Australian National University

Amanda Kau

**Understanding Knowledge Networks From A Graph Perspective**

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*EfInqfXKiK2qI4IU" />
<figcaption>Source: <a
href="https://unsplash.com/photos/open-book-lot-Oaqk7qqNh_c">https://unsplash.com/photos/open-book-lot-Oaqk7qqNh_c</a></figcaption>
</figure>

#### **Author:**

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

Since 2020, over ten million scholarly articles have been published
annually. To put that into perspective, say all ten million articles
were released on the first day of the year. To get through them all, you
would have to read about 27,397 articles every single day. Even if you
were only interested in a specific field like artificial intelligence
(AI), with 242,375 articles about AI in AceMap's database in 2020 alone,
that is still 664 articles daily.

We all agree that this is a staggering volume of articles that poses
significant issues. For instance, governments struggle to choose which
endeavours to allocate research funding to. Organisations face
difficulties in recognising influential works deserving of awards like
Turing Awards or Nobel Prizes. Researchers grapple with identifying
research trends to do further exploration. Several factors make this
more challenging:

- The structure of platforms that researchers commonly turn to, such as
 academic search engines like Google Scholar or pre-print platforms
 like arXiv, lack the structure to easily analyse interactions between
 publications and academic entities. Imagine you were given a long list
 of PDF documents, it would be hard to connect them all to spot trends
 or to compare them.
- Current metrics like citation counts only partially capture a
 paper's impact.
- Tools for citation analysis, such as CiteSpace and CitNetExplorer, do
 not specifically identify or quantify the influence of publications.

This article will review the [**AceMap**](https://www.acemap.info/)
system and introduce several features of the AceMap interface. Then, the
**Scientific X-ray** framework, designed to quantify the influence of
publications using AceMap data, will be introduced with some interesting
findings.

### What Is AceMap?

[AceMap](https://www.acemap.info/) is a database system that connects
academic entities in a knowledge graph called **AceKG** (AceMap
Knowledge Graph). Containing more than 221 million papers from 1800 to
2021, by more than 103 million authors across 767 thousand different
research fields, AceMap was designed to address four fundamental
questions:

1. How do we effectively represent complex interactions between
 entities?
2. What is an intuitive method to represent the structure of the
 scholarly network?
3. How can we measure the knowledge possessed by entities for
 comparison?
4. How can we analyse these networks and present the findings in a
 digestible way?

We will briefly discuss each of these aspects in relation to AceMap's
features.

#### 1. How do we effectively represent complex interactions between entities? {#how-do-we-effectively-represent-complex-interactions-between-entities}

AceMap is built on AceKG, and a diagram of its main components is shown
in the image below. The graph comprises five classes of entities
represented by icons --- papers, authors, venues, fields of study and
institutes --- totalling over 114 million entities. Each entity has
several properties such as name or publish date, represented by hollow
circles, while the relationships between entities are represented by
black arrows.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*9OIP3g3tSUvJwqQ8" />
<figcaption>The AceKG Ontology. Source: Wang et al., 2024 <a
href="https://doi.org/10.48550/arXiv.2403.02576">https://doi.org/10.48550/arXiv.2403.02576</a></figcaption>
</figure>

This graphical representation allows further connections to be made:

- **Topic Tree:** Hierarchical relationships can be formed by studying
 co-occurrence of concepts. For instance, the field of model-based
 reasoning, which is an inference method for artificial intelligence
 systems to model the real world, has the following parent and
 children topics.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*Rvvq-1yAr3B0MYbW" />
<figcaption>Example of parent and children topics for topic “Model-based
reasoning”. Obtained on 14 March 2024 from AceMap, <a
href="https://www.acemap.info/field/2044117001">https://www.acemap.info/field/2044117001</a></figcaption>
</figure>

- **Tracing and Evolution Tree:** Accessible from the IdeaReader section
 of each publication's webpage, the tracing tree details past papers
 that motivated the current paper, while the evolution tree maps out
 the current paper's influence on subsequent works.
- **Paper Map of Field:** Papers that define every field, like
 "Model-based reasoning" in the example here, are listed out based on
 first authors. The links between these papers are represented as well.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*gNqHzW9Je24S2CPr" />
<figcaption>Paper map of field for the field “Model-based reasoning”.
Obtained on 14 March 2024 from AceMap, <a
href="https://www.acemap.info/map/paperMap/2044117001?type=papermap_4">https://www.acemap.info/map/paperMap/2044117001?type=papermap_4</a></figcaption>
</figure>

- **DeepReport:** This novel research assistant system analyses
 co-occurrence and convergence of concepts to identify new ideas by
 combining current ones.

