Category: Structured Data

Benchmarking: Diffbot Knowledge Graph Versus Google Knowledge Graph

Posted on | by Merrill Cook

Knowledge graphs play a role in many of our favorite products. They provide information and context that serves up recommendations and additional information just where we need it.

They’re how Alexa and Google search can provide information on entities related to a request. They’re how Netflix builds a profile of the genres, plots, and actors you like.

Many knowledge graphs that reach consumers are primarily built on internal data stores. But a growing number also augment their breadth and timeliness by sourcing information from the public internet. 

Three North American entities have claims to crawling the whole web in order to structure information into knowledge graphs. These organizations are Google, Bing, and Diffbot. 

All three provide some level of knowledge graph access to end consumers. Though –of these three – Diffbot is the only commercial knowledge graph provider that allows data teams to integrate and download the entirety of their data. This makes Diffbot’s Knowledge Graph a great starting point for machine learning projects, deeper market intelligence exercises, or web-wide news monitoring projects. 

With that said, many product leaders and data teams are not looking for the widest coverage or the largest sets of ingestible data, per se. Rather, these teams are discerning which knowledge graph has the coverage they need.

For example, do you need rapidly updated information about large entities that are easy to track? Do you need suitable coverage of extremely long tail organizations? And what types of data do you need? Basic organizational data? Articles about specific entities? Product data? Discussions or events? 

In this guide we’ll work through a comparison of data coverage between Diffbot and Google knowledge graphs, both of which are available through knowledge graph search APIs. 

Note: Before we jump in, one thing worth noting is that the Google knowledge graph API is not recommended for production uses, rather it’s more of a demo of their internal technology and data. 

Check out our comparison of data returned from Google and Diffbot KG search APIs here. 

Which Knowledge Graph is Larger? Google VS. Diffbot

Historically, knowledge graphs used in academic settings have been too small for viable commercial use. But once knowledge graphs grew substantially past this size, the absolute size of a knowledge graph often wasn’t a proxy for the usefulness of a knowledge graph. 

To show you what we mean, both the Diffbot and Google knowledge graphs hold roughly the same number of entities: ~5B (Google, 2020), and ~5.9B (Diffbot, 2022). But knowledge graphs are built around “things” (items in the world), and ~5B doesn’t begin to account for all of those.

So what “things” are included? Industry insights? Global news coverage? Can this data tell you whether you’d like a movie or should buy a product?

All of these are viable uses for knowledge graphs, and the answers are dependent on the following non-scale related features:

  • What type (topics and fact types) of data is included
  • How up-to-date data is
  • The number of valuable fields per entity
  • How accurate data is
  • How easy it is to extract the data you need
  • How easy it is to fit this data into your workflows
  • And business process-related aspects like pricing, uptime, data provenance, and so forth

To dive into the differences between Diffbot and Google knowledge graphs on the above points, we’ll need to provide some background information about how these knowledge graphs are constructed. Following this, we’ll jump into an up-to-date benchmarking of the coverage of specific entities within each knowledge graph. 

How Google Crawls the Web

Historically, Google has crawled the web to surface what it deems to be the most useful pieces of content around search keywords. Sites deemed more useful or “important” get crawled more frequently. And the top sites tend to present highly in many search terms related to their offerings. 

While Google applies robust natural language processing to pages in order to provide their search service, many surfaced “facts” are not integrated with their knowledge graph as seen in knowledge panels or their KG search API. Take for example the knowledge panel result for Diffbot.

The entry to the right typifies an organization “knowledge panel” within Google search

The area to the right in the screenshot above is the Google knowledge graph-derived knowledge panel. Facts included in these panels are typical of linked data, wherein the organization entity of Diffbot is attached to other knowledge graph entities including locations, people, and other organizations. 

Search results (content) are used to expand knowledge panel offerings

Furthermore, the result is enhanced by additional content. If we click through to competitors, Google can serve up and highlight a portion of content that claims to be about competitors. But even though Webhose, Thinknum, Scrapinghub and others listed all have their own knowledge graph entries, this data isn’t linked. The NLP by which Google is parsing content to categorize and serve up this article on competitors is not integrated into the Google knowledge graph. Clicking through to the headline about competitors does not lead to knowledge panel-related data. But rather takes you to an article that is the top ranking result of the search “Diffbot competitors.” 

