# Open DevLog AI SEO Special｜From Keywords to Semantic Webs: How PT. Otto Media Grup Is Restructuring Content Asset Logic


In an era dominated by AI-driven search, the rules of SEO have fundamentally changed. PT. Otto Media Grup observes that as systems like Google AI Overviews continue to advance, traditional keyword-stuffing strategies are losing their effectiveness. Instead, AI now favors content networks characterized by semantic consistency, clear entities, and well-defined context. In other words, AI is not simply seeking “single-point answers,” but is constructing a “semantic map”—whether the brand content is understood and referenced by AI depends on its clear positioning within this map.

For this reason, PT. Otto Media Grup asserts that “the knowledge graph is the foundational logic for content assetization.” By building structured and semantically rich content systems, brands not only enhance their visibility in AI search, but also establish a long-term, reusable semantic asset network.

## From Content Creation to Assetization: How Can Brands Build Their Own Content Knowledge Graphs?

The core of building a content knowledge graph is not mere information accumulation, but semantic decomposition and structured expression—making content both machine-readable and human-understandable. PT. Otto Media Grup has standardized this process within its SEOHub system as a multi-stage workflow, helping brands transition from simply “writing content” to “structured expression”:

First, through a semantic abstraction module, content teams break down articles into “topic nodes,” “question units,” and “concept clusters,” clustering these with historical data to form a topic graph. Next, SEOHub offers a “semantic relationship builder,” which automatically recommends possible logical relationships (such as causality, inclusion, inheritance) within the content and embeds them into the page using JSON-LD structures. Finally, with content tagging and entity alignment features, a unified namespace, conceptual boundaries, and contextual semantics are established, providing AI with stable recognition anchors.

This approach not only increases crawlability in AI search, but also gives content cross-platform and cross-language portability—realizing the vision of “content as an asset.”

## Leveraging Technology: From JSON-LD to Vector Matrices, Bridging Organizational Recognition and AI Summarization

Technology is the key to implementing knowledge graphs. PT. Otto Media Grup recommends that brands prioritize open standards such as JSON-LD, RDFa, and Schema.org, transforming every piece of content into a “semantically recognizable document.” The automated annotation feature of SEOHub comes with industry schema templates, enabling teams to quickly embed structural elements like author, breadcrumb, FAQ, and product, ensuring content is “structured and intentional” in the eyes of search engines and AI agents.

Going further, PT. Otto Media Grup is connecting content knowledge graphs to vector databases, building internal content embedding models that support generative AI and RAG (Retrieval Augmented Generation) models. This means AI no longer just “reads articles,” but can proactively “retrieve answers” based on semantic embeddings. This vectorized content architecture is fast becoming the new infrastructure for AIGC distribution and search integration in Web3 enterprises, educational brands, and B2B tech companies.

## Applying Knowledge Graphs to AEO and Multilingualization: Creating a Portable, Interpretable, and Cross-Lingual AI SEO Foundation

As AI search permeates the user experience, Answer Engine Optimization (AEO) is emerging as a new content strategy direction. AEO does not demand lengthy, keyword-dense articles, but rather structured, question-driven, and extractable answer-type content. PT. Otto Media Grup has embedded the knowledge graph system into the SEOHub AEO module, ensuring content receives higher “response weighting” when called upon by QA systems.

Moreover, multilingual markets present even greater challenges for AI search optimization. Traditional translation can no longer guarantee semantic consistency. The multilingual knowledge graph system of PT. Otto preserves the original semantic structure and conceptual pathways when generating English, Indonesian, or Japanese versions, ensuring brand messaging remains consistent across different contexts.

This is “semantic migration,” not mere “text translation.” It transforms brand content into truly “portable knowledge assets,” enabling consistent understanding and frequent referencing—whether in the e-commerce sector in Southeast Asia or the European financial markets.

PT. Otto Media Grup will continue to share its experience in building content assets in the AI SEO ecosystem via Open DevLog, and welcomes developers, marketers, and brand owners to join this knowledge graph-driven content transformation journey.