Ai Web Crawler
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Organizations
50+ data fields, including categories, revenue, locations, and investments
Over 246M companies and non-profits in the Knowledge Graph
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News & Articles
More than just text — entity matching, topic-level sentiment, and more
Over 1. 6B news articles, blog posts, and press releases in the Knowledge Graph
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Retail Products
20+ data fields, including brand, images, reviews, offer, and sales prices
Over 3M pre-crawled retail products in the Knowledge Graph
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Discussions
Unique data type allowing access to insights in forums and reviews
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DATA TYPE NEW
Events
Features complete descriptions and normalized start and end date times.
Over 23k events in the Knowledge Graph
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Extract
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Crawl
Turn any site into a structured database of products, articles, and discussions in minutes.
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Using AI to Automate Web Crawling – Semantics3
Writing crawlers to extract data from websites is a seemingly intractable problem. The issue is that while it’s easy to build a one-off crawler, writing systems that generalize across sites is not easy, since websites usually have distinct unique underlying patterns. What’s more, website structures change with time, so these systems have to be robust to the age of machine learning, is there a smarter, more hands-off way of doing crawling? This is a goal that we’ve been chipping away at for years now, and over time we’ve made decent progress in doing automated generalizable crawling for a specific domain — ecommerce. In this article, I’d like to describe the system that we’ve built and the algorithms behind it; this work is the subject of a recent patent filing by our goal of our automated crawling projectOur goal in this project is to extract the entire catalog of an ecommerce site given just its homepage URL (see image above). This involves three key allenge #1 — Identifying Product URLsTypes of PagesWebsites contain all sorts of pages, so at the outset, we need a system to separate the useful pages from the the case of ecommerce, websites have:Category landing pages (e. g. a landing page for women’s apparel)Brand specific pages (e. a page for Nike products)Deal specific pages (discounts and offers)Seller information pages (about the business selling the product, in the case of marketplaces)Review pages and forums (customer generated content)Product pages (which describe a specific product and allow you to purchase it)… and more. Since our goal is to capture product details, we’ll look to isolating just the product pages. Other pages aren’t of use to use, except to discover product allenge #1 — Identifying product URLsSample product pageSupervised Classification? Our initial approach to tackling this problem was to frame it as a supervised multi-class classification problem, with the HTML and screenshot of each page being sent through a pervised classification approach to identifying product URLs, both visually and using HTML (features)The issue with this approach though is that it requires that we crawl URLs first in order to classify them. And wasted crawling is resource inefficient. To reduce throwaway crawls, we need a system that can make a decision using just the URL of the, URL clusteringThe aim of URL clustering is to divide URLs into groups based on string ustering URLs into groupsThe first step in doing this is to split URLs by path and query string, and count the rank of each component entify number of URL parametersNext, we attempt to represent each component with a set of comprehensive pre-defined mini regular a mini-regex for each parameterFinally, we break down each URL into a tree, specific to its path rank — query string rank combo as a URL tree for each rank-pair (URL #1)This is a binary tree in which each node splits into two children, one which represents the specific text of the URL component, and another which represents the generalized mini regex. At each leaf node, we note down two numbers — the count of number of URLs that decomposed into that leaf node, and a count of the number of generalized regex patterns used in that ’s run another URL through the same tree above and see how this a URL tree for each rank-pair (URL #2)Note how the frequency counters of overlapping tree paths have changed with the second URL coming determine the representative regex of a URL, we pick the path that has the highest leaf frequency, with the fewest regex generalizations. This allows us to cluster the URLs as shown above — in this example, the representative regex is ([^\]+). Tagging the groupWe’re almost there. Now that we’ve divided URLs into groups, how do we decide which group represents product URLs? We use the supervised classification approach shown above of course! This time around though, since we need to label groups rather than each individual URL, all we need to do is to sample a few URLs from each group and voila, our scalability challenge is debar: This regex based approach also allows us to canonicalize URLs, i. e., deduplicate different URL forms that effectively point to the same webpage. This is because one of the regex groups in the URL cluster usually points to the unique web SKU ID of the allenge #2 — Mining Product URLsSpidering overloadNow that we know what to look for, we need to figure out how to find as many product URLs as we obvious way to do this is to visit the homepage, collect all the URLs that it leads to, visit each of them in turn, and repeat. Spidering explosionThis issue with this approach, however, is that the number of URLs gathered explodes within a few