The dataset contains “successive pulses from the first pulsar discovered, CP 1919, are here superimposed vertically. The pulses occur every 1.337 seconds. They are caused by rapidly spinning neutron star.” (The Cambridge Encyclopaedia of Astronomy, 1977)
Thanks to Scientific American, there is a complete explanation of the dataset and its origin.
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Insights and Future Implications of the CP 1919 / PSR B1919+21 Dataset
The author of the input text has contributed to the open-source community by adding the CP 1919 / PSR B1919+21 Dataset to their GitHub repository. This dataset is derived from the first pulsar ever discovered, CP 1919, and includes data on the pulsar’s successive pulses. Each pulse occurs roughly every 1.337 seconds, the result of the rapid spinning of the neutron star.
Value of Open-Source Data Visualizations
Three unique data visualizations using the ‘ggplot2’ and ‘ggridges’ libraries are showcased in the input text, each presenting the dataset in different color schemes. These visualizations not only help to explain the data but they also make the data accessible and understandable to a broader audience. This has significant long-term implications for data accessibility and opens up opportunities for more individuals and organizations to leverage this dataset and extract valuable insights.
Actionable Advice
1. Collaborate to Improve Data Attribution: The author found this dataset in one of their old external drives and it is not known who initially gathered the dataset into a CSV file. It could be beneficial for the open-source data community to collaborate on improving metadata and data lineage transparency, ensuring data creators and contributors are properly credited.
2. Advocate for Data Accessibility: Our understanding of universal phenomena can come from the most unexpected places – in this case, a pulsar discovered back in 1919. Advocates in the field should work on making more such datasets available, as they can play a pivotal role in enabling more discoveries.
3. Learn and Leverage R Programming: For those interested in data analysis, visualization, and science, it is advisable to learn how to interact with data using R programming language. The visuals generated with ‘ggplot2’ and ‘ggridges’ in this input testify to the power and flexibility that R can provide for visualizing complex data.
4. Explore Data Reuse for New Insights: The original intent behind creating a dataset may limit its use in that original context. But freely available datasets, like CP 1919 / PSR B1919+21, can be reused for a variety of other purposes. Creatives, data scientists, and researchers can repurpose such data providing fresh insights or contributing to the development of interdisciplinary applications.
[This article was first published on The Jumping Rivers Blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
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This is part one of a two part series on {vetiver}. Future blogs will
be linked here as they are released.
Part 1: Vetiver: First steps in MLOps (This post)
Part 2: Vetiver: Model Deployment (Coming soon)
Most R users are familiar with the classic workflow popularised by R for
Data Science. Data scientists begin by importing and cleaning the data,
then iteratively transform, model, and visualise it. Visualisation
drives the modeling process, which in turn prompts new visualisations,
and periodically, they summarise their work and report results.
This workflow stems partly from classical statistical modeling, where we
are interested in a limited number of models and understanding the
system behind the data. In contrast, machine learning prioritises
prediction, necessitating the consideration and updating of many models.
Machine Learning Operations (MLOps) expands the modeling component of
the traditional data science workflow, providing a framework to
continuously build, deploy, and maintain machine learning models in
production.
Data: Importing and Tidying
The first step in deploying your model is automating data importation
and tidying. Although this step is a standard part of the data science
workflow, a few considerations are worth highlighting.
File formats: Consider moving from large CSV files to a more
efficient format like Parquet, which reduces storage costs and
simplifies the tidying step.
Moving to packages: As your analysis matures, consider creating an R
package to encourage proper documentation, testing, and dependency
management.
Tidying & cleaning: With your code in a package and tests in place,
optimise bottlenecks to improve efficiency.
Versioning data: Ensure reproducibility by including timestamps in
your database queries or otherwise ensuring you can retrieve the same
dataset in the future.
Data comes in all shapes and sizes. It can often be difficult to know where to start. Whatever your problem, Jumping Rivers can help.
Modelling
This post isn’t focused on modeling frameworks, so we’ll use {tidymodels} and the {palmerpenguins} dataset for brevity.
We aim to predict penguin species using island, flipper_length_mm, and
body_mass_g. A scatter plot indicates this should be feasible.
The scatter plot points to an obvious separation of Gentoo, to the other
species. But pulling apart Adelie / Chinstrap looks a little more
tricky.
