#Knowledge Graph Embedding

[in-sightpublishing]

Conversation with Tomáš Perna on ANNs and More (6)

Scott Douglas Jacobsen In-Sight Publishing, Fort Langley, British Columbia, Canada Correspondence: Scott Douglas Jacobsen (Email: scott.jacobsen2025@gmail.com) Received: January 2, 2025 Accepted: N/A Published: January 15, 2025         Abstract This interview features Scott Douglas Jacobsen engaging in a profound discussion with Tomáš Perna on the simulation of human intelligence through…

[drmikewatts]

Soft Computing, Volume 29, Issue 8, April 2025

1) Infinite computations and Büchi automata using grossone

Author(s): Louis D’Alotto

Pages: 3749 - 3755

2) Analysis of selection noise in genetic algorithms

Author(s): Nataliya M. Gulayeva, Joaquín Borrego-Díaz, Fernando Sancho-Caparrini

Pages: 3757 - 3773

3) Partial metric spaces on effect algebras

Author(s): Sarvesh Kumar Mishra, Mukesh Kumar Shukla, Akhilesh Kumar Singh

Pages: 3775 - 3784

4) Deductive systems and R-congruences of pre-Hilbert algebras

Author(s): Andrzej Walendziak

Pages: 3785 - 3794

5) Environmental impact assessment using bipolar complex intuitionistic fuzzy sets

Author(s): Abd Ulazeez M. J. S. Alkouri, Zeyad A. Alshboul

Pages: 3795 - 3809

6) Uncertain Fox equation

Author(s): Dan Chen, Yang Liu

Pages: 3811 - 3822

7) Identification of block cipher algorithms using multi-layer perception algorithm

Author(s): Ke Yuan, Daoming Yu, Zheng Li

Pages: 3823 - 3834

8) Color-channel adversarial attack with resolution based camouflaging

Author(s): Guowei Li, Ping Li, Xinpeng Zhu

Pages: 3835 - 3846

9) Novel deep learning-based side-channel attack on different-device

Author(s): Ji-Eun Woo, Yongsung Jeon, Dong-Guk Han

Pages: 3847 - 3854

10) Min-cost route problems for multimodal sustainable logistics cooperation

Author(s): Carmine Cerrone, Anna Sciomachen, Maria Truvolo

Pages: 3855 - 3868

11) A multi-objective genetic algorithm for unequal area facility layout problem considering safety and cost

Author(s): Hamidreza Koosha, Fatemeh Mirsaeedi, Mohammad Taghi Assadi

Pages: 3869 - 3887

12) Public health events emergency management supervision strategy with multi-agent participation

Author(s): Bingjie Lu, Decheng Wen

Pages: 3889 - 3908

13) Efficient scheduling of integrated operating rooms and post-anesthesia care units under uncertain surgery and recovery times: an artificial neural network-metaheuristic framework

Author(s): Mohammad Amin Ahmadian, Mohsen Varmazyar, Ali Fallahi

Pages: 3909 - 3941

14) A novel variant of moth swarm algorithm for flexible AC transmission system-based optimal power flow problem

Author(s): Dhiman Banerjee, Provas Kumar Roy, Goutam Kumar Panda

Pages: 3943 - 3983

15) Identification of protein-coding regions using optimized sinusoidal assisted variational mode decomposition based on swarm optimization algorithm

Author(s): K. Jayasree, Malaya Kumar Hota

Pages: 3985 - 4000

16) Comparison of different sway control algorithms for multi carriages crane system

Author(s): Emir Esim, Şahin Yıldırım, Ayşegül Gördebil

Pages: 4001 - 4020

17) Toward almost-zero fault acceptance of deep learning-based voice authentication using small training dataset

Author(s): Seung-A. Park, Sun-Beom Kwon, Dooho Choi

Pages: 4021 - 4032

18) Adaptive RoI-aware network for accurate banknote recognition using natural images

Author(s): Zhijie Lin, Zhaoshui He, Hao Liang

Pages: 4033 - 4043

19) N-gram opcode frequency based malware detection using CNN algorithm

Author(s): Seok Min Ko, JaeHyeok Yang, Ilsun You

Pages: 4045 - 4053

20) GA-based partial high-order-cascaded-deep time series forecasting model

Author(s): Gulseren Birim, Ozge Cagcag Yolcu

Pages: 4055 - 4074

21) Lightweight image super-resolution via an adaptive information fusion attention network

Author(s): Yi He, Hai Huan, Chao Wang

Pages: 4075 - 4089

22) Meet MASKS: integrating distributed knowledge and verification for multi-agent systems

Author(s): Majid Alizadeh, Amirhoshang Hoseinpour Dehkordi, Ali Movaghar

Pages: 4091 - 4106

23) Yolo-tir: an improved YOLOv5 model for vehicle and pedestrian in thermal infrared images

Author(s): Shangshu Yao, Kun Yu, Yufang Liu

Pages: 4107 - 4119

24) An approach of multi-viewed graph embedding with adaptive heat kernel based diffusion and global expressive learning

Author(s): Phu Pham

Pages: 4121 - 4137

25) PGA-Net: progressive granularity-aware training network for fine-grained image recognition

Author(s): Wei He, Zhixiang He, Jianhui Wu

Pages: 4139 - 4152

26) Early stopping strategies in Deep Image Prior

Author(s): Alessandro Benfenati, Ambra Catozzi, Federica Porta

Pages: 4153 - 4174

27) Pygrossone: a python-powered library for operating with the infinity computer arithmetic

Author(s): Alberto Falcone, Alfredo Garro, Yaroslav D. Sergeyev

Pages: 4175 - 4189

28) Unobserved expected returns in a diffusive price process: is filtering effective?