Recommendations (“People also search”) are facilitated by knowledge graph linkages

Let’s take another example, wherein we look at a publicly traded company. Searching “Microsoft Revenue” returns the Microsoft knowledge panel as well as the most recent publicly listed revenue number. A great number of fields displayed here are from the Google knowledge graph API. But clicking through to the disclaimer below financial data takes us to Google Finance, a separate service from Google’s knowledge graph. Linked data is present in the “people also search for” section. And each of these organizations does have their own knowledge panel result. But at the end of the day, clicking through any of these simply routes users to a suggested search. 

Related products are held in the knowledge graph, but data is provided by Google Shopping

Dropping to the bottom of the knowledge panel for Microsoft, yet again we see the appearance of linked data. In this case products that are related to Microsoft. But clicking through to each simply returns search results (albeit aggregated values related to price, availability, and reviews). We can verify that this product data is not in fact part of Google knowledge graph by searching for a “Microsoft Xbox One Wireless Controller” using the knowledge graph search API.

There is no “XBOX One Wireless Controller” (from the prior image) in the Google Knowledge Graph

Above is the result for a Google knowledge graph API search for a particular model of XBOX controller (an XBOX One controller) that is served up within the knowledge panel results. What is returned is a general category of “XBOX controller” sourced from Wikipedia, with entity types of “thing” and “productModel.” The closest entity to what was served within the knowledge panel as actually a somewhat generic category of products. The “XBOX One Controller” from the knowledge panel actually isn’t from the Google knowledge graph. 

All this hints at the fact that Google pads out the appearance of their knowledge graph in it’s most prevalent form (knowledge panel results) while not actually ingesting and linking many of these additional data structures. 

Sure, Google crawls the entire web to return search results. But what does a large portion of this crawling have to do with their knowledge graph? 

This distinction between Google’s search-related crawls and their knowledge graph data likely begins with Freebase. Freebase was rolled into an early version of Google’s knowledge graph after the company was acquired. Freebase largely crowdsourced knowledge, allowing users to manually tag, relate, update, and create their own knowledge bases. While this enabled some scale (2.4B facts as of 2014), little automation was factored into fact accumulation. While Freebase compiled one of the largest commercially-aimed knowledge bases, they did so manually.

Freebase’s data pipeline didn’t really have anything to do with Google’s automated knowledge accumulation that powers their search engine. 

You don’t have to take our word for it, see Google’s own description: 

“Facts in the Knowledge Graph come from a variety of sources that compile factual information. In addition to public sources, we license data to provide information such as sports scores, stock prices, and weather forecasts. We also receive factual information directly from content owners in various ways, including from those who suggest changes to knowledge panels they’ve claimed.”

Or put another way: “it’s a bit amusing that i’ve been invited to speak at a conference on automated knowledge base construction because both in the world I work and my background I don’t know anything about the automated side of this. The world I work in is far from automated. We have automated processes and things like that. But in terms of knowledge base construction, the world I work in is really one of a watchmaker. A precision scientist.” – Jamie Taylor, Decade-long leader at Freebase (now Google Knowledge Graph)

While Google’s knowledge graph is certainly a massive knowledge base, the inclusion of core constituents that are manually sourced including “claimed” (human sourced) knowledge panels and Freebase point to a knowledge graph primarily based on human inputs. 

How Diffbot Crawls the Web

For comparison, Diffbot’s web crawling was always set up as a way to extract, structure, validate, and link data across the web in an automated way (see “The Economics of Building Knowledge Bases”) . Our original product line of AI-enabled automatic extraction APIs were meant to be able to extract valuable facts and information from a variety of page types without even seeing their format in advance. Over time, crawling infrastructure as well as the ability to link and apply automated inference and understanding on top of these page crawls enabled our Knowledge Graph. 

How does this work? 

Early research shows us that a large majority of the internet was composed of 9 separate “types” or pages. Think of these pages like articles, discussions, profiles, product pages, event pages, lists, and so forth. 

Across languages and sites, the “types” of information that humans tend to find valuable on these page types persists. For example, whether you’re on Amazon or Walmart’s websites, some of the valuable data types on a product page include reviews, price, availability, a picture, and product specifications. These commonalities allow Diffbot to automatically extract information humans care about in a standardized format even if the actual layout of pages is different. All of this with no human input. 

Once facts, underlying text, images, and metadata are extracted, powerful natural language processing tech can transform these inputs into entities and relationships (constructing a graph). 

