Adot: The Real Web3 Search Engine
Adot is a decentralized Web3 search engine with the goal of redefining how data is collected, organized and delivered. Adot sorts, aggregates, and indexes data from on-chain and off-chain sources, and enables users to customize a personalized search experience through data ownership they control.
Adot is committed to making all high-quality data publicly available to every developer and user, and building a decentralized search engine that completely surpasses traditional search engines.
In addition, Adot also hopes to build a composable, open source and indexing ecosystem, so that users can be rewarded and contribute data source input for accurate, scalable and indexed data within seconds. At the same time, developers can also build their own search engines and recommendation systems based on the Adot SDK and API.
Adot offers a decentralized protocol called Adot Search that allows builders to index, query and freely combine data from blockchain and traditional internet sources. Adot Search is a decentralized general intelligent search engine that provides users and developers with all the necessary functions and services needed to interact with Web3.
Adot also provides efficient data tools, implement Adot indexing technology into your ideas and build your own projects, aiming to promote the adoption of Adot infrastructure and help collect decentralized data in the network to promote ecological development and internal economic activity.
Adot's goal is to build a complete and effective search ecosystem, providing decentralized indexing protocols, smart search applications, and open source data frameworks for the entire Web3 industry.
Introduction to Application Scenarios
Adot has a wide range of application scenarios and can provide developers with powerful search functions and complete data sets in different fields, including social, aggregation, publishing, analysis, and artificial intelligence.
Adot can analyze social user identities and aggregate social data, and generate relationship graphs and knowledge networks based on blockchain wallet addresses, thereby helping Web3 social applications build powerful search functions and complete data sets.
Adot is able to aggregate relevant information to enrich reference content about tokenized assets, thereby helping traders browse and trade assets, and potentially increase the value of assets.
Adot aims to create a more efficient distribution channel for content, empowering creators economically. Adot breaks through the barriers between applications and protocols, and improves traditional ranking algorithms, bringing exponential traffic and even royalties to creators.
Adot indexes a wide variety of data sources, making it a powerful analytical reference. Whether it is analyzing the liquidity of NFT projects based on transaction data, or in-depth analysis of newly established communities based on social data, Adot can satisfy users who are eager for clean, dense, complete and well-structured data.
5. Artificial intelligence
Adot is integrating AI into the search engine. Using the categorized, structured, and diverse data available in search engines, AI can return more accurate, personalized, interactive, and even human-like output. At the same time, these data can also give AI the ability to train models.
Adot's technical architecture mainly includes the following levels:
1. Hybrid data acquisition layer
The goal of this layer is to obtain Web3-related data from various storage media. These data can come from Ethereum transaction data, Arweave decentralized Lens posts, Twitter data on centralized servers, user data in browsers, etc.
2. Structured analysis layer
This layer enables search engines to provide more signal support for searches by analyzing the original data structure. Each individual data point is called a "page", which can be articles, social posts, transaction data or NFT metadata, etc. Compared with the way Web2 search engines extract unstructured information (such as splitting text into words), Adot, as a Web3 search engine, can identify the complex structure of each page and establish a structured data system, making it easier to build knowledge maps and improve The quality of search results and helps clarify the owner of each data, thereby rewarding data providers with a reliable token economic mechanism.
3. AI model layer
This layer utilizes raw data and parsed content to generate depth signals, generates human-readable and machine-readable labels through natural language processing techniques, and annotates non-text content. Additional computed ranking signals further improve the precision of search algorithms, and users' search histories are also processed to generate information such as user tags and embeddings.
4. Structured index layer
This layer builds its index following the indexing protocol based on the output of the content parser and modeling. Indexing is the key for each search engine to search billions of pages and return relevant results to users within milliseconds. Adot uses ElasticSearch (ES) to create basic indexes, but the traditional indexing process only breaks down content into unstructured text (i.e. bag of words), not structured knowledge. Therefore, Adot improved the ES index to integrate the page structure and knowledge graph, so that it can handle complex search query structures and arrange search results more precisely with richer information.
5. Search algorithm layer
When a user enters a query, the search engine retrieves relevant pages from the index and uses search algorithms to aggregate these pages into a list of results that the user can view. The query processor uses natural language processing techniques to analyze the explicit keywords and implicit context of the query to fully understand what the user is looking for. Structured queries also allow users and developers to describe their needs more precisely. Multi-tier ranking techniques are used over traditional search engines.
Adot Search is a decentralized intelligent search engine based on Web3, open to everyone. It acts as a portal into Web3 for users and developers, providing all the necessary features and services needed to interact with the Web. Adot's search engine architecture is divided into five levels:
1. Hybrid data acquisition layer
This layer is designed to stream all data on Web3 into search engines, no matter where the data is stored. Adot has invented a hybrid system that combines different methods to efficiently acquire data from different sources. Web3 crawlers work best against data stored on central servers such as Twitter or Medium. Users and developers can contribute to this layer by uploading data, hosting crawler nodes, or sending personal user data.
2. Structure analysis layer
The acquired data is initially stored in many different protocols such as HTML, text, Lens protocol, Twitter format, etc. This layer parses the structure of the raw data, establishes connections between them, and converts them into an indexing protocol. The unified data structure is easy for developers to use and easy for users to read. Developers can contribute by annotating the raw structure of their data and generating parsed content based on the indexing protocol.
3. AI modeling layer
Well-structured data contains valuable knowledge. This layer applies AI techniques to mine deep knowledge from data and generate additional signals. For example, create an embed for each image to make the images searchable. Developers can generate labels by running their own AI models. Users can manually tag content and users based on their knowledge.
4. Structured index layer
Each parsed content, generated tag or signal is generated offline and cannot be directly used to provide results to users in real time. Indexes connect offline data and online computation. Adot invented a structured index not only for regular content, but also for content structure and knowledge graph to improve search accuracy.
5. Search algorithm layer
This layer implements complex algorithms to process user-entered search queries, search for relevant data from the index, run some real-time calculations, and return ranked results. Developers and users can contribute their own search algorithms or define rules to customize their search engine experience. These algorithms are plugged into the overall search infrastructure and return customized results to developers and users.
Adot is a very promising Web3 search engine. Its goal is to redefine the way data is collected, organized and delivered, and to provide a decentralized intelligent search application that completely surpasses traditional search engines. With the continuous development of blockchain technology and the Web3 ecosystem in the future, we believe that Adot will become an important infrastructure, providing services such as decentralized indexing protocols, intelligent search applications, and open source data frameworks for the entire industry.
At the same time, Adot has a wide range of application scenarios, and has great development potential in social networking, aggregation, publishing, analysis, and artificial intelligence. We look forward to seeing even more success and progress in the future development of Adot.