levels of spidering. Crawling every single one of these spidering URLs is prohibitive from both a cost and time perspective. Are there smarter ways of doing this? What would a human do? If an intern were tasked with mining product URLs, how would he/she go about it? One strategy would be to look for category listing tegory listing pagesAnd then systematically paginate gination elementsSome strategies will be more optimal than others. For example, in comparison, visiting recommended similar products from product URL pages is less likely to systematically maximize the number of URLs oduct recommendation pages usually offer diminishing returnsHow do we distil this intuition around discovering strategies and evaluating them into algorithm form? Let’s gamify our challengeMulti-armed bandits to the foreWe can set the problem up as a game, with the mechanics defined as follows:Choices: Each pull is the choice of a URL (cluster) to tions: At each turn, an agent selects a URL cluster from which a sampled URL is Awarded for each unique product URL Every action/crawl accrues a fixed negative reward; this penalizes the agent for triggering excessive fruitless Keeps track of which URLs have been discovered, which of them have been visited, and how many reward points have accrued to each here, even a simple Epsilon-Greedy strategy works effectively:Initial Exploration: Agent randomly samples k URLs from each cluster. Exploitation: Steady state, the cluster with the highest payout rate is selected 1-e% of the Exploration: e% of the time, a random cluster is there we have it. Combined with our approach towards identifying product URLs, we now have a way to generate product URLs given just the homepage of the ecommerce step to goChallenge #3 — Content Extraction from Product URLsFinal bossFinally, we come to the challenge of extracting structured attributes from product URLs, the hardest part of web crawling. Challenge #3 — Content extraction from product URLsBaseline winsWe use meta tag mappings and DOM tree rules to achieve quick wins where possible — a baseline set of effective heuristics go a long way in chipping away at the problem. Where these rules don’t deliver sufficient recall though, we turn to element-wise featuresOur approach is to build deep learning classifiers to tag HTML elements to a list of specific known render the webpage through a Headless Chrome session, and retrieve key elements from the webpage, along with the following features:HTML features: e. g., tag type, font-size, font-weightVisual features: e. g., color, width, height, areaText features: e. length of string, data typePositional features: x-y coordinates of the bounding boxThen, elements with similar coordinates and properties are merged together. This group ensures that only the distinctive elements on the page that could represent the kind of information that we are looking for are sent to the next classifierOnce this data has been gathered, the inputs are fed into the classifier, along with other available multi-modal data, such as a relevant screenshot of the element or text Element ClassifierThe output classes conform to a standard group of possible fields such as name, crumb, price, images, specifications, description and so, you ask, do we deal with a need for new fields, namely category-specific fields such as screen size or RAM memory? Most ecommerce websites represent such fields under umbrella sections of the webpage such as specifications or description. Our approach is to capture all such information as either unstructured text or key-value pairs, and subsequently run it through our text attribute extraction pipelines, a subject for a different discussion. A word on featuresNotice that in the list of input features to the model, we didn’t significantly emphasize text characters or image pixel inside the element; even without these key signals, using just metadata features, it is often possible to confidently predict what a particular element refers nsider, for example, a screenshot from a Japanese ecommerce website shown below — even to an observer who doesn’t understand Japanese, it’s not difficult to map segments of the page to commonly understood attributes. This is because ecommerce websites often conform to norms of design ntent extraction demo on Japanese website — note the extracted fields and boxes drawnThis works for Arabic websites too, even though the text is right-to-left ntent extraction demo on Arabic website (right to left oriented) — note the extracted fields and boxes drawnWhat about variations? Here’s where it gets really tricky. Ecommerce products often come with variation options (think color and size) — extracting such information requires interaction with the webpage, and identifying nested patterns and combinations where riation palette #1Variation palette #2Our solution to this involves two steps. First, we identify which elements have variant options, using a binary variation elements using binary classifiersThen, using Puppeteer on a Headless Chrome session, we systematically click through and capture the state and feature bundles of each variation nally, each combination of features is run through our previously discussed element classification system to generate one output product set per ntent extraction classifiers run on every variation combinationThus, we’re able to generate catalogs from ecommerce websites using completely automated practice, since the goal of most crawling projects is to be as close to perfect precision and recall as possible, these unsupervised methods are used in tandem with human-in-the-loop approaches. These algorithms complement our operational efforts to build parsers and spiders at scale, and are layered with inputs in our in-house DSL (Domain Specific Language) to achieve optimal business to Praveen Sekar, our lead data scientist on this project, and to our entire Data Ops team for their valuable insights.