The details of the many natural language processing, data extraction,
and other machine learning techniques that were employed to construct
AceKG can be found in the AceMap paper itself (see the references in the
last section of this post).

#### 2. What is an intuitive method to represent the structure of the scholarly network? {#what-is-an-intuitive-method-to-represent-the-structure-of-the-scholarly-network}

Existing visualisation methods were inadequate to display the vastness
of AceKG, as they handle only up to hundreds of thousands of nodes, far
from AceKG's millions. Instead, a new method called **VSAN**
(Visualising Super-Large Academic Networks) was proposed. Essentially,
the graph was divided into smaller segments which were displayed
separately and stitched together.

Four million nodes and 36 million edges from the DBLP computer science
bibliography were mapped onto a galaxy-like graph. In the image below,
in panel (c), computer vision conferences like CVPR and ICCV were
clearly clustered. Panels (d) and (e) show topic clusters in the
computer networks field and in an information theory journal.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*MgbUg4qYyAQu006g" />
<figcaption>Galaxy-like structures could be mapped out using VSAN.
Source: Wang et al., 2024 <a
href="https://doi.org/10.48550/arXiv.2403.02576">https://doi.org/10.48550/arXiv.2403.02576</a></figcaption>
</figure>

#### 3. How can we measure the knowledge possessed by entities for comparison? {#how-can-we-measure-the-knowledge-possessed-by-entities-for-comparison}

Instead of using citation-based metrics to measure knowledge, the
**Knowledge Quantification Index** (KQI) based on entropy was proposed.
A higher KQI is assigned to publications with better acceptability and
dependability, meaning the paper was derived from reliable parent
knowledge and inspires numerous subsequent works. KQI offers several
advantages over other metrics:

- It considers the flow of ideas from paper to paper.
- It cannot be manipulated by self-citation or citation stacking,
 wherein authors repeatedly cite their own works to inflate
 citation counts.
- It can pinpoint valuable knowledge.

In fact, when ordering the top 10,000 authors by KQI from their computer
science dataset, the creators of AceMap identified 71 Turing Award
winners, amounting to 96% of all winners up to 2020.

#### 4. How can we analyse these networks and present the findings in a digestible way? {#how-can-we-analyse-these-networks-and-present-the-findings-in-a-digestible-way}

Besides the numerous graphs and charts presented on the AceMap website,
there is an additional feature called **IdeaReader**. In a snapshot of
IdeaReader's interface below, information regarding the target paper is
provided to the user in panel (1), including the list of authors,
abstract and several metrics. IdeaReader identifies papers most likely
to inspire or be influenced by the target publication. Automatically
generated summaries of the top five papers in these two groups are
provided in panels (2) and (3). The flow of ideas from papers before and
after the target paper are also given in panel (4).

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*lGO4jmCv1jxCV18uab1BFw.png" />
<figcaption>Information provided by IdeaReader for the paper “Attention
Is All You Need”. Source: Wang et al., 2024 <a
href="https://doi.org/10.48550/arXiv.2403.02576">https://doi.org/10.48550/arXiv.2403.02576</a></figcaption>
</figure>

### Scientific X-ray: A Framework Built on AceMap's Database

In 2022, the **Scientific X-ray** framework was proposed by Li and
colleagues to help evaluate the development and potential of ideas, to
identify them amidst the vast array of publications. Citation networks
(depicted as tree structures in the image) were constructed for each
target publication (depicted as pink circles). Distinct network
structures were observed for different publications, like the top
structure in panel (b) in the image below representing summative work
like textbooks, and the bottom structure showing innovative research.
The 'idea' tree in panel (c) was formed to represent the flow of ideas
to subsequent papers. Based on these trees, three metrics were
introduced:

1. **Knowledge Entropy** (KE): Quantifies knowledge within a paper
 based on its tree's structure.
2. **Valid Depth** (VD): Measures the degree of development of the
 target publication's idea.
3. **Development Potential Index** (DPI): Assesses future potential of
 the target publication's idea.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*i6NYZuHnMSk-kRsClCP3JA.png" />
<figcaption>The framework of Scientific X-ray. Source: Li et al., 2022
<a
href="https://doi.org/10.1371/journal.pone.0275192">https://doi.org/10.1371/journal.pone.0275192</a></figcaption>
</figure>

Noteworthy findings were discovered using these metrics. Firstly, a
significant proportion (31.11%) of publications had a VD of zero,
showing challenges in new knowledge being adopted. Secondly, although
citation counts could increase indefinitely, 99% of publications could
not exceed the upper bound of influence at a VD of six. Furthermore, the
evolution of ideas across disciplines followed six distinct patterns
automatically derived from idea trees:

1. Summative work, like textbooks, lacked the ability to inspire future
 works or foster new ideas.
2. Continued development of a target article's idea needed subsequent
 works which were also influential.
3. A relay of influential subsequent works was necessary for the
 continued development of the target publication's idea.
4. Subsequent work that overshadowed the target publication caused the
 original idea to lose relevance and attention.
5. If two branches of thought followed from the target publication, the
 dominance of one branch impeded development in the second branch.
6. Publications approaching the upper limit of development required
 many influential subsequent works to sustain progress.