Author(s): Paride Antonini, Flavio Angelini, Marco Nicolosi

Pages: 4191 - 4205

29) Do oil prices impact on transportation? Evidence from random matrix theory

Author(s): Antonio Garcia-Amate, Laura Molero-Gonzalez, Juan Evangelista Trinidad-Segovia

Pages: 4207 - 4218

30) Time series clustering for high-dimensional portfolio selection: a comparative study

Author(s): Raffaele Mattera, Germana Scepi, Parmjit Kaur

Pages: 4219 - 4231

31) Mean–variance–skewness portfolio optimization with uncertain returns via co-skewness

Author(s): Jinwu Gao, Haomiao Hu, Hamed Ahmadzade

Pages: 4233 - 4246

32) An exterior algebra approach to generalised variances and cross-covariances

Author(s): Henry P. Wynn, Anatoly Zhigljavsky

Pages: 4247 - 4257

33) Optimised resilience measures for supply chain using portfolio selection

Author(s): Risto Talas, Ashraf Labib, Gurjeet Dhesi

Pages: 4259 - 4273

34) A new model and DCA based algorithm for clustering

Author(s): Hoai An Le Thi

Pages: 4275 - 4285

35) Infinite numbers, infinity computing the philosophy of grossone

Author(s): Gabriele Lolli

Pages: 4287 - 4299

[microlearningplatform]

Revolutionize Learning: How ChatGPT Powers Instant Training on MaxLearn

Creating a compelling image for a blog post like "ChatGPT-Based Instantaneous Training Platform" isn't just about aesthetics; it's about immediate communication, engagement, and brand reinforcement. In today's visually-driven digital landscape, an impactful image can be the difference between a scroll-past and a deep dive into your content. For a sophisticated platform like MaxLearn, leveraging the power of visuals to convey innovation, efficiency, and intelligence is paramount. This article will delve into the art and science of crafting the perfect image for such a specialized blog post, from conceptualization to execution, including the growing role of AI in this creative process.

The Undeniable Power of Visuals in Digital Content

In a world saturated with information, our brains are constantly seeking shortcuts to process data. Visuals offer exactly that. Studies consistently show that articles with relevant images receive significantly more views and shares than those without. The human brain processes images up to 60,000 times faster than text, making them incredibly effective for conveying complex ideas quickly and memorably.

For a blog post about a "ChatGPT-Based Instantaneous Training Platform," an image serves several critical functions:

Grabbing Attention: A compelling visual acts as a hook, stopping the reader's scroll and drawing them into the article.

Communicating Instantly: Before a single word is read, the image can convey the core message – AI, speed, learning, and modern technology.

Enhancing Comprehension: Visuals can simplify abstract concepts, making the "instantaneous" and "ChatGPT-based" aspects more tangible.

Boosting Engagement: High-quality, relevant images make content more enjoyable to consume, encouraging readers to spend more time on the page.

Improving SEO and Shareability: Search engines favor content with rich media, and visually appealing posts are far more likely to be shared across social media platforms, extending your reach.

Building Brand Identity: A consistent visual style reinforces MaxLearn's brand as innovative, professional, and forward-thinking in the e-learning space.

Deconstructing the Blog Post Title: "ChatGPT-Based Instantaneous Training Platform | MaxLearn"

To create an effective image, we must first dissect the core concepts embedded in the title:

ChatGPT-Based: This immediately brings AI, natural language processing, conversational interfaces, and intelligent automation to mind.

Instantaneous Training: This speaks to speed, efficiency, on-demand learning, immediate results, and perhaps a reduction in traditional training bottlenecks.

Training Platform: Implies learning, skill development, a digital environment, and perhaps collaboration or personalized paths.

MaxLearn: The brand name, suggesting maximum learning, growth, and a professional, cutting-edge educational solution.

Combining these, the image should evoke intelligence, speed, user-friendliness, and effective learning outcomes, all within a modern, professional context.

Conceptualizing the Ideal Image: Brainstorming Ideas

With the core concepts in mind, let's brainstorm visual metaphors and direct representations:

AI Interaction:

A human hand reaching out to an ethereal, glowing AI interface (perhaps represented by a subtle ChatGPT logo or a stylized brain icon), with knowledge flowing between them.

A minimalist chat bubble merging with a brain or a lightbulb, signifying instant comprehension.

A student (diverse representation is key) engaged in a focused conversation with a sleek, futuristic AI bot on a screen.

Speed & Efficiency:

A fast-moving light trail or a blurred background around a sharp, focused learner, symbolizing rapid progress.

A clock face transforming into a growth chart or a knowledge graph, showing time efficiency.

Lightning bolts striking a knowledge base, indicating instantaneous access and learning.

Knowledge & Growth:

A growing plant or tree with roots extending into a network of data or AI circuits.

A pathway or staircase illuminated by a digital glow, leading quickly upwards.

Gears turning rapidly, powered by an intelligent core, producing a finished product (trained individual).

Platform Representation:

A sleek, modern digital interface (perhaps a tablet or laptop screen) showcasing a clean design with AI chat elements visible.

Abstract data visualizations converging into a clear, understandable output, representing simplified complex learning.

The MaxLearn logo subtly integrated into a dynamic, AI-powered learning environment.

Choosing the Best Concept: For "instantaneous training" and "ChatGPT-based," a concept that marries the human element of learning with the speed and intelligence of AI will be most effective. An image featuring a person seamlessly interacting with an AI interface, perhaps with subtle visual cues of speed (like light trails or data flow), would resonate well. The overall aesthetic should be clean, modern, and trustworthy.

The Role of AI in Image Creation for Modern Marketing

Given that the blog post is about a "ChatGPT-based" platform, it's fitting to consider using AI image generators for creating the visual. Tools like DALL-E 3 (integrated into ChatGPT Plus), Midjourney, Adobe Firefly, or Stable Diffusion can produce high-quality, unique images from text prompts.

How to Use AI Image Generators Effectively:

Craft Detailed Prompts: Don't just type "AI learning." Be specific: "A professional, diverse student, mid-shot, focused on a glowing tablet screen, subtle futuristic data streams flowing from the screen, abstract representation of AI brain behind, light blue and purple color scheme, modern, clean, high-resolution, MaxLearn branding colors, fast learning concept, digital, sophisticated."