Because they provide information and context, graph databases are one of the most well suited data sources for machine learning. We leverage ML over our graph to incorporate new fact types such as similarity scores, enhanced organizational descriptors, and estimated revenue of private organizations. 

The range of automated inputs allow Diffbot’s Knowledge Graph to cover a huge range of commercially interesting data types. As of the time of this article’s writing, our data coverage included the following, all linked and with an average of 31 facts per entity:

  • 243MM organization entities
  • 773MM person entities
  • 1,879MM image entities
  • 1,621 article entities
  • 880MM post entities
  • 128MM discussion entities
  • 141MM product entities
  • 89MM video entities
  • 20MM job entities
  • 42MM event entities
  • .56M FAQ entities
  • 73MM miscellaneous entities
  • 10MM place entities
  • 49MM creativeWork entities
  • .17MM intangible entities

Total: 5,953MM entities

We’ll jump into additional comparisons of data within Diffbot and Google knowledge graphs in the next section. But hopefully you can begin to see the fundamental differences between a Knowledge Graph built for automated fact accumulation from the start (Diffbot) and one built with manual processes (Google). 

Benchmarking Google And Diffbot’s Knowledge Graphs

While there are substantial coverage differences between entity types in Google and Diffbot knowledge graphs, organization entities are well represented in both. Organization entities are also of broad commercial interest, with uses ranging from market intelligence, to supply chain risk analysis, to sales prospecting. 

For our study of Google and Diffbot knowledge graph organizational coverage, we looked at a representative number of randomized head entity and long tail organizations. Head entity organizations in this case are publicly-traded companies randomly chosen from the Russel 2000 index. Long tail entities include a random sampling of Series A and earlier startups with less than 50 employees. 

For both head entity and long tail organizations, we sought out external records of truth on a range of fields including:

  • CEO
  • Headquarter location
  • Number of employees
  • Revenue
  • And homepage URL

Example “ground truth” publications included SEC financial filings, Crunchbase, and Linkedin.

The results of our analysis show strong coverage of head entities across both knowledge graph providers. In this instance, lack of coverage centered around missing “ground truth” revenue fields in the case of several publicly traded companies who are yet to generate revenue. 

Among startups, a substantial spread emerged. For many uses, SMB/MMKT data is particularly hard to come by at scale, and Diffbot’s coverage includes 100’s of millions of “longtail” entities. 

An organization entity within Diffbot’s visual interface for Knowledge Graph search

While we chose fields present for organizations in both knowledge graphs, Diffbot’s Knowledge Graph also provides a wider range of additional fields. Above is a screenshot from our Knowledge Graph search visual interface. But additional fields attached to most organization entities within the Knowledge Graph include:

  • Noteworthy Employees
  • News Coverage
  • Industries
  • Locations
  • Subsidiaries
  • Funding Rounds
  • Descriptions
  • Revenue (or estimated revenue)
  • Similar organizations 
  • Technologies used
  • Among many other fields. 
The same entity as it’s presented in Google’s search interface

In the Google knowledge graph derived knowledge panel, the only three fields not presented by an external API (Google Finance) are a brief description, the URL of the organization, and the logo. 

While we’ve presented but a handful of samples within this article, Diffbot routinely benchmarks wide ranges of our knowledge graph against competitors and can patently say we have the world’s most accurate and up-to-date large-scale knowledge graph.

Interested in exploring Diffbot Knowledge Graph data for yourself? Grab a free trial or reach out to our sales team for a custom demo.



Data Trends: Comparing Data Fabrics, Data Meshes, And Knowledge Graphs

Posted on | by Merrill Cook

Data meshes, fabrics, and knowledge graphs are all positioned as frameworks through which similar benefits are realized. 

All three promote interoperability and ease of integrating new data sources. To varying degrees all three support real-time and event-driven data ingestion and processing. All three seek to avoid flat data output, data that needs additional processing time once it has been extracted, and orphaned data that becomes progressively stale. Additionally, with the focus on myriad (and a growing number of) data sources, robust data governance and semantic enrichment is at the forefront of each of these systems. 

With that said, there are differences between data mesh, fabric, and knowledge graphs. 

What Is A Data Fabric?

Data fabric is an architecture-centered design concept governing data access across many decentralized data sources. Initial ideas behind the development of data fabric methodologies include costly, slow, and low value data integration cycles common to centralized data lakes or warehouses. The aspirations of data fabric systems are to promote connectivity of disparate data sources as well as reusability by avoiding issues such as orphaned data or large volumes of extraneous data that tend to compile in centralized data stores. 