Meanwhile, by assessing DPI, Scientific X-ray accurately identified 40
out of 49 Nobel Prize topics in Physics, Chemistry, and Physiology or
Medicine from 1900 to 2016, demonstrating the effectiveness of DPI in
predicting the influence of scientific ideas.

### Conclusion

We have introduced the **AceMap** system, which relies on the knowledge
graph **AceKG**, showcasing various features including the tracing and
evolution trees, IdeaReader, and DeepReport. A new visualisation
technique for massive graphs, **Visualising Super-Large Academic
Networks** (VSAN), was presented. We also discussed the **Scientific
X-ray** framework based on AceMap data and presented six interesting
patterns regarding idea development in scientific research. Furthermore,
we explored four novel metrics --- the **Knowledge Quantification
Index** (KQI), **Knowledge Entropy** (KE), **Valid Depth** (VD), and
**Development Potential Index** (DPI) --- as means to measure the
knowledge, development, and potential of publications.

#### **References**

Wang, X., Fu, L., Gan, X., Wen, Y., Zheng, G., Ding, J., Xiang, L., Ye,
N., Jin, M., Liang, S., Lu, B., Wang, H., Xu, Y., Deng, C., Zhang, S.,
Kang, H., Wang, X., Li, Q., Guo, Z., ... Zhou, C. (2024). AceMap:
Knowledge Discovery through Academic Graph (Version 1). arXiv.
<https://doi.org/10.48550/ARXIV.2403.02576>

Li, Q., Wang, X., Fu, L., Wang, J., Yao, L., Gan, X., & Zhou, C. (2022).
Scientific X-ray: Scanning and quantifying the idea evolution of
scientific publications. In E. Caroselli (Ed.), PLOS ONE (Vol. 17, Issue
9, p. e0275192). Public Library of Science (PLoS).
<https://doi.org/10.1371/journal.pone.0275192>

![](https://medium.com/_/stat?event=post.clientViewed&referrerSource=full_rss&postId=1a6f5ee7921b){width="1"
height="1"}

Understanding Knowledge Networks From A Graph Perspective


 Author:


 Amanda Kau (ORCID:


 0009–0004–4949–9284


 )

Since 2020, over ten million scholarly articles have been published annually. To put that into perspective, say all ten million articles were released on the first day of the year.

Uncovering The Secrets Behind The Most Influential Scholarly Publications With AceMap

Dhruv Gupta

**Unlocking the power of knowledge graphs in research catalogues: A deep
dive into OpenAlex**

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*vz8OrFW6N2e-vdwz" />
<figcaption>Image from Unsplash.</figcaption>
</figure>

### **Author:**

- **Dhruv Gupta (ORCID:**
 [**0009--0004--7109--5403**](https://orcid.org/0009-0004-7109-5403)**)**

Clive Humby, in 2006 rightly said, "Data is the new oil". With data
being present everywhere, it has never been more valuable. With
companies using data to make smarter decisions its storage is slowly
becoming an issue. This is where Knowledge Graphs come into the
equation. In this article, we will talk about what knowledge graphs are
followed by a deep dive into Open Alex.

### **What Are Knowledge Graphs?**

The term knowledge graph was first used by Google in 2012 to refer to
its knowledge base. These graphs help organise data from different
sources, capture information about stakeholders and try to build
connections among them. With applications ranging from search engines,
chatbots, and recommendation systems knowledge graphs are everywhere.
Knowledge graphs are being widely used in the field of machine learning
as well. They complement machine learning tasks by:

- Reducing the need for large labelled datasets
- Allow transfer learning
- Allows cross-domain learning by building connections

<figure>
<img src="https://cdn-images-1.medium.com/max/589/0*GuNQ6pxjNRAwLIQX" />
<figcaption>An example of a Knowledge Graph. Source: Research Graph
Foundation <a
href="https://researchgraph.org/">https://researc​​hgraph.org/</a>.</figcaption>
</figure>

The use of knowledge graphs has seen an increase in text-related tasks.
Big graphs can provide background knowledge, human-like awareness and
concepts.

In addition to this knowledge-graphs are being used a lot for research
purposes as well. A lot of research databases such as OpenAlex,
Dimensions, and the Lens are being utilised to help researchers. These
tools help the researchers by giving them appropriate metadata such as
bibliographical knowledge, publications, and citations which can be
utilised to further enhance their research.