Modelling wise, we’ll again keep things simple – a straight forward
nearest neighbour model, where we use the island, flipper length and
body mass to predict species type:
model = recipe(species ~ island + flipper_length_mm + body_mass_g,
data = penguins_data) |>
workflow(nearest_neighbor(mode = "classification")) |>
fit(penguins_data)
The model object can now be used to predict species. Reusing the same
data as before, we have an accuracy of around 95%.
To deploy a {vetiver} model object, we use a pin from the {pins}
package. A pin is simply an R (or Python!) object that is stored for
reuse at a later date. The most common use case of the {pins} package
(at least for me) is for caching data for a shiny application or quarto
document. Basically an easy way to cache
data.
However, we can pin any R object – including a pre-built model. We
pin objects to “boards” – boards can exist in many places, including
Azure, Google drive, or a simple s3 bucket. For this example, I’m using
using Posit Connect:
# Not something you would normally do with a {vetiver} model
pins::pin_read(pins::board_connect(), "colin/k-nn")
#> $model
#> bundled workflow object.
#>
#> $prototype
#> # A tibble: 0 × 3
#> # ℹ 3 variables: island <fct>, flipper_length_mm <int>, body_mass_g <int>
Deploying as an API
The final step is to construct an API around your stored model. This is
achieved using the {plumber} package. To deploy locally, i.e. on your
own computer, we create a plumber instance and pass the model using
{vetiver}
This deploys the APIs locally. When you run the code, a browser window
will likely open. If it doesn’t simply navigate to http://127.0.0.1:7764/__docs__/.
If the API has successfully deployed, then
base_url = "127.0.0.1:7764/"
url = paste0(base_url, "ping")
r = httr::GET(url)
metadata = httr::content(r, as = "text", encoding = "UTF-8")
jsonlite::fromJSON(metadata)
The API also has endpoints metadata and pin-url allowing you to
programmatically query the model. The key endpoint for MLops, is predict. This endpoint allows you to pass new data to your model, and
predict the outcome
Understanding MLOps and its Role in Data Science Workflows
The current state of data science, particularly in the realm of R users, begins with data importation and cleaning, followed by iterative transformations, modeling, and visualizations to drive the work forward. This workflow is largely a result of traditional statistical modeling, a method that involves studying and understanding the system underlying the data.
Machine Learning Operations, or MLOps, however, is structured differently. It prioritizes prediction over understanding and involves the consideration of several different models. MLOps broadens the modeling aspect of the data science workflow and offers a structure to continuously build, deploy, and manage machine learning models efficiently.
Relevance of Data Importation and Tidying
Automating the process of importing and tidying data is the first step in deploying a model. Key factors to consider include the file formats, moving to packages as the analysis evolves, optimising bottlenecks in tidying and cleaning, and ensuring data reproducibility with versioning. Pay attention to how these considerations fit into the broader context of perpetually building, deploying, and managing machine learning models.
Predicting with Models
The {tidymodels} and {palmerpenguins} datasets are used for an example of predicting penguin species based on the island, flipper length, and body mass. By working with pre-existing data, a model was built that achieved an accuracy of around 95%. This model can now be used to predict species, thus illustrating a simple application of machine learning in a data science workflow.
Storing Your Model
After building a model, MLOps comes into play, with a package called {vetiver}. It collates crucial details to store, deploy, and version the model. This is achieved through a pin from the {pins} package, an R object that can be stored for reuse. These pins are stored on boards, which can be located in various places, such as Azure and Google Drive. Once the model is pinned to a board, it can be retrieved for use in future computation.
Deploying as an API
The final deployment phase involves creating an API around your stored model using the {plumber} package. An API endpoint’s main role in MLOps is the prediction endpoint, which allows for new data passage to the model and predicts the outcome based on existing patterns in the model.
Actionable points based on insights
In your data science workflow, consider integrating MLOps strategies for efficient, continuous build, deployment, and management of machine learning models.
Take automation steps in data importation and tidying to ease the process of data handling in model deployment.
As the analysis evolves, consider moving to R packages for documentation, testing, and dependency management.
Consider using efficient file formats like Parquet.
Use tools like {vetiver} and {plumber} for model deployment and embedding your model into an API respectively.
Prepare for the future of MLOps by keeping up-to-date with information regarding model deployment in R.
