ATlas Of MachIne
Commonsense) is a dataset that focuses on capturing three types of
if-then relations: If-Event-ThenMental-State, If-Event-Then-Event, and
If-Event-Then-Persona. A PLM was trained to generate a target sequence
given an event phrase and an inference dimension based on the KG. For
instance, when prompted with "PersonX wins the title" and "As a result,
X wants to", the PLM could generate inferences about actions or
consequences such as "celebrate", "brag" and "congratulate themselves".

#### **4. Optimise Word Masks** {#optimise-word-masks}

Masked language models like [BERT](https://huggingface.co/blog/bert-101)
randomly mask words during training. However, there may be correlation
between consecutive words that can be lost if one of the words is
randomly masked. For example, if "Los Angeles" is masked and becomes
"Los \[MASK\]", the model might fail to understand semantics and treat
"Los" and "Angeles" separately despite their obvious correlation.
**ERNIE** (Enhanced Language RepresentatioN with Informative Entities)
avoids this by identifying entities in text and aligning them with
corresponding entities in KGs. The model was trained by randomly masking
entities instead of single words and asked to choose appropriate
entities from KGs to fill in the masked positions.

### During-training Enhancement Models

While before-training enhancement models focused on incorporating KG
data into text data, during-training enhancement models give PLMs
knowledge during training by modifying their encoders or training tasks.
This allows PLMs to learn from text and KGs simultaneously. Specialised
information or modules can be added in for specific domains or tasks.
However, these models usually have larger architectures, hence requiring
longer training times. They are limited to the scope of the training
data and are prone to overfitting due to larger and more complex
architectures. During-training enhancement models are most beneficial
when multiple complex tasks are required or for knowledge-grounded
tasks. There are four possible approaches to achieving this.

#### **1. Incorporate Knowledge Encoders** {#incorporate-knowledge-encoders}

Models may incorporate various encoders for text, KGs, or a hybrid of
these data formats to obtain embeddings for the model. For example, the
previously mentioned **ERNIE** model uses a textual encoder (T-Encoder)
and a knowledgeable encoder (K-Encoder). First, the T-Encoder captures
lexical information from input tokens by encoding text similarly to
BERT, utilising the token, segment and positional embeddings. The graph
structure of KGs is encoded with algorithms like
[TransE](https://paperswithcode.com/method/transe). Both textual and
graph encodings are given to the K-Encoder to fuse the information into
a unified representation.

#### **2. Insert Knowledge Encoding Layers** {#insert-knowledge-encoding-layers}

This method involves inserting layers into PLMs that inject knowledge
from KGs. **KnowBERT** by Peters et al. (2019) achieves this by
inserting a Knowledge Attention and Recontextualisation (KAR) mechanism
between two layers in the middle of the BERT architecture. This
additional layer takes in the contextual representations from the
previous BERT layer, uses an entity linker to obtain relevant entity
embeddings from KGs, and outputs knowledge-enhanced embeddings to the
next BERT layer.

#### **3. Add Independent Adapters** {#add-independent-adapters}

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*UNxNiGNKyIXn-rXw" />
<figcaption>(a) PLMs consist of a series of transformer layers (TRM),
meaning parameters for the entire model are re-trained for every type of
injected knowledge. (b) Instead, each adapter in K-ADAPTER, consisting
of adapter layers (KIA), is independent and retains the original PLM
parameters as well. Source: Wang et al., 2020 <a
href="https://doi.org/10.48550/ARXIV.2002.01808">https://doi.org/10.48550/ARXIV.2002.01808</a></figcaption>
</figure>

Whilst other methods require PLM parameter updates, the **K-ADAPTER**
framework seeks to preserve the PLM's parameters to support the
injection of multiple types of knowledge without entangling the
resulting representations. This allows the effects of each knowledge
type to be studied independently. These adapters, which are compact
neural models, accept hidden states from the PLM's intermediate layers
as input, as shown in the diagram above. In K-ADAPTER, the authors
aligned Wikipedia text to Wikidata triplets and injected this knowledge
using such an adapter.

#### **4. Modify the Pre-training Task** {#modify-the-pre-training-task}

Rather than random masking, masked entity modelling may be adopted
instead. The previously discussed **ERNIE** masks entities rather than
individual words to capture entities that span multiple words. Other
methods integrate knowledge representation learning to concurrently
update knowledge representations and PLM parameters. For instance, the
**KEPLER** (Knowledge Embedding and Pre-trained LanguagE Representation)
model jointly optimises the knowledge embedding and masked language
modelling objectives, by following existing PLM approaches as well as
encoding entity descriptions as entity embeddings to represent
relational facts in knowledge graphs.

### Post-training Enhancement Models

Post-training enhancement is achieved by fine-tuning PLMs on additional
data or tasks, which is a relatively low-cost and straightforward way of
tailoring PLM outputs to domain-specific tasks. However, the data needs
to be labelled and crafting prompts requires prior knowledge and
external resources. Furthermore, prompts are designed to constrain the
PLM's generations, but may limit its flexibility during text generation.
There are two methods used for post-training enhancement.

#### **1. Fine-tune PLMs with knowledge** {#fine-tune-plms-with-knowledge}

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*xNJ7wpsUnI_0rgrX" />
<figcaption>The framework of KagNet. Source: Lin et al., 2019 <a
href="https://doi.org/10.48550/arXiv.1909.02151">https://doi.org/10.48550/arXiv.1909.02151</a></figcaption>
</figure>

In the example of **KagNet** shown in the above image, an initial
question-answer pair is encoded by a PLM such as BERT. Additional
knowledge is included in the form of a schema graph containing relevant
knowledge to the given question and answer. This is retrieved from
external knowledge graphs like ConceptNet. The graph is encoded by a
knowledge-aware graph network module. Finally, KagNet generates a
plausibility score, allowing the model to pick the answer with the
highest score as the most likely answer.