### **Open Alex**

Microsoft Academic Graph (MAG) was first launched in 2015. Since its
first public appearance, MAG has served as a promising new data source
for researchers, due to its rich bibliographic analysis and large
coverage and availability of metadata. However, in 2021 via its blog,
MAG announced that it would retire on 31st December. Soon after that
OurResearch announced that they would incorporate the last full MAG
corpus excluding the patent data to create a new graph called OpenAlex.
OpenAlex which was first launched in January 2022 also incorporated data
from CrossRef, another source of large data. OpenAlex was launched with
open API access to both their services and any form of data dumps.
OpenAlex dataset is a heterogeneous directed graph composed of five
types of scholarly entities and connections. The data in these entities
is represented by a unique ID called CEID (Canonical External ID).

<figure>
<img src="https://cdn-images-1.medium.com/max/548/0*QbBh0Z2tf30Zu6lD" />
<figcaption>Basic Knowledge Graph showing different entities in
OpenAlex.</figcaption>
</figure>

The different entities serve purposes as listed below:

- **Works**: contains scholarly documents such as journal articles,
 books, databases, and theses. The CEID for works is DOI.
- **Authors**: contains people who create works. There are approximately
 213M authors in OpenAlex. The CEID for authors is ORCID. They are
 connected to works via authorship.
- **Venues**: defined as places that host works. There are roughly
 124000 venues in OpenAlex. The CEID for venues is ISSN-L.
- **Institutions**: are organisations to which authors claim
 affiliations. There are roughly 109000 institutions in OpenAlex. The
 CEID for institutions is ROR-ID.
- **Concepts**: These are the abstract ideas works are about. There are
 approximately 65000 concepts in OpenAlex. The CEID for concepts is
 Wikidata ID.

OpenAlex contains approximately 240M works with 50000 being added daily
onto the graph. These works include scholarly articles which are further
divided into 26 document types which further inherit its definitions
from CrossRef.

#### **Open access**

There are three ways to obtain data from OpenAlex:

- A full data dump is updated every fortnight
- REST API calls are updated daily
- Web-based GUI

All of these methods are free and require no registration or permission.
The code behind OpenAlex is fully open source and is available on the
OurResearch Github account as well.

#### **OpenAlex vs MAG**

Even though OpenAlex is still quite young it has practically transferred
all works from MAG preserving almost all the metadata that is useful.
More than 90% of the MAG documents have an equivalent document type in
OpenAlex. In addition to this, the reclassification of documents done by
OpenAlex has proven to be valid which has improved the quality of
research. Moreover, the papers in OpenAlex have better Field of Study
(FoS) assignments. As we go deeper into the graph the FoS difference
is drastic.

### **Limitations and Future Work**

OpenAlex as a knowledge graph is still very young and there are areas
for improvement. It needs to make sure that there are no disambiguates
among the entities, especially authors and institutions. The dataset
also needs to add certain metadata values such as funding sources, as
well as data about corresponding authors. However, this being said the
researchers have done a great job in building a dataset which shows
great promise as an alternative for MAG.

#### Sources

- Priem, J., Piwowar, H., & Orr, R. (2022). OpenAlex: A fully-open index
 of scholarly works, authors, venues, institutions, and concepts
 (Version 2). arXiv. DOI: 10.48550/ARXIV.2205.01833
- Jiao, C., Li, K., & Fang, Z. (2023). How are exclusively data journals
 indexed in major scholarly databases? An examination of the Web of
 Science, Scopus, Dimensions, and OpenAlex (Version 1). arXiv. DOI:
 10.48550/ARXIV.2307.09704
- Scheidsteger, T., & Haunschild, R. (2023). Which of the metadata with
 relevance for bibliometrics are the same and which are different when
 switching from Microsoft Academic Graph to OpenAlex? In El Profesional
 de la información. Ediciones Profesionales de la Informacion SL.
 DOI:10.3145/epi.2023.mar.09
- Ontotext. What is a Knowledge Graph?
 <https://www.ontotext.com/knowledgehub/fundamentals/what-is-a-knowledge-graph/>

![](https://medium.com/_/stat?event=post.clientViewed&referrerSource=full_rss&postId=024941f2cfb2){width="1"
height="1"}

Unlocking the power of knowledge graphs in research catalogues: A deep dive into OpenAlex


 Author:


 Dhruv Gupta (ORCID:


 0009–0004–7109–5403


 )

Clive Humby, in 2006 rightly said, “Data is the new oil”. With data being present everywhere, it has never been more valuable.

Rogue Scholar Posts

Uncovering The Secrets Behind The Most Influential Scholarly Publications With AceMap

Knowledge Graphs: Deep Dive into OpenAlex