From Telegram Groups to a Knowledge Graph: Structuring Messages and People

The Challenge of Unstructured Chat Data

Telegram channels and group chats (and similarly WhatsApp groups or Facebook groups) are rich in content – ideas, discussions, and contacts – but they suffer from a lack of structure. Valuable messages often get buried in the flow of conversation, and important insights or questions remain unorganized. As a result, messages get lost in the stream, important ideas remain scattered, interested contacts are hard to track, and it’s nearly impossible to quickly retrieve specific information or identify engaged people. This is not just an inconvenience for casual users; it can be especially critical for businesses, where losing track of audience questions or feedback means missed opportunities for engagement and sales. Clearly, there is a need to capture and organize the knowledge hidden in these chat channels.

Building a Knowledge Base from Telegram Channels

Currently, the focus is on Telegram as a primary source, given its popularity for community-building and its accessible API. The solution is to collect data from Telegram channels and groups and transform it into a structured knowledge base. Tools like Conoted exemplify this approach by integrating with Telegram via an official bot and automating the entire process. Using Telegram’s Bot API (for example, creating a bot via @BotFather and linking it to the app), the system can access the channel’s messages, comments, and even member information with the channel owner’s permission. Once connected, the following happens:

• Automatic Extraction of Posts and Comments: All channel posts and their discussion comments are pulled into the system in real-time. Each message or comment becomes an individual data item (a "note" in the knowledge base).

• Note Creation for Each Message: Every message is converted into a note in the knowledge base, preserving its content (text, links, etc.). This makes each piece of information discrete and easy to manage.

• AI Tagging and Content Linking: Artificial intelligence algorithms analyze each note and automatically assign tags (keywords or topics) to it. For example, a post about running might get tagged as #running or #health. The system also suggests connections between notes that share similar topics or ideas. This reflects a Zettelkasten-inspired methodology, where notes are interlinked to reveal relationships between ideas.

• Linking Notes to Authors: Crucially, each note is linked to the contact (user profile) of the person who posted it, thereby tying content to its creator. The contacts of members who comment or post are synced into the system, and their contributions are attached to their profiles. In effect, the platform builds a knowledge graph that connects not only notes to each other, but also notes to the people who authored or engaged with them.

After these steps, the once chaotic stream of chat messages is transformed into an organized network of notes. Already at this stage, the channel’s feed turns into a convenient content system – instead of endless scrolling through chat history, one can jump from topic to topic and see how ideas connect in a structured way. In other words, the Telegram channel’s content becomes a navigable knowledge base: messages are categorized by topic, related posts are linked together, and everything is accessible through search and tags rather than by manual browsing.

Figure: Conceptual illustration of transforming a Telegram channel’s unstructured chat into structured knowledge and insights. Messages are extracted and tagged (symbolized by the chart and tags on screen), and user interactions are mapped (depicted by the target icon connecting to user profile icons), enabling targeted knowledge retrieval and engagement.

Linking Notes to People: A Social Knowledge Graph

An important aspect of this structured approach is that it doesn’t treat knowledge as isolated bits of text – it recognizes that knowledge is tied to people and their context. In fact, knowledge management experts emphasize that information gains value when connected to the people who hold or seek that knowledge. By linking each note to its author or contributors, the system creates a social knowledge graph. This means for any given topic or note, one can see who was involved in that conversation or idea.

Connecting notes with their human sources has multiple benefits. First, it helps in tracking the context of information: not only can you retrieve the content of a message, but you can also see which member shared it and engage with them if needed. For instance, if someone in your Telegram group shared a detailed solution on “digital marketing strategies,” the note will be connected to that user’s profile. Later, you can easily find the person with that expertise or interest when you need to follow up or collaborate. In a traditional chat, you might forget who said what; here the authorship is preserved and made searchable. In fact, the system allows you to “easily find people you’ve discussed certain topics with” for future collaboration or networking.

Second, linking knowledge to people enables identification of subject matter experts or highly interested individuals within your community. The knowledge base can even recommend relevant experts for a note’s topic by analyzing the social graph (who frequently discusses or contributes to that topic). In other words, the app can highlight users who are most active or knowledgeable about X topic, effectively turning your contact list into a living index of expertise.

This social dimension turns a static archive of messages into a dynamic community resource. It echoes well-known knowledge management principles, such as Nonaka’s theory that knowledge is created through social interaction between explicit information and tacit experience. By linking notes to the people behind them, the system fosters a community of practice around topics – you don’t just collect facts, you also connect with those who contributed the facts, enabling further dialogue and exchange of tacit knowledge. Knowledge no longer “exists in a vacuum,” separate from its community; instead, your knowledge base inherently includes a social context. This is a powerful way to build collective intelligence.

Finding Information and Maintaining Context

One of the immediate advantages of structuring chat data into tagged notes is vastly improved information retrieval. Rather than searching manually through thousands of messages or relying on Telegram’s basic search, users can leverage the knowledge base to quickly find all messages related to a specific topic. For example, if you want to see everything that’s been discussed about “product marketing” in a channel, you can simply search for the #marketing tag or the keyword “marketing” and retrieve an organized list of all relevant notes. This search is context-aware: because the notes are interlinked, you can also see how those “marketing” notes connect to other topics (perhaps “strategy” or “user feedback”), offering a broader context for that subject.