Iterate and Refine: AI generation is often an iterative process. Generate multiple options, identify what works, and refine your prompts based on the results. Experiment with different styles (photorealistic, illustrative, abstract).

Brand Consistency: If possible, train the AI on existing MaxLearn brand assets or explicitly include brand colors and stylistic elements in your prompts.

Legal and Ethical Considerations: Be mindful of copyright and licensing for AI-generated images. While many platforms offer commercial use, it's always wise to check their terms of service. Ensure the generated images do not contain bias or stereotypes.

Alternatively, if AI generation isn't the chosen path, high-quality stock photography (from sites like Unsplash, Pexels, Shutterstock, Adobe Stock) can be an excellent resource. Look for images that feel authentic and avoid generic "stock photo" clichés. Custom graphics created by a designer using tools like Adobe Illustrator or Canva can also provide unique and brand-aligned visuals.

Best Practices for Implementing the Image

Once the perfect image is created, its implementation is equally crucial for maximum impact:

High Quality & Resolution: Ensure the image is crisp, clear, and high-resolution, suitable for web display. Avoid pixelated or blurry visuals at all costs.

Relevance is Key: The image must directly relate to the content and theme of the blog post. A beautiful but irrelevant image can confuse readers.

Optimal File Size: Large image files slow down page loading times, negatively impacting user experience and SEO. Compress images (e.g., using TinyPNG or JPEGmini) without compromising quality. Aim for web-friendly formats like JPEG for photos and PNG for graphics with transparency. WebP is also an excellent modern format for optimized performance.

Strategic Placement: The featured image at the top of the blog post is paramount. Within the article, place images strategically to break up text, illustrate points, and maintain reader engagement (e.g., every 300-400 words).

Descriptive Alt Text: Always add descriptive alt text to your images. This is crucial for:

Accessibility: Screen readers use alt text to describe images to visually impaired users.

SEO: Search engines use alt text to understand image content, which can help your post rank higher in image search results.

Fallback: If an image fails to load, the alt text will be displayed.

Example Alt Text: "AI-powered learning platform with student interacting instantaneously with ChatGPT on MaxLearn."

Captions: While not always mandatory, captions can provide additional context or highlight key takeaways related to the image.

Mobile Responsiveness: Ensure the image displays correctly and looks good on all devices (desktops, tablets, smartphones). Most modern content management systems handle this automatically, but always double-check.

Brand Consistency: Maintain a consistent visual style across all your blog post images to build a recognizable brand identity for MaxLearn. This includes color palettes, iconography, and overall aesthetic.

Conclusion

In the dynamic realm of digital content, an image is far more than just decoration. For a professional blog post like "ChatGPT-Based Instantaneous Training Platform | MaxLearn," a carefully chosen and optimized visual is a powerful communication tool. It can instantly convey innovation, efficiency, and the intelligent future of learning, captivating your audience and driving deeper engagement. By thoughtfully conceptualizing the message, leveraging advanced tools including AI image generators, and adhering to best practices for web implementation, MaxLearn can ensure its blog content not only informs but also inspires and connects with its target audience in a visually compelling way, truly maximizing learning through cutting-edge technology.

[iknowledgenile-blog]

Neuro Symbolic AI vs Traditional AI: Why the Future Needs Both Brains and Logic

In the ever-evolving landscape of artificial intelligence, two dominant paradigms have shaped its progress Traditional AI (symbolic AI) and Neural AI (deep learning). While each brings unique strengths to the table, a growing consensus is emerging around the idea that the real power lies in combining both, a concept known as Neuro-Symbolic AI. 

Traditional AI: Logic and Rules 

Traditional AI, also known as symbolic AI, relies on logic-based reasoning, knowledge graphs, and rule-based systems. It excels in tasks that require clear, structured reasoning like solving equations, making decisions based on rules, or following complex instructions. Symbolic systems are interpretable and transparent, making them ideal for applications where explainability and trust are crucial, such as legal reasoning or medical diagnostics. 

However, traditional AI struggles with real-world ambiguity. It finds it difficult to adapt to new data or learn from unstructured inputs like images, audio, or raw text without extensive manual effort. 

Neural AI: Learning from Data 

Neural networks, particularly deep learning models, thrive on massive datasets. They’ve powered recent breakthroughs in image recognition, natural language processing, and speech-to-text applications. Neural AI is intuitive and excels at pattern recognition, making it perfect for tasks that are too complex to explicitly define with rules. 

But neural AI is often a black box. It lacks transparency and can falter in situations requiring logical consistency or common-sense reasoning. It also needs vast amounts of labeled data and may fail when faced with novel scenarios outside its training set. 

Neuro-Symbolic AI: Best of Both Worlds 

Neuro-symbolic AI blends the learning capability of neural networks with the reasoning power of symbolic systems. Imagine a system that can learn from raw data and then reason it using logical structures. That’s the promise of this hybrid approach. 

For example, in healthcare, a neuro-symbolic system can analyze patient scans (neural part) and reason through diagnoses based on established medical guidelines (symbolic part). In education, it can understand a student’s handwritten input and provide structured, logical feedback. 

The Future is Hybrid 

AI’s future lies not in choosing between logic or learning but in embracing both. Neuro-symbolic AI offers the robustness, transparency, and adaptability required to tackle complex, real-world problems. As AI becomes more embedded in our daily lives, this balanced approach will ensure it remains powerful, ethical, and trustworthy. 

[transcuratorsblog]

How to Use “People Also Search For” Data to Create Blog Angles

Creating blog content that resonates with your audience and ranks on search engines is a challenge — especially in competitive niches. That’s where using Google's “People Also Search For” (PASF) feature can give you an edge. This often-overlooked data source provides real search behavior insights, helping you uncover untapped content ideas and fresh angles that can boost your organic reach.

While businesses may rely heavily on pay per click services for visibility, optimizing blog content using PASF data helps you get evergreen traffic with zero ongoing ad spend. In this guide, we’ll break down how to mine PASF results and turn them into high-performing blog angles that truly meet user intent.