In the long run, as machine learning becomes even more integrated into data science, developing knowledge and skills in MLOps will be indispensable. As seen in this exploration of R’s MLOps, the process of preparing, creating, deploying, and maintaining machine learning models has massive implications for efficiency and reproducibility, leading to a more streamlined data science workflow.
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Keith Karani Wachira, the Dekut R Community organizer based in Nyeri, Kenya, was recently interviewed by the R Consortium and shared his journey in the R community, which began in 2019 during his university years. Sparked by a tech meetup, Keith’s interest grew through the pandemic sessions. Now in academia, he uses R to address business automation challenges, attracting industry professionals to his practical sessions. Excited by trends like AI integration and tools like Quarto, Keith foresees increased automation and efficiency. Outside work, he enjoys baseball, graphic design, web development, and teaching R, finding great reward in his students’ success.
Please share about your background and your involvement in the R Community. What is your level of experience with the R language?
I began my journey with R in early 2019 while studying at university. In May 2019, I learned about a tech community through a friend who posted in one of our school’s WhatsApp groups, inviting us to join a meetup. Curious, I decided to attend.
I remember the meetup was on a Saturday, and it turned out to be the launch of a new club. My friend invited me and was part of the Microsoft Learn Students’ Ambassadors. His classmates used R for their engineering projects, which sparked my interest.
During the first lesson, I found it challenging as there were about 30 students, most of whom were first-year students pursuing various degrees, including Business Information Technology, which I was majoring in, along with a minor in Communication. My first programming language that year was C, which I found interesting.
Over time, I found the R language interesting, especially its syntax. What fascinated me the most was how data could be used to create visualizations. This curiosity led me to explore data from my local sewerage and water company, using R to create informative visualizations and derive insights that can be used in decision making.
I continued attending the sessions in 2020 during the pandemic. Although we no longer had in-person classes, we adapted using Microsoft Teams for our meetings. Eric organized the meetups and arranged tech talks with speakers from Posit (formerly RStudio) and NairobiR. I remember attending these sessions and understanding how powerful R is.
Throughout 2020, I attended regularly but still lacked confidence in the language. However, in 2022, I made significant progress. Under Eric’s leadership, we expanded the community to involve more people, especially students from the department of Actuarial Science, Telecommunication Engineering and electrical engineering. We set up a structured learning environment based on materials from Hadley Wickham’s books and resources from the R website and blogs.
Eric’s leadership greatly influenced me. He taught us how to write blogs using Markdown and publish on RPubs. This is a bit about my background. Today, we continue to teach R, following a structured approach to help others intermediate in using the language.
What industry are you currently in? How do you use R in your work?
I’m currently in academia, primarily focusing on various technical challenges. We hold sessions where we demonstrate the use of R in robotics for members in Electrical Engineering and Telecommunication Engineering. For those in Actuarial Science, we show how to create time series models using R.
Coming from a background in business and information technology, I focus on solving business challenges, particularly automating business processes and addressing issues in banking, logistics, retail using opensource datasets. Our efforts are not limited to academia; we concentrate on applying R across different disciplines within academia to tackle these challenges.
Why do industry professionals come to your user group? What is the benefit of attending?
An interesting scenario arose when I became interested in EMS (Engineering and Management Systems). We started organizing hybrid sessions after the COVID period and it caught interest of students from another university in Kenya ,Egerton University. Through, statistical analysis bureau of Egerton University they joined our sessions to learn how to leverage tidy models packages to create machine learning models and also collaborate with the community members.
They were very interested, and as future economists, we demonstrated how to build and appreciate these models. In previous meetups, we also introduced participants to Shiny apps, teaching them how to host their models and create interfaces to display their work.
Another valuable skill we taught was generating reports using R Markdown. This allows users to write code, format text, add videos, images, and emojis, and present their work in a professional and engaging manner. Attendees found this particularly useful as it enhanced their ability to write, structure, and report code effectively.
Participants learned to leverage the R ecosystem for coding, structuring their work, and reporting their findings by attending our sessions.
What trends do you currently see in R language and your industry? Any trends you see developing in the near future?
A trend I’ve noticed is the widespread effort to include everyone in learning programming languages like R. This is evident in the emergence of specialized groups such as R for Medicine and R for Pharma. Two of our alumni even demonstrated how R can be used in robotics through a talk at Posit Conference 2022, demonstrating its applicability in specific industries. This specialization fascinates me, and I am eager to see how R will be used across various fields.