Moreover, the structured format preserves conversational context that would otherwise be lost. Comments that were replies to a post are now part of the same note or linked cluster of notes, so you can read a discussion thread in one place. By moving from one connected note to another (following the AI-suggested links), you effectively follow the trail of a topic’s discussion across the community, rather than dealing with disjointed snippets. This means the context around an idea is maintained – you can start with one note and then see related insights or follow-up questions that were linked to it, painting a full picture of how that topic evolved in the conversation.

The knowledge base thus becomes a living repository of knowledge built from both your own messages and others’ contributions. It’s not just an archive; it’s a structured memory of the community. Over time, as more messages are accumulated from the channel (and even from multiple sources), you are essentially building a knowledge repository that grows and learns. You can incorporate external information too (for instance, Conoted allows integrating public notes from other users to enrich your base) to augment what your community has discussed. All of this leads to better-informed decision making and learning: you can reference past discussions on a topic before bringing it up again, avoid repeated questions by quickly finding previous answers, and synthesize insights from numerous chat interactions.

Crucially, this approach saves time and effort. What used to require combing through chat logs manually is now largely automated and at your fingertips. As the Conoted team puts it, automated structuring means you no longer need to manually transfer or copy-paste information from chats – the system does it for you. The end result is that you have both the details and the big picture: granular messages preserved as notes, and the high-level structure (tags, links, people) that makes the information accessible and meaningful.

Targeted Insights and Finding Key Contacts

Beyond personal knowledge management, structuring chat data unlocks powerful analytical and engagement opportunities – especially for community managers, marketers, or anyone looking to understand and leverage their audience. Because the system keeps track of which users are interested in which topics, one can perform very targeted segmentation. For example, you can identify all members who have shown interest in “AI discussions” or who frequently comment on posts about “finance” with just a query or filter. These individuals are essentially your warm contacts for that topic – you know they care about it, since they’ve engaged with related content.

This capability turns a once-anonymous crowd into discernible segments. Administrators can see that, say, 50 members are actively talking about web3 or that a particular subset of users always engages with posts about running and fitness. Conoted’s system, for instance, automatically tags subscribers by their interests based on their interactions (if a user comments on a #running post, they get tagged with #running in their profile). Over time, each user builds up an interest profile (a set of tags) reflecting what they’ve talked about. The platform can then rank or list contacts by topic, making it easy to find the most relevant people for any given subject. In one use-case, if you run a Telegram channel for online courses, you could instantly pull up everyone who has commented on posts about “marketing courses” and reach out to them with a tailored offer.

The implications are significant for engagement and targeting. Instead of broadcasting a generic message to your entire group, you can craft communications for the specific slice of the audience that is interested. For instance, you might send a personal message or special invitation to users tagged with #finance, since you know they participated in finance discussions. This could be an invitation to a finance-themed webinar, or simply a question to spark further engagement on that topic. Such “topic targeting” enables smart, precise communication with your audience, rather than broad one-size-fits-all announcements. As one guide notes, this is “smart communication with a precise audience” as opposed to advertising to everyone blindly.

From a networking perspective, the knowledge graph helps in finding interesting contacts for collaboration or community building. If you are an individual user, you can find people who have knowledge in areas you’re exploring and potentially connect with them (since you’ve identified them as active in those discussions). If you’re a community manager, you might identify passionate members who could be elevated to moderators or contributors because the data shows they consistently engage on certain topics. In professional settings, sales or marketing teams could use this information as leads – for example, exporting a list of users interested in “cloud services” and then targeting them for a relevant product demo (within the bounds of privacy and platform rules). Indeed, Conoted allows channel owners to export a copy of the tagged contact list (usernames, IDs, and interest tags) for further outreach or analysis. This audience insight and segmentation is incredibly valuable: administrators can understand their subscriber base at a glance (e.g., what percentage are interested in tech vs. art) and tailor content or advertising accordingly.

To illustrate, consider a fitness discussion group where the knowledge base shows that 30% of active members are tagged with #running and 20% with #nutrition. Knowing this, you might organize separate Q&A sessions – one targeting the runners, another for nutrition enthusiasts – thereby directly addressing each group’s interests. Or if you have a new product (like a marathon training plan), you can confidently offer it to the users who have shown interest in running, rather than spamming everyone. This increases relevance and engagement, and users appreciate content that aligns with their interests.

In short, by structuring chat data and preserving the link between topics and people, you gain actionable insights. You can “understand the audience by identifying subscribers’ interests and segment them for targeted interaction”. What was once an indistinct mass of channel subscribers becomes a map of distinct interest groups and key individuals. This not only helps with marketing or community management but also fosters a sense of personal touch – people receive information or opportunities that matter to them. Whether the goal is to drive business (leads, conversions) or simply to build a stronger community, leveraging these targeted insights is a game-changer.