What Is “People Also Search For”?

“People Also Search For” is a feature in Google Search that appears when a user clicks on a search result and quickly returns to the search engine results page (SERP). It shows related searches based on what other users commonly explore around the same topic.

Where You’ll See It:

Beneath a search result after a quick bounce

On the right side panel of Google Knowledge Graph entries

Embedded in mobile search results

These suggestions come directly from real user behavior, making them powerful tools for identifying complementary and follow-up blog topics.

Why Use PASF for Blog Content Planning?

Unlike traditional keyword research tools that suggest broad keywords, PASF helps you:

Discover user questions and curiosity paths

Find related, lower-competition angles

Expand content to build topical authority

Create content clusters around your main topic

Improve internal linking opportunities and dwell time

It’s a smart, data-backed way to ensure your content matches how users actually explore topics, not just how we assume they search.

Step-by-Step: How to Use PASF to Craft Blog Angles

1. Start With a Seed Keyword

Begin with a broad keyword relevant to your brand or niche.

Example: Let’s say your primary topic is: Email Marketing Tools.

Google it. Click on a few organic results, then quickly return to the results page. You’ll now see “People Also Search For” suggestions such as:

Best free email marketing tools

Email marketing vs. CRM

Email marketing strategy examples

How to measure email campaign ROI

These related phrases give you multiple blog angles to explore, based on actual user intent.

2. Validate the PASF Suggestions

Don’t blindly create content around every suggestion. Check:

Search volume using tools like Google Keyword Planner or Ahrefs

Competition levels

Relevance to your niche and offering

This helps ensure your blog topics are both searchable and valuable to your audience.

3. Map Suggestions Into Angles and Subtopics

Instead of turning every PASF phrase into a separate blog post, map them into content clusters. This way, your blog structure serves SEO and user experience.

Example Structure:

Main Post: Email Marketing Tools: Complete Guide for 2025 Supporting Blog Angles from PASF:

Email Marketing vs. CRM: Which Should You Choose?

How to Measure Email Marketing ROI: A Step-by-Step Guide

10 Free Email Tools for Beginners

You now have pillar and cluster content — an ideal structure for SEO.

4. Use PASF in Content Optimization

Already written a blog post? Use PASF queries to:

Add FAQs using the related queries

Optimize H2 and H3 headings

Improve internal linking between related posts

Include PASF terms in your meta descriptions or alt text

This makes your post more aligned with how users explore and improves the chances of ranking for long-tail variations.

5. Keep Tracking Changes in PASF Suggestions

PASF data evolves as user behavior changes. Revisit your key blog topics every few months to see if new PASF suggestions appear. This gives you a chance to:

Update your content

Add new sections or sidebars

Build spin-off blog posts targeting new queries

Tools to Help You Scale PASF Research

While you can manually check PASF in Google, some tools simplify the process:

AlsoAsked.com – Visualizes question trees around your topic

Keyword Insights – Automates cluster creation

Keywords Everywhere – Shows PASF data in real-time as you search

These tools make it easier to scale ideation and spot valuable patterns.

Conclusion: Use PASF to Write Smarter, Not Just More

In a crowded content landscape, blogging success isn’t just about volume — it’s about relevance and strategy. Using “People Also Search For” data gives you a real look into the minds of your audience, helping you craft blog angles that match how users think, search, and click.

And while pay per click services provide short-term visibility, PASF-inspired blog posts can deliver sustained, long-tail SEO traffic for months or even years. If you want your content marketing to go beyond the basics, start listening to what your audience is also searching for.

[small-bizz-press]

Structured Data, Schema, and the Future of AI-Readable Content

Introduction

As AI-driven platforms like Google SGE, ChatGPT, Bing Copilot, and voice assistants reshape how content is discovered and presented, structured data has become the backbone of visibility.

In a world where language models rely on context and structure—not just keywords—schema markup is your key to making content machine-readable, trustworthy, and reference-worthy.

This article breaks down how structured data works, why it matters more than ever in the era of LLMs, and how B2B companies can implement schema to boost AI discoverability and authority.

What Is Structured Data?

Structured data refers to a standardized format (typically using Schema.org) that tells search engines and AI tools what your content means, not just what it says.

It helps algorithms:

Identify page content (e.g., “This is a service,” “This is a review,” “This is a location”)

Understand entities like businesses, people, products, and events

Connect your brand to broader knowledge graphs

The format is often implemented in JSON-LD, embedded in the HTML of a web page.

Why Structured Data Is Crucial for AI Visibility

✅ 1. It Enables AI Summarization and Citations

AI tools use structured data to:

Understand the context and credibility of a page

Attribute content to specific entities or people

Create summaries and comparisons with higher confidence

Without structured data, your content might not be indexed properly by LLMs, even if it's well-written.

✅ 2. It Boosts Google SGE & Rich Result Inclusion

Google’s AI-generated results frequently reference content that:

Uses schema for clarity

Provides clear answers to common questions

Is linked to recognized entities (brands, people, products)

Schema improves your chances of appearing in:

Featured snippets

SGE-generated answer panels

Knowledge panels and entity boxes

✅ 3. It Future-Proofs Your Website for Voice and Chat Interfaces

AI assistants (e.g., Google Assistant, Alexa, Siri, ChatGPT voice) need structured inputs to:

Provide accurate answers

Reference brands confidently

Handle follow-up questions

Structured data acts as a translation layer between your website and AI interfaces.

Core Schema Types Every B2B Business Should Use

Schema TypePurposeOrganizationDefines your business name, logo, contact info, social linksLocalBusinessAdds NAP info, service areas, hours for Google Maps/SGEProduct/ServiceDescribes offerings, use cases, benefits, pricesFAQPageMarks up frequently asked questions and answersReview/RatingSurfaces testimonials or case study summariesArticle/BlogPostingClarifies authorship, publish date, topicWebPageDefines canonical URL and page-specific detailsPersonUseful for expert-driven brands (show bios, credentials)

Pro Tip: Use tools like Schema Markup Validator to test your structured data.