#### **2. Generate Knowledge-based Prompts** {#generate-knowledge-based-prompts}

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*okg6DUAUA_5LBb7h" />
<figcaption>A knowledge-to-text framework for knowledge-enhanced
commonsense question-answering. Source: Bian et al., 2021 <a
href="https://doi.org/10.48550/arXiv.2101.00760">https://doi.org/10.48550/arXiv.2101.00760</a></figcaption>
</figure>

An example of this approach is found in the knowledge-to-text framework
proposed by Bian et al. (2021). Given a question and a series of
possible answers, the first knowledge retrieval step obtains relevant
facts from a KG. These facts are transformed into text in the second
knowledge-to-text transformation step via template-based,
paraphrasing-based, and retrieval-based algorithms. Finally, the
obtained text is fed into a PLM for it to predict the answer.

### Conclusion

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*tzAApAg78T6czLZ3" />
<figcaption>Summary of the types of KGPLMs, corresponding techniques and
example models. Source: Yang et al., 2023 <a
href="https://doi.org/10.48550/ARXIV.2306.11489">https://doi.org/10.48550/ARXIV.2306.11489</a></figcaption>
</figure>

In summary, this article explored three types of knowledge graph
enhanced pre-trained language models (**KGPLMs**): before-training
enhancement, during-training enhancement, and post-training enhancement
models. There were several methods described in each model type, as
listed in the table above. We discussed the advantages and limitations
of each type of KGPLM which determines the situations they are suitable
for. However, these methods are definitely not mutually exclusive. For
instance, the ERNIE model showcased the optimisation of word masks,
incorporated knowledge encoders, and modified pre-training tasks.
Although we only focused on a few models to illustrate these methods,
numerous models utilise these techniques in diverse ways, as seen in the
table above. There are also newer models that have not been included in
the article that continue to expand the capabilities of KGPLMs and will
continue to do so.

### **References**

- Yang, L., Chen, H., Li, Z., Ding, X., & Wu, X. (2023). Give Us the
 Facts: Enhancing Large Language Models with Knowledge Graphs for
 Fact-aware Language Modeling (Version 2). arXiv.
 <https://doi.org/10.48550/ARXIV.2306.11489>
- Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., & Wang, P.
 (2020). K-BERT: Enabling Language Representation with Knowledge Graph.
 In Proceedings of the AAAI Conference on Artificial Intelligence (Vol.
 34, Issue 03, pp. 2901--2908). Association for the Advancement of
 Artificial Intelligence (AAAI).
 <https://doi.org/10.1609/aaai.v34i03.5681>
- Yamada, I., Asai, A., Shindo, H., Takeda, H., & Matsumoto, Y. (2020).
 LUKE: Deep Contextualized Entity Representations with Entity-aware
 Self-attention (Version 1). arXiv.
 <https://doi.org/10.48550/ARXIV.2010.01057>
- Sap, M., Le Bras, R., Allaway, E., Bhagavatula, C., Lourie, N.,
 Rashkin, H., Roof, B., Smith, N. A., & Choi, Y. (2019). ATOMIC: An
 Atlas of Machine Commonsense for If-Then Reasoning. In Proceedings of
 the AAAI Conference on Artificial Intelligence (Vol. 33, Issue 01, pp.
 3027--3035). Association for the Advancement of Artificial
 Intelligence (AAAI). <https://doi.org/10.1609/aaai.v33i01.33013027>
- Zhang, Z., Han, X., Liu, Z., Jiang, X., Sun, M., & Liu, Q. (2019).
 ERNIE: Enhanced Language Representation with Informative Entities
 (Version 3). arXiv. <https://doi.org/10.48550/ARXIV.1905.07129>
- Peters, M. E., Neumann, M., Logan, R. L., Schwartz, R., Joshi, V.,
 Singh, S., & Smith, N. A. (2019). Knowledge Enhanced Contextual Word
 Representations (Version 2). arXiv.
 <https://doi.org/10.48550/ARXIV.1909.04164>
- Wang, R., Tang, D., Duan, N., Wei, Z., Huang, X., ji, J., Cao, G.,
 Jiang, D., & Zhou, M. (2020). K-Adapter: Infusing Knowledge into
 Pre-Trained Models with Adapters (Version 5). arXiv.
 <https://doi.org/10.48550/ARXIV.2002.01808>
- Wang, X., Gao, T., Zhu, Z., Zhang, Z., Liu, Z., Li, J., & Tang, J.
 (2019). KEPLER: A Unified Model for Knowledge Embedding and
 Pre-trained Language Representation (Version 3). arXiv.
 <https://doi.org/10.48550/ARXIV.1911.06136>
- Lin, B. Y., Chen, X., Chen, J., & Ren, X. (2019). KagNet:
 Knowledge-Aware Graph Networks for Commonsense Reasoning (Version 1).
 arXiv. <https://doi.org/10.48550/ARXIV.1909.02151>
- Bian, N., Han, X., Chen, B., & Sun, L. (2021). Benchmarking
 Knowledge-Enhanced Commonsense Question Answering via
 Knowledge-to-Text Transformation (Version 2). arXiv.
 <https://doi.org/10.48550/ARXIV.2101.00760>

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Techniques to integrate Knowledge Graphs into Language Models

 Author

Amanda Kau

 (ORCID:


 0009–0004–4949–9284


 )

Introduction Both knowledge graphs (KGs) and pre-trained language models (PLMs) have gained popularity due to their ability to comprehend world knowledge and their broad applicability.

Rogue Scholar Posts

Working with Graphs: Pre-training and Application

A Deep Dive Into Knowledge Graph Enhanced Pre-trained Language Models