Expanding to WhatsApp and Facebook Groups

While Telegram is a starting point (due to its openness and the availability of bots), the vision is to extend this data-gathering and structuring approach to other popular communication platforms like WhatsApp and Facebook Groups. Each platform has its own ecosystem, but the idea remains the same: collect the valuable conversations from these channels and bring them into the structured knowledge base for unified organization and analysis.

• WhatsApp Groups: WhatsApp is a more closed platform (with end-to-end encryption) but it does offer integration pathways through the WhatsApp Business API and other tools. By leveraging these, one can connect to WhatsApp group chats (with appropriate consent or as an admin) and retrieve the messages. In practice, this might involve linking a WhatsApp Business account or using a service that bridges WhatsApp to external applications. Some CRM systems already do this – they integrate WhatsApp so that chats can be viewed and managed on a central dashboard. For example, it's possible to integrate WhatsApp with a CRM and access all your WhatsApp group conversations from one interface without switching apps. Similarly, for our knowledge base context, a connector could feed group messages into the note-taking system. Once the data is in, the same pipeline applies: each WhatsApp message becomes a note, tags are auto-assigned, and authors (phone contacts) are linked. Imagine all the scattered WhatsApp group discussions (from team chats, customer queries, interest-based groups, etc.) indexed and searchable in one place – it would greatly enhance retrieval of insights that are otherwise locked away in your phone.

• Facebook Groups: Facebook groups often host in-depth discussions and user-generated content on countless topics, and being able to archive and structure that content is equally valuable. Facebook provides APIs for group content (for group admins or via third-party apps) and even simple methods to export data. In fact, it’s possible to export Facebook Group posts and comments to external files (CSV/Excel) using available tools or scripts. This indicates that with the right integration, one can programmatically collect posts, comments, and possibly user information from a Facebook Group. In our context, those posts and comments would be ingested as notes in the knowledge base, just like Telegram messages. Tags would be assigned (perhaps based on post topics or keywords), and contributors’ profiles could be linked (potentially via their Facebook profile names or IDs, respecting privacy settings). The result would be that discussions from a Facebook community – say a niche group for “DIY Electronics” – could be merged into your broader knowledge repository. You could then cross-reference knowledge from that Facebook group with knowledge from your Telegram channels or WhatsApp chats on related topics.

Extending the platform support in this way moves toward a unified knowledge system across multiple social and chat channels. Many of us participate in diverse communities – a Telegram channel for industry news, a WhatsApp group with colleagues, a Facebook group for hobbyists – and important information is spread across all of them. By aggregating these into one structured hub, you ensure that insights gained in one place are not lost or siloed from the others. It also means the interest tagging and people-network features span platforms: for example, you might discover that a contact from your WhatsApp group is also active in a Facebook group on the same topic – indicating a strong interest or expertise. The knowledge base could potentially merge those identities (if linked) or at least let you see a fuller profile of someone’s contributions across platforms.

Of course, each new integration comes with technical and ethical considerations (WhatsApp’s terms, Facebook’s data policies, etc.), but from a conceptual standpoint, the approach is sound. In the end, the goal is to “merge communication channels and converse via a single platform” where all interactions are organized and accessible – essentially breaking down the walls between separate apps to create a holistic knowledge and contact center.

Conclusion

Focusing on data collection from channels and groups and structuring that data into notes and people-linkages can dramatically change how we interact with information. What begins as noisy, ephemeral chat across Telegram, WhatsApp, Facebook, and other platforms can be transformed into a lasting, valuable knowledge base that is both highly structured and richly social. By systematically gathering messages (with user consent and participation) and applying intelligent organization – automatic tagging, linking related ideas, and associating contributions with their authors – we turn chaotic streams of conversation into an asset.

The benefits of this approach are multi-faceted. For individuals, it means no idea or useful tip from a group chat ever slips away – you can always find it later and see it in context. Your personal and community learning is compounded by having all the discussions you care about at your fingertips, neatly organized. You can recall not just what was said, but who said it, enabling you to reconnect with that person when needed. All your notes and chats effectively become a part of your extended memory, “transformed into a structured knowledge base, accessible at any time”. For teams and businesses, the structured chat data provides unprecedented insight into audience behavior and interests. Engagement is no longer a black box – you can pinpoint engaged members, tailor content to what people want, and follow up with warm leads who have signaled interest in your offerings. Marketing and community outreach become data-driven, improving efficiency and fostering a more personal connection with your audience.

By building on this foundation (starting with Telegram and expanding to WhatsApp, Facebook, and beyond), one creates a unified platform for knowledge and relationship management. This approach embodies the idea that knowledge is a network, not just a database. It’s a network of ideas and the people behind those ideas. In practice, that means faster information retrieval, deeper context, stronger communities, and the ability to act on insights that would otherwise be lost in chat history. As the Conoted integration demonstrated, when you “turn chaotic chats into a valuable resource”, you unlock new levels of productivity and engagement. In summary, putting an accent on collecting data from channels and groups – and structuring notes and people – is a forward-thinking strategy to harness the full potential of our everyday communications.