Examples of Structured Data in Practice

✅ B2B Cybersecurity Firm:

Uses:

Organization schema for company identity

Service schema for penetration testing, compliance audits

FAQPage schema for “What is SOC 2?” questions

Result: Higher inclusion in AI responses when users ask about “best SOC 2 auditors for fintech companies.”

✅ SaaS Vendor for Logistics:

Uses:

Product schema for software features

Review schema to showcase customer feedback

LocalBusiness schema to target regional clients

Result: Appearances in SGE panels and Bing summaries for “supply chain SaaS providers near me.”

How to Implement Structured Data on Your Website

🛠️ 1. Add JSON-LD Markup to Your Header

Embed schema code in the <head> section or body of relevant pages.

Example:

jsonCopy

Edit

{ "@context": "https://schema.org", "@type": "Organization", "name": "Acme B2B Solutions", "url": "https://www.acmeb2b.com", "logo": "https://www.acmeb2b.com/logo.png", "sameAs": [ "https://www.linkedin.com/company/acmeb2b", "https://twitter.com/acmeb2b" ], "contactPoint": { "@type": "ContactPoint", "telephone": "+1-800-123-4567", "contactType": "Customer Service" } }

🧱 2. Use a Schema Plugin (for WordPress or CMS Sites)

Top options:

Rank Math (WordPress)

Yoast SEO (includes basic schema)

Schema App Structured Data (enterprise-level)

✍️ 3. Mark Up Content Types with Clear Hierarchy

Use FAQPage schema only when the content is truly Q&A format

Add author, datePublished, and headline to all blog posts

Be consistent in naming and referencing your company and people

Schema for AI vs. Schema for SERPs

Traditional schema focused on:

Winning rich snippets

Increasing CTR from search

AI-era schema focuses on:

Making content machine-readable

Increasing inclusion in summaries

Establishing entity clarity and credibility

Both are still important—but the balance is shifting.

Common Mistakes to Avoid

❌ Using outdated schema types (e.g., Blog instead of BlogPosting)

❌ Marking up in-line content incorrectly (like a paragraph as a product)

❌ Leaving out schema on “about,” “services,” and “case study” pages

❌ Over-optimizing with spammy structured data

❌ Not linking schema to real-world entities (e.g., Wikipedia, LinkedIn)

Future Trends in Structured Data and AI

TrendWhy It MattersEntity linkingAI relies on connections to known data sets (Wikidata, Freebase)Voice search schemaSpeakable markup helps with AI-read audio responsesReal-time updatesSchema signals may be refreshed more often by LLMsMultimodal dataStructured metadata for images, video, and audio will gain importanceContent provenanceSchema for authorship and source credibility will help combat AI misinformation

Conclusion

As generative AI becomes the front door to your B2B brand, structured data is no longer optional—it’s essential.

If you want ChatGPT, Bing Copilot, and SGE to:

Understand what your company does

Include you in summaries

Recommend your solutions to potential buyers

…you need to give them clean, clear, and complete schema markup to work with.

This is how you move from being searchable to being summarized.

[damilola-doodles]

Project Title: Advanced knowledge graph construction and anomaly detection with pandas.

This project builds an Enterprise‑Scale Knowledge Graph by fusing tabular records, textual logs, and network data entirely in Pandas, augmented with graph analytics, NLP embeddings, temporal features, and anomaly detection pipelines for actionable insights. The code leverages Dask for scalability, NetworkX and Faiss for graph operations, HuggingFace transformers for text, PyOD for outlier…

[dammyanimation]

Project Title: Advanced knowledge graph construction and anomaly detection with pandas.

This project builds an Enterprise‑Scale Knowledge Graph by fusing tabular records, textual logs, and network data entirely in Pandas, augmented with graph analytics, NLP embeddings, temporal features, and anomaly detection pipelines for actionable insights. The code leverages Dask for scalability, NetworkX and Faiss for graph operations, HuggingFace transformers for text, PyOD for outlier…

[damilola-ai-automation]

Project Title: Advanced knowledge graph construction and anomaly detection with pandas.

This project builds an Enterprise‑Scale Knowledge Graph by fusing tabular records, textual logs, and network data entirely in Pandas, augmented with graph analytics, NLP embeddings, temporal features, and anomaly detection pipelines for actionable insights. The code leverages Dask for scalability, NetworkX and Faiss for graph operations, HuggingFace transformers for text, PyOD for outlier…

[damilola-warrior-mindset]

Project Title: Advanced knowledge graph construction and anomaly detection with pandas.

This project builds an Enterprise‑Scale Knowledge Graph by fusing tabular records, textual logs, and network data entirely in Pandas, augmented with graph analytics, NLP embeddings, temporal features, and anomaly detection pipelines for actionable insights. The code leverages Dask for scalability, NetworkX and Faiss for graph operations, HuggingFace transformers for text, PyOD for outlier…

[damilola-moyo]

Project Title: Advanced knowledge graph construction and anomaly detection with pandas.

This project builds an Enterprise‑Scale Knowledge Graph by fusing tabular records, textual logs, and network data entirely in Pandas, augmented with graph analytics, NLP embeddings, temporal features, and anomaly detection pipelines for actionable insights. The code leverages Dask for scalability, NetworkX and Faiss for graph operations, HuggingFace transformers for text, PyOD for outlier…

[keploy]

Top AI Tools and Libraries for Java Developers in 2025

AI is becoming a crucial part of how we build software. From recommending products to helping businesses predict what their customers might need, AI is changing how we think about building and using apps and software’s.

The wide variety of libraries and frameworks available in the Java ecosystem will bring developers powerful, specific tools for creating AI applications that enable a variety of subjects, including machine learning and natural language processing. Whether you’re just a beginner with machine learning or looking to level up your current projects, this blog will help you to go through some of the best AI tools and libraries available to Java developers today.

Want to dive deeper into effective Java unit testing?