Another trend is using tools like Quarto, which facilitates the implementation of such specializations. Additionally, I am excited about the incorporation of AI in building R applications, such as using Gemini for Shiny apps. Although materials on this are currently limited, I see this as a growing trend.
The integration of AI will likely lead to the automation of many manual processes, further enhancing R’s utility and efficiency in various industries.
We would like to get to know you more personally. Can you please tell me about yourself? For example, your hobbies/interests or anything else you want to share.
When not in front of my laptop, I enjoy playing baseball and softball, especially as a catcher. Catching allows me to see the entire game command the play, and I enjoy throwing the ball from home to second base and picking off a runner. It’s a challenging position that helps me focus and improve my aim.
On the side, I also do some graphic design using Canva which I use to create posters and newsletters for our community meetups. Additionally, I have web development skills using MERN stack.
Another passion of mine is teaching R to others. I love seeing people learn and apply the concepts and then go on to teach others. One of my students from his first year has now taken over as a lead in our community, which is incredibly encouraging. He even competed in hackathons and finished fourth, showing how much he has grown.
Teaching and seeing others succeed is something I find very rewarding and motivating.
How do I Join?
R Consortium’s R User Group and Small Conference Support Program (RUGS) provides grants to help R groups organize, share information, and support each other worldwide. We have given grants over the past four years, encompassing over 68,000 members in 33 countries. We would like to include you! Cash grants and meetup.com accounts are awarded based on the intended use of the funds and the amount of money available to distribute.
Summary of Keith Karani Wachira’s Journey in R Language and Its Implications
This text primarily discusses Keith Karani Wachira’s journey with the R programming language and his leadership in the R community through Dekut R Community in Nyeri, Kenya. Keith began his R language journey at university and continued through pandemic sessions, leading to his current role in academia to address business automation challenges.
Key Insights
Keith’s journey started in 2019, generated from his curiosity about data visualization and exploration. He continued his learning during the pandemic, sparking interest in R for various groups, such as Business Information Technology, Actuarial Science, and Telecommunication Engineering students. Keith’s dedication has garnered interest from industry professionals.
His work in academia emphasizes on solving business challenges using R language, particularly in automating business processes. This has led to collaborative sessions with industry professionals and different academic disciplines.
Long-term Implications
The increasing interest in R driven by Keith’s initiatives will likely continue to grow, encouraging more individuals and sectors to learn and utilize R for their projects. As a leader in the R community, Keith’s influence extends to other areas of study and industry.
Witnessing the trend of learning programming languages like R, Keith also noted the development of specialized groups such as R for Medicine and R for Pharma. The emergence of these specialized groups highlights the extensive potential of R and its applicability in various industries.
Keith also takes note of the rising trend of AI integration into R applications. As the AI trend continues to grow, R will likely be increasingly utilized, leading to greater efficiency and automation in various industries.
Future DEVELOPMENTS
In the future, we can expect more integrations of R in various industrial applications, including areas like medicine and pharmaceuticals. Tools like Quarto are anticipated to facilitate the efficient implementation of these specializations further.
Actionable Advice
Moving forward, it’s crucial to support initiatives like Keith’s to expand the usage and understanding of R in various fields. Incorporating R in academia and industry is a conducive way of addressing business and workflow automation challenges.
Encouraging specialized groups to learn R and apply it in their respective fields boosts diversity in understanding and applying the programming language, helping generate creative solutions to complex problems.
Finally, it’s important to stay updated with current trends in R language application, such as AI integration, to utilize its potential fully and efficiently.
Recommendations
Support initiatives encouraging the application of R in various industries
Encourage diverse groups to learn and utilize R in their areas of study or work
Stay current with emerging trends such as AI integration in R applications
Overall, Keith’s journey in the R community signifies the growing influence of R language across various industries and academic disciplines. It also emphasizes the importance of the continued exploration and cultivation of R language skills in different sectors for driving innovation and efficiency.
arXiv:2406.04309v1 Announce Type: cross Abstract: The common trade-offs of state-of-the-art methods for multi-shape representation (a single model “packing” multiple objects) involve trading modeling accuracy against memory and storage. We show how to encode multiple shapes represented as continuous neural fields with a higher degree of precision than previously possible and with low memory usage. Key to our approach is a recursive hierarchical formulation that exploits object self-similarity, leading to a highly compressed and efficient shape latent space. Thanks to the recursive formulation, our method supports spatial and global-to-local latent feature fusion without needing to initialize and maintain auxiliary data structures, while still allowing for continuous field queries to enable applications such as raytracing. In experiments on a set of diverse datasets, we provide compelling qualitative results and demonstrate state-of-the-art multi-scene reconstruction and compression results with a single network per dataset.
The article “Multi-Shape Representation with Recursive Hierarchical Formulation” addresses the common trade-offs faced by state-of-the-art methods for representing multiple shapes in a single model. These trade-offs typically involve sacrificing modeling accuracy in exchange for memory and storage efficiency. However, the authors propose a novel approach that enables the encoding of multiple shapes as continuous neural fields with higher precision and lower memory usage than previously possible.
The key innovation of their method lies in a recursive hierarchical formulation that takes advantage of object self-similarity. This formulation results in a highly compressed and efficient shape latent space, allowing for spatial and global-to-local latent feature fusion without the need for auxiliary data structures. Moreover, the authors demonstrate that their method supports continuous field queries, enabling applications such as raytracing.
To validate their approach, the authors conduct experiments on diverse datasets. The results showcase compelling qualitative outcomes and demonstrate state-of-the-art multi-scene reconstruction and compression results using only a single network per dataset. Overall, this article presents a promising solution for multi-shape representation that balances modeling accuracy, memory usage, and storage efficiency.
Exploring Innovative Solutions for Multi-Shape Representation
Exploring Innovative Solutions for Multi-Shape Representation
In the field of multi-shape representation, researchers have often faced trade-offs between modeling accuracy and memory storage. State-of-the-art methods typically sacrifice modeling accuracy to optimize memory usage. However, a new approach presented in a recent paper challenges this paradigm by introducing a highly compressed and efficient shape latent space.
Encoding Multiple Shapes with High Precision and Low Memory Usage
The key innovation of the proposed method lies in its recursive hierarchical formulation, which takes advantage of object self-similarity. By leveraging this self-similarity, the method achieves a higher degree of precision in encoding multiple shapes represented as continuous neural fields. This breakthrough is achieved while maintaining low memory usage, addressing one of the common challenges of existing methods.
The recursive formulation enables the method to support spatial and global-to-local latent feature fusion without relying on auxiliary data structures. This means that there is no need for additional initialization or maintenance of such data structures throughout the process. As a result, the method achieves a significant reduction in computational complexity and improves overall efficiency.
Continuous Field Queries and Applications
One of the remarkable features of this innovative method is its ability to enable continuous field queries. This allows for seamless integration with applications such as raytracing. By providing continuous field queries, the method opens up new possibilities for more advanced and realistic simulations and visualizations.
Compelling Results and State-of-the-Art Performance
Experimental evaluation on diverse datasets highlights the effectiveness of the proposed method. The obtained qualitative results are compelling and demonstrate improved multi-scene reconstruction and compression compared to existing techniques. Notably, the method achieves these results using a single network per dataset, which contributes to its efficiency and scalability.
Conclusions
The innovative approach presented in this paper offers a fresh perspective on multi-shape representation. By leveraging object self-similarity and adopting a recursive hierarchical formulation, the method achieves a highly compressed and efficient shape latent space. The ability to support continuous field queries further expands its potential applications. Overall, the proposed method contributes to advancements in multi-shape representation and provides a promising avenue for more accurate and efficient modeling in various domains.
“The only way to discover the limits of the possible is to go beyond them into the impossible.” – Arthur C. Clarke
The paper arXiv:2406.04309v1 discusses a novel approach to multi-shape representation using continuous neural fields. The authors address the common trade-offs faced by existing methods in terms of modeling accuracy, memory, and storage. They propose a recursive hierarchical formulation that takes advantage of object self-similarity, resulting in a highly compressed and efficient shape latent space.
One of the key contributions of this approach is the ability to encode multiple shapes with a higher degree of precision than previous methods, while still maintaining low memory usage. This is achieved by leveraging the recursive formulation, which allows for spatial and global-to-local latent feature fusion without the need for auxiliary data structures. This is a significant advantage as it simplifies the implementation and reduces the computational overhead.
The authors also highlight the usefulness of their method in enabling continuous field queries, which opens up applications such as raytracing. This means that the encoded shapes can be efficiently queried for rendering purposes, enhancing the realism and efficiency of computer graphics applications.