A focus on value-added data integration is central to the notion of data fabrics. Systems for semantic enrichment, linked data, and the harmonization of a variety of unstructured, semi-structured, and structured data are key for successful data fabric delivery. The creation of these systems is not decentralized. As such in a data fabric, data access is centralized and held under a single point of control.

Where available, data fabric makes data available via objective-centered APIs. For example, in the event a user needs to build a dashboard comparing hiring trends of competitors with news monitoring around noteworthy market events, a data fabric approach would involve first ingesting these disparate data sources, adding context or additional fields to data, then exposing the data as an API for the dashboard. 

What Is A Data Mesh?

First and foremost, data mesh is an organization-centered approach to data management. A data management system built with data mesh-centric principles enables users to access and query data from a variety of sources without first ingesting this data into a centralized warehouse. While architecture design is part of a data mesh, it is not as central to the characterization of a data mesh as to a data fabric. 

From an organizational perspective, data mesh views each edge data source as a product owned by a business unit in charge of that domain. In relation to these decentralized data stores, data mesh serves as a connectivity layer that is built such that both technical and non-technical users can utilize data sets where they reside. 

Ingestion of data closer to the source – without the need for transfer and ingestion into a central repository – can lower processing costs, decrease time-until analysis, and avoid privacy issues regarding data transferred between particular geographies. 

What Is A Knowledge Graph? 

Contrary to data meshes and fabrics, a knowledge graph is not a connectivity-layer-centric solution or a data management imperative. 

Knowledge graphs are graph databases that are built to preserve information and context. In particular, knowledge graphs are built around nodes (entities) and edges (relationships). Though data can be outputted in a format similar to a relational database, knowledge graphs provide better performance traversing through linked data and are much more adept at adding new fact types and data source formats “on the fly.” 

This makes knowledge graphs a natural choice for high velocity and variable type data like those used in news or market monitoring.  Data is linked and often augmented with additional semantic features upon ingestion in knowledge graphs, aligning with the objectives of data fabrics. For example, within Diffbot’s Knowledge Graph we have organization entities for which we can infer detailed industry fields, machine learning-computed estimated revenue, as well as similarity scores between organizations. 

Use of knowledge organization systems (KOS) aligns with data fabric and mesh goals to add additional semantics to variable incoming data streams and promote linked data. KOS’s commonly utilized in Knowledge Graph construction include: 

  • Glossaries/synonym rings: properly merge facts attached to entities mentioned in multiple ways
  • Unique identifiers: disambiguate entities with the same name (Apple Inc vs. Apple the fruit) 
  • Taxonomies: classify new entities in relation to old entities allowing for additional inferences (California is a state in the United States, therefore San Francisco is in the United States) 
  • Associative clustering: track loose relationships and similarities between entities (Pho is often associated with Vietnamese restaurants; machine learning engineers often work at AI startups)
  • Ontologies: rules, properties, constraints to entities and relationships (only organizations have funding rounds)

Also similar to data fabrics, knowledge graphs are often constructed with a single centralized data access via an API or integrations.

As the provider of the world’s largest commercially-available Knowledge Graph, Diffbot has seen many successful use cases for Knowledge Graph data. These uses include:

  • Market monitoring: tracking of firmographic changes and key events
  • Product intelligence: building knowledge graphs of related products 
  • News monitoring: tracking key events and relationships in the news
  • Machine learning: easy labeled data with context leads to quick workflows and explainability  
  • Sales development: ability to filter through detailed firmographics and person records
  • Hiring and investing: track attrition, skill sets, and meaningful organizational events
  • Data enrichment: easily digestible structured and linked data with expanding field types
  • Product Recommendations: serve up recommendations based on associated behaviors and products
  • Discussion tracking: velocity, sentiment, and influencer tracking
  • Fake news detection: the ability to corroborate facts across millions of articles and train models to predict accuracy of statements
  • Fraud detection: the ability to visualize and track complex relationships between regulatory bodies, private organizations, and key individuals
  • Supply chain / risk: the ability to visualize and track partnerships, key events, suppliers, vendors, locations, and hiring trends

Of course, many of the use cases above can also be supported with data fabrics and meshes. But where meshes and fabrics describe an