AI for Java: key benefits and strength

Here are the key benefits of using AI for Java Development, it includes:

• Code Completion: Ai tools can understand what you are writing and it provides suggestions as you type. You will spent less time in debugging, fewer typos, and more focusing on actual problem you are solving .

• Automated Testing: Ai can generate test cases for your java code automatically which means more reliable apps and less time spent writing repetitive test codes.

• Good for Learning Purpose: Ai helps junior developer or someone new to java by giving documentation and explanation. Instead of searching on Stack Overflow, learners can just ask to Ai assistant and you will be provided with relevant answers right when you need them.

There are many libraries that enable AI process that belongs to rich ecosystem of Java. Here are a few of the most notable libraries:

Discover how AI is transforming the way developers code. Explore the top AI coding tools.

Key Features:

• Scalability: It can be scaled easily from a single node up to a large-scale distributed system, which means you can use it to solve complex deep learning problems with ease. • Integration: It is integrated with popular big data frameworks such as Hadoop and Apache Spark for efficient data processing. • Flexibility: It supports many types of neural network, including feedforward neural networks and complex convolutional and recurrent neural networks.

Use Cases:

• Computer Vision: Image recognition, object detection are also done in deeplearning4j. • Anomaly detection: Discovering unusual behaviour in the data set. • Recommendation Systems: Recommending products or content to users.

Key Features:

• Tokenization: It breaks down text into single words, punctuations, or other units. • Detection of sentences: Detection of the start and ending position of each sentence within some block of text. • Parsing: Analyzes the grammatical structure of sentences.

Use Cases:

• Text classification: Categorizing documents, sentiment analysis, or spam detection. • Tokenization: Breaking text into words, sentences or paragraphs. • Language Detection: Identifying the language of a text.

Weka (Waikato Environment for Knowledge Analysis) is a free, open-source machine learning and data mining software written in Java. It’s widely used for data preprocessing, classification, regression, clustering, association rules, and visualization. Weka can be use through its GUI, As a Java API or even from the command line interface.

Key Features:

• Data Preprocessing: Its includes tools for data cleaning, normalization, attribute selection, discretization and handling missing values, preparing dataset for analysis. • Visualization Tools: Features visualization like scatter plots, histograms and decision tree graphs to help users explore and interpret data. • API For Java Integration: It can be embedded in Java Application.

Use Cases:

• Machine Learning Research: Testing and comparing algorithm performance. • Drug Discover: Analysing molecular data to identify potential drug candidates. • Sentiment Analysis: Classifying customer feedback and reviews.

Spring AI is an application framework developed by the Spring team to simplify the creation of AI- powered applications in Java easier and more efficiently. It provides tools to integrate AI functionalities- such as chat models, text-to-image generation, chat completion, audio transcription and embeddings into your projects without unnecessary complexity.

Spring AI brings these concepts for the java ecosystem, focusing on portability, modularity, and ease of use with spring boot.

Key features:

• Portable APIs: Spring AI supports multiple AI providers including OpenAI, Azure OpenAi for chat, image generation, and embedding models. • Abstraction: It offers an interface like ChatClient to interact with Ai models. • Spring Boot Integration: Spring AI comes with auto-configuration and starters allowing you to quickly set up AI features in Spring Boot applications with less setup.

Use Cases:

• LLM integration: It Easily connects your applications to large language models like OpenAI’s GPT models. • Prompt Engineering: Managing and optimizing prompt to��enhance the performance of your AI models. • AI powered applications: Build smarter applications with AI features, such as automated customer support, content generation, Personalized recommendations and more.

GitHub Copilot is an AI pair programmer or a coding assistant that integrates with IDEs to help Java Developers write code faster and accurately. It offers intelligent code suggestions, writes code for you, and generates test cases which makes development more efficient. It works inside your code editor like VS Code or Jetbrains IDEs and supports many programming languages as well.

Key Features:

• Test generation: Copilot can create an entire Junit test case for your code based on the implementation and it helps in achieving better test coverage with less effort. • Framework Assistance: It offers suggestions for popular Java frameworks like Spring, Hibernate, and Jakarta EE. • Code Completion: Copilot provides context-aware suggestions for methods, classes, and code implementations as you type. It also understands Java syntax, conventions, and common patterns.

Use Cases:

• Code completion: It provides real-time suggestions as you code. • Learning from suggestions: GitHub Copilot can be used as a learning tool to discover new patterns, techniques and different approaches in java. • Beneficial for Developers: It can be used for generating the code while building AI applications.

Tabnine is an AI-powered code completion tool that uses deep learning algorithms to predict and suggest code snippets as you type. By analyzing vast amounts of code data, tabnine streamlines the coding process, allowing developers to focus on creativity and problem-solving rather than the minor syntax.

It works with multiple programming languages, including Java, and it works smoothly with popular IDEs like IntelliJ IDEA, VS Code, and Eclipse.

Key Features:

• Intelligent Code Completions: Tabnine offers context-aware code suggestions, helping you write code faster by predicting the next snippet based on your current context. • Framework-Specific Features: Tabnine integrates with a wide range of libraries and tools. It ensures that developers can leverage AI without altering in their existing workflow. • Privacy and Security: It offers code security for local AI models that run directly on your machine to ensure code stays private and secure.

Use Cases:

• Prevention of error: It helps to correct syntax and logic errors early. • High speed: The suggestions appear in milliseconds, that makes our work faster and smoother.

Conclusion:

The tools and libraries that I wrote in this blog are useful for a wide range of tasks in AI and machine learning, including deep learning and neural networks, natural language processing, etc. The choice of tools or library will depend on the specific needs of your project and the type of AI project you are working on.

These libraries are updated regularly as AI continues to evolve to include the latest research and techniques. These tools are useful for developers to build useful applications that use artificial intelligence to automate tasks and enhance productivity.

FAQs:

What makes Java a good choice for AI development?

Java is a good choice for AI development because it is robust, platform-independent, has a strong community, and offers many libraries and frameworks for AI projects.