In the experimental evaluation, the authors demonstrate the effectiveness of their approach on diverse datasets. They provide compelling qualitative results, showcasing state-of-the-art multi-scene reconstruction and compression results. Importantly, they achieve these results using a single network per dataset, which further enhances the efficiency and simplicity of their method.
Overall, this paper presents a promising solution to the challenges in multi-shape representation. The recursive hierarchical formulation and the use of continuous neural fields offer a powerful and efficient approach for encoding multiple shapes with high precision and low memory usage. The demonstrated state-of-the-art results in multi-scene reconstruction and compression further validate the effectiveness of the proposed method. Future research in this area could focus on exploring the applicability of this approach to other domains and expanding its capabilities to handle more complex shapes and scenes. Read the original article
arXiv:2406.01759v1 Announce Type: new
Abstract: This paper introduces a post-hoc explainable AI method tailored for Knowledge Graph Embedding models. These models are essential to Knowledge Graph Completion yet criticized for their opaque, black-box nature. Despite their significant success in capturing the semantics of knowledge graphs through high-dimensional latent representations, their inherent complexity poses substantial challenges to explainability. Unlike existing methods, our approach directly decodes the latent representations encoded by Knowledge Graph Embedding models, leveraging the principle that similar embeddings reflect similar behaviors within the Knowledge Graph. By identifying distinct structures within the subgraph neighborhoods of similarly embedded entities, our method identifies the statistical regularities on which the models rely and translates these insights into human-understandable symbolic rules and facts. This bridges the gap between the abstract representations of Knowledge Graph Embedding models and their predictive outputs, offering clear, interpretable insights. Key contributions include a novel post-hoc explainable AI method for Knowledge Graph Embedding models that provides immediate, faithful explanations without retraining, facilitating real-time application even on large-scale knowledge graphs. The method’s flexibility enables the generation of rule-based, instance-based, and analogy-based explanations, meeting diverse user needs. Extensive evaluations show our approach’s effectiveness in delivering faithful and well-localized explanations, enhancing the transparency and trustworthiness of Knowledge Graph Embedding models.
Analysis of Post-Hoc Explainable AI Method for Knowledge Graph Embedding Models
Knowledge Graph Embedding (KGE) models have played a crucial role in Knowledge Graph Completion by capturing the semantics of knowledge graphs through latent representations. However, the opaque and black-box nature of these models has been a major criticism. This paper introduces a post-hoc explainable AI method specifically designed for KGE models, aiming to provide transparent, interpretable insights.
One of the key challenges in achieving explainability for KGE models lies in understanding the complex latent representations that encode the relationships within the knowledge graph. Existing methods have focused on visualizations or indirect explanations, but this approach takes a different approach.
The proposed method leverages the principle that similar embeddings reflect similar behaviors within the knowledge graph. By decoding the latent representations and identifying distinct structures within subgraph neighborhoods of similarly embedded entities, the method uncovers statistical regularities that the KGE models rely on. These regularities are then translated into human-understandable symbolic rules and facts.
What makes this method unique is its ability to bridge the gap between the abstract representations of KGE models and their predictive outputs. It provides immediate, faithful explanations without requiring retraining of the models, which is a significant advantage for real-time applications, especially on large-scale knowledge graphs.
The multi-disciplinary nature of this work is evident through its integration of AI, knowledge representation, and graph analysis. It combines techniques from machine learning and graph theory to extract interpretable insights from KGE models.
Furthermore, this method offers flexibility in generating different types of explanations based on user needs. It can produce rule-based explanations, which provide generalizable patterns and insights, instance-based explanations, which offer specific explanations for individual entities, and analogy-based explanations, which identify similarities and differences between different entities.
Extensive evaluations of the proposed method demonstrate its effectiveness in delivering faithful and well-localized explanations. By enhancing the transparency and trustworthiness of KGE models, this method has the potential to address the concerns of stakeholders who require interpretable AI systems. It could be particularly valuable in domains such as healthcare, finance, and recommendation systems where explainability is crucial.
In conclusion, this post-hoc explainable AI method for Knowledge Graph Embedding models represents a significant step towards addressing the explainability challenges in knowledge graph completion. By directly decoding latent representations and translating them into human-understandable symbolic rules and facts, this method offers a transparent, interpretable approach without sacrificing the predictive power of KGE models. Its multi-disciplinary nature and flexibility make it a promising avenue for future research and application in various domains.