What is a web crawler? | How web spiders work | Cloudflare
What is a web crawler bot?
A web crawler, spider, or search engine bot downloads and indexes content from all over the Internet. The goal of such a bot is to learn what (almost) every webpage on the web is about, so that the information can be retrieved when it’s needed. They’re called “web crawlers” because crawling is the technical term for automatically accessing a website and obtaining data via a software program.
These bots are almost always operated by search engines. By applying a search algorithm to the data collected by web crawlers, search engines can provide relevant links in response to user search queries, generating the list of webpages that show up after a user types a search into Google or Bing (or another search engine).
A web crawler bot is like someone who goes through all the books in a disorganized library and puts together a card catalog so that anyone who visits the library can quickly and easily find the information they need. To help categorize and sort the library’s books by topic, the organizer will read the title, summary, and some of the internal text of each book to figure out what it’s about.
However, unlike a library, the Internet is not composed of physical piles of books, and that makes it hard to tell if all the necessary information has been indexed properly, or if vast quantities of it are being overlooked. To try to find all the relevant information the Internet has to offer, a web crawler bot will start with a certain set of known webpages and then follow hyperlinks from those pages to other pages, follow hyperlinks from those other pages to additional pages, and so on.
It is unknown how much of the publicly available Internet is actually crawled by search engine bots. Some sources estimate that only 40-70% of the Internet is indexed for search – and that’s billions of webpages.
What is search indexing?
Search indexing is like creating a library card catalog for the Internet so that a search engine knows where on the Internet to retrieve information when a person searches for it. It can also be compared to the index in the back of a book, which lists all the places in the book where a certain topic or phrase is mentioned.
Indexing focuses mostly on the text that appears on the page, and on the metadata* about the page that users don’t see. When most search engines index a page, they add all the words on the page to the index – except for words like “a, ” “an, ” and “the” in Google’s case. When users search for those words, the search engine goes through its index of all the pages where those words appear and selects the most relevant ones.
*In the context of search indexing, metadata is data that tells search engines what a webpage is about. Often the meta title and meta description are what will appear on search engine results pages, as opposed to content from the webpage that’s visible to users.
How do web crawlers work?
The Internet is constantly changing and expanding. Because it is not possible to know how many total webpages there are on the Internet, web crawler bots start from a seed, or a list of known URLs. They crawl the webpages at those URLs first. As they crawl those webpages, they will find hyperlinks to other URLs, and they add those to the list of pages to crawl next.
Given the vast number of webpages on the Internet that could be indexed for search, this process could go on almost indefinitely. However, a web crawler will follow certain policies that make it more selective about which pages to crawl, in what order to crawl them, and how often they should crawl them again to check for content updates.
The relative importance of each webpage: Most web crawlers don’t crawl the entire publicly available Internet and aren’t intended to; instead they decide which pages to crawl first based on the number of other pages that link to that page, the amount of visitors that page gets, and other factors that signify the page’s likelihood of containing important information.
The idea is that a webpage that is cited by a lot of other webpages and gets a lot of visitors is likely to contain high-quality, authoritative information, so it’s especially important that a search engine has it indexed – just as a library might make sure to keep plenty of copies of a book that gets checked out by lots of people.
Revisiting webpages: Content on the Web is continually being updated, removed, or moved to new locations. Web crawlers will periodically need to revisit pages to make sure the latest version of the content is indexed.
requirements: Web crawlers also decide which pages to crawl based on the protocol (also known as the robots exclusion protocol). Before crawling a webpage, they will check the file hosted by that page’s web server. A file is a text file that specifies the rules for any bots accessing the hosted website or application. These rules define which pages the bots can crawl, and which links they can follow. As an example, check out the file.