Can AI tools for coding be used by beginners?

Yes, AI tools for coding are suitable for beginners as they help correct errors and speed up the coding process by giving suggestions to users.

What are the drawbacks of using Java for AI development?

Java isn’t as popular as Python for AI because it doesn’t have as many libraries or a large support community. Plus, its longer and more complex code can make it harder to test faster and make changes, which is a big part of working in AI.

What are the future of AI in Java ?

Java is growing faster in the AI, with new libraries emerging and smoother integration with popular machine learning frameworks. By 2026, you can expect java to work even more effortlessly with tools like Deeplearning4j and have smarter, AI-integrated features build into the IDEs.

[cybersecurityict]

Semantic Knowledge Graphing Market Size, Share, Analysis, Forecast, and Growth Trends to 2032: Transforming Data into Knowledge at Scale

The Semantic Knowledge Graphing Market was valued at USD 1.61 billion in 2023 and is expected to reach USD 5.07 billion by 2032, growing at a CAGR of 13.64% from 2024-2032.

The Semantic Knowledge Graphing Market is rapidly evolving as organizations increasingly seek intelligent data integration and real-time insights. With the growing need to link structured and unstructured data for better decision-making, semantic technologies are becoming essential tools across sectors like healthcare, finance, e-commerce, and IT. This market is seeing a surge in demand driven by the rise of AI, machine learning, and big data analytics, as enterprises aim for context-aware computing and smarter data architectures.

Semantic Knowledge Graphing Market Poised for Strategic Transformation this evolving landscape is being shaped by an urgent need to solve complex data challenges with semantic understanding. Companies are leveraging semantic graphs to build context-rich models, enhance search capabilities, and create more intuitive AI experiences. As the digital economy thrives, semantic graphing offers a foundation for scalable, intelligent data ecosystems, allowing seamless connections between disparate data sources.

Get Sample Copy of This Report: https://www.snsinsider.com/sample-request/6040 

Market Keyplayers:

Amazon.com Inc. (Amazon Neptune, AWS Graph Database)

Baidu, Inc. (Baidu Knowledge Graph, PaddlePaddle)

Facebook Inc. (Facebook Graph API, DeepText)

Google LLC (Google Knowledge Graph, Google Cloud Dataproc)

Microsoft Corporation (Azure Cosmos DB, Microsoft Graph)

Mitsubishi Electric Corporation (Maisart AI, MELFA Smart Plus)

NELL (Never-Ending Language Learner, NELL Knowledge Graph)

Semantic Web Company (PoolParty Semantic Suite, Semantic Middleware)

YAGO (YAGO Knowledge Base, YAGO Ontology)

Yandex (Yandex Knowledge Graph, Yandex Cloud ML)

IBM Corporation (IBM Watson Discovery, IBM Graph)

Oracle Corporation (Oracle Spatial and Graph, Oracle Cloud AI)

SAP SE (SAP HANA Graph, SAP Data Intelligence)

Neo4j Inc. (Neo4j Graph Database, Neo4j Bloom)

Databricks Inc. (Databricks GraphFrames, Databricks Delta Lake)

Stardog Union (Stardog Knowledge Graph, Stardog Studio)

OpenAI (GPT-based Knowledge Graphs, OpenAI Embeddings)

Franz Inc. (AllegroGraph, Allegro CL)

Ontotext AD (GraphDB, Ontotext Platform)

Glean (Glean Knowledge Graph, Glean AI Search)

Market Analysis

The Semantic Knowledge Graphing Market is transitioning from a niche segment to a critical component of enterprise IT strategy. Integration with AI/ML models has shifted semantic graphs from backend enablers to core strategic assets. With open data initiatives, industry-standard ontologies, and a push for explainable AI, enterprises are aggressively adopting semantic solutions to uncover hidden patterns, support predictive analytics, and enhance data interoperability. Vendors are focusing on APIs, graph visualization tools, and cloud-native deployments to streamline adoption and scalability.

Market Trends

AI-Powered Semantics: Use of NLP and machine learning in semantic graphing is automating knowledge extraction and relationship mapping.

Graph-Based Search Evolution: Businesses are prioritizing semantic search engines to offer context-aware, precise results.

Industry-Specific Graphs: Tailored graphs are emerging in healthcare (clinical data mapping), finance (fraud detection), and e-commerce (product recommendation).

Integration with LLMs: Semantic graphs are increasingly being used to ground large language models with factual, structured data.

Open Source Momentum: Tools like RDF4J, Neo4j, and GraphDB are gaining traction for community-led innovation.

Real-Time Applications: Event-driven semantic graphs are now enabling real-time analytics in domains like cybersecurity and logistics.

Cross-Platform Compatibility: Vendors are prioritizing seamless integration with existing data lakes, APIs, and enterprise knowledge bases.

Market Scope

Semantic knowledge graphing holds vast potential across industries:

Healthcare: Improves patient data mapping, drug discovery, and clinical decision support.

Finance: Enhances fraud detection, compliance tracking, and investment analysis.

Retail & E-Commerce: Powers hyper-personalized recommendations and dynamic customer journeys.

Manufacturing: Enables digital twins and intelligent supply chain management.

Government & Public Sector: Supports policy modeling, public data transparency, and inter-agency collaboration.

These use cases represent only the surface of a deeper transformation, where data is no longer isolated but intelligently interconnected.

Market Forecast

As AI continues to integrate deeper into enterprise functions, semantic knowledge graphs will play a central role in enabling contextual AI systems. Rather than just storing relationships, future graphing solutions will actively drive insight generation, data governance, and operational automation. Strategic investments by leading tech firms, coupled with the rise of vertical-specific graphing platforms, suggest that semantic knowledge graphing will become a staple of digital infrastructure. Market maturity is expected to rise rapidly, with early adopters gaining a significant edge in predictive capability, data agility, and innovation speed.

Access Complete Report: https://www.snsinsider.com/reports/semantic-knowledge-graphing-market-6040 

Conclusion

The Semantic Knowledge Graphing Market is no longer just a futuristic concept—it's the connective tissue of modern data ecosystems. As industries grapple with increasingly complex information landscapes, the ability to harness semantic relationships is emerging as a decisive factor in digital competitiveness.

About Us:

SNS Insider is one of the leading market research and consulting agencies that dominates the market research industry globally. Our company's aim is to give clients the knowledge they require in order to function in changing circumstances. In order to give you current, accurate market data, consumer insights, and opinions so that you can make decisions with confidence, we employ a variety of techniques, including surveys, video talks, and focus groups around the world.

Contact Us:

Jagney Dave - Vice President of Client Engagement

Phone: +1-315 636 4242 (US) | +44- 20 3290 5010 (UK)

[thatware03]

Unlocking the Future of Search: AEO for Google Gemini and AEO for Bard

Introduction

As the digital landscape evolves, so does the way we approach search engine optimization (SEO). Traditional SEO is no longer enough to dominate the SERPs. With the rise of generative AI and conversational interfaces, Answer Engine Optimization (AEO) is quickly becoming the next frontier. At ThatWare LLP, we’re at the cutting edge of this transformation—developing powerful strategies to optimize for AI-driven platforms like Google Gemini and Google Bard.

In this blog, we’ll explore what AEO for Google Gemini and AEO for Bard means, why it's crucial in 2025, and how your business can stay ahead of the curve.

What Is AEO and Why Does It Matter?

Answer Engine Optimization (AEO) is the process of structuring and optimizing content so that it directly answers user queries in a format easily understood and extracted by AI systems, voice assistants, and large language models (LLMs). Unlike traditional SEO, AEO prioritizes precise, context-rich, and authoritative responses.

As generative AI becomes the new search interface, being the top answer, not just the top link, is the goal.

AEO for Google Gemini: A New SEO Paradigm

Google Gemini is Google's next-generation AI model, integrated into its search and productivity tools. It's deeply embedded in Search, Android, and other core products—reshaping how people interact with information.

Optimizing for Gemini involves:

Semantic structuring of content for contextual understanding.

Entity-based SEO to align with Google's knowledge graph.

Use of vector embeddings to make content machine-readable for Gemini’s multimodal capabilities.

AI-aligned markup (like schema.org) to help Gemini extract accurate responses.

At ThatWare LLP, our proprietary AI tools are already tuned to craft content that aligns with Gemini’s retrieval mechanisms, ensuring better visibility in AI-generated snippets and summaries.

AEO for Bard: Conversational AI Meets SEO

While Gemini powers the backend, Google Bard is the user-facing chatbot interface. Bard takes search from keyword-based queries to natural, conversational prompts—and the answers it delivers are powered by AEO-rich content.

To optimize for Bard:

Answer questions directly and conversationally.

Incorporate FAQs and structured Q&A formats on your site.

Focus on topical authority and E-E-A-T (Experience, Expertise, Authoritativeness, and Trust).

Create content designed for zero-click searches.

ThatWare LLP’s AI-driven SEO strategies ensure your content ranks not just in search, but in conversation—where Bard chooses its responses from high-quality, answer-optimized sources.

Why Businesses Need to Adapt Now

Failing to adapt to AEO means falling behind as AI becomes the dominant gateway to information. Google Gemini and Bard are already shifting user behavior from link-clicking to answer-consuming. If your content isn’t designed for this future, your online visibility will drop—fast.

With ThatWare LLP’s AI and NLP expertise, we offer tailored AEO solutions that help you:

Rank in AI-generated answers

Outperform competitors in AI chatbots

Generate leads through conversational search

Final Thoughts

The age of generative AI is here, and AEO for Google Gemini and AEO for Bard are no longer optional—they’re essential. At ThatWare LLP , we specialize in merging AI, SEO, and data science to help brands thrive in this new search paradigm.

[cm-shorts]

Semantic Entropy

Semantic entropy is a way to describe how varied or rich the meanings are in a collection of ideas. Imagine you’re having a conversation with someone who jumps between very different topics—talking about music, then philosophy, then cooking, then physics. That conversation would feel wide-ranging and full of variety. In contrast, a conversation that stays strictly within one topic, like only discussing the rules of chess, would feel more narrow and predictable. Semantic entropy tries to capture this difference, not in conversations, but in how an artificial system handles concepts.

In the context of AI and knowledge graphs, each idea or “node” in the graph has a meaning. These meanings are not just labels like “apple” or “planet”—they are embedded in a multidimensional space based on how a language model understands relationships between words and concepts. Two concepts that are similar in meaning are close together in this space; very different concepts are far apart. If an AI system builds a network where all the concepts are similar and close together, it doesn’t take much mental effort to connect them. That’s low semantic entropy. But if the system draws from a wide range of meanings—linking distant and unexpected ideas—then the overall richness increases. That’s high semantic entropy.

To make this measurable, the system first places each node in this meaning space using a language model. Then, it builds a “semantic adjacency matrix” that records how close in meaning each pair of nodes is. It’s like a giant table where each entry shows how much two concepts have in common. From this matrix, the system creates a simplified map of how meanings are distributed overall—similar to how a weather map shows where it's hot or cold. The diversity in this distribution is what semantic entropy reflects.

What makes semantic entropy interesting is that it doesn’t describe structure—it describes possibility. It tells us about the mental or conceptual landscape available to the system, not how the system has chosen to connect things. The system may still be very selective and only build a few connections, but if the concepts it’s working with come from all over the idea space, then there’s a lot of untapped potential. That potential is the core of what semantic entropy measures.

In the study of creative AI systems, semantic entropy plays a special role. It turns out that when semantic entropy is slightly higher than structural order, the system stays in a sweet spot. It keeps discovering new and interesting relationships without falling into total randomness. This balance supports continuous exploration. It’s as if the system is always surrounded by meaningful possibilities, even though it only builds a few new connections at each step. And because it’s always surrounded by these varied options, it never runs out of creative directions to go in.