All these factors are weighted differently within the proprietary algorithms that each search engine builds into their spider bots. Web crawlers from different search engines will behave slightly differently, although the end goal is the same: to download and index content from webpages.
Why are web crawlers called ‘spiders’?
The Internet, or at least the part that most users access, is also known as the World Wide Web – in fact that’s where the “www” part of most website URLs comes from. It was only natural to call search engine bots “spiders, ” because they crawl all over the Web, just as real spiders crawl on spiderwebs.
Should web crawler bots always be allowed to access web properties?
That’s up to the web property, and it depends on a number of factors. Web crawlers require server resources in order to index content – they make requests that the server needs to respond to, just like a user visiting a website or other bots accessing a website. Depending on the amount of content on each page or the number of pages on the site, it could be in the website operator’s best interests not to allow search indexing too often, since too much indexing could overtax the server, drive up bandwidth costs, or both.
Also, developers or companies may not want some webpages to be discoverable unless a user already has been given a link to the page (without putting the page behind a paywall or a login). One example of such a case for enterprises is when they create a dedicated landing page for a marketing campaign, but they don’t want anyone not targeted by the campaign to access the page. In this way they can tailor the messaging or precisely measure the page’s performance. In such cases the enterprise can add a “no index” tag to the landing page, and it won’t show up in search engine results. They can also add a “disallow” tag in the page or in the file, and search engine spiders won’t crawl it at all.
Website owners may not want web crawler bots to crawl part or all of their sites for a variety of other reasons as well. For instance, a website that offers users the ability to search within the site may want to block the search results pages, as these are not useful for most users. Other auto-generated pages that are only helpful for one user or a few specific users should also be blocked.
What is the difference between web crawling and web scraping?
Web scraping, data scraping, or content scraping is when a bot downloads the content on a website without permission, often with the intention of using that content for a malicious purpose.
Web scraping is usually much more targeted than web crawling. Web scrapers may be after specific pages or specific websites only, while web crawlers will keep following links and crawling pages continuously.
Also, web scraper bots may disregard the strain they put on web servers, while web crawlers, especially those from major search engines, will obey the file and limit their requests so as not to overtax the web server.
How do web crawlers affect SEO?
SEO stands for search engine optimization, and it is the discipline of readying content for search indexing so that a website shows up higher in search engine results.
If spider bots don’t crawl a website, then it can’t be indexed, and it won’t show up in search results. For this reason, if a website owner wants to get organic traffic from search results, it is very important that they don’t block web crawler bots.
What web crawler bots are active on the Internet?
The bots from the major search engines are called:
Google: Googlebot (actually two crawlers, Googlebot Desktop and Googlebot Mobile, for desktop and mobile searches)
Bing: Bingbot
Yandex (Russian search engine): Yandex Bot
Baidu (Chinese search engine): Baidu Spider
There are also many less common web crawler bots, some of which aren’t associated with any search engine.
Why is it important for bot management to take web crawling into account?
Bad bots can cause a lot of damage, from poor user experiences to server crashes to data theft. However, in blocking bad bots, it’s important to still allow good bots, such as web crawlers, to access web properties. Cloudflare Bot Management allows good bots to keep accessing websites while still mitigating malicious bot traffic. The product maintains an automatically updated allowlist of good bots, like web crawlers, to ensure they aren’t blocked. Smaller organizations can gain a similar level of visibility and control over their bot traffic with Super Bot Fight Mode, available on Cloudflare Pro and Business plans.
Frequently Asked Questions about ai web crawler
Is Web crawler an AI?
The issue is that while it’s easy to build a one-off crawler, writing systems that generalize across sites is not easy, since websites usually have distinct unique underlying patterns. … What’s more, website structures change with time, so these systems have to be robust to change.Nov 6, 2019
What is a web crawler used for?
A web crawler, or spider, is a type of bot that is typically operated by search engines like Google and Bing. Their purpose is to index the content of websites all across the Internet so that those websites can appear in search engine results.
How can AI be used in web scraping?
Web scraping involves writing a software robot that can automatically collect data from various webpages. Simple bots might get the job done, but more sophisticated bots use AI to find the appropriate data on a page and copy it to the appropriate data field to be processed by an analytics application.Jun 29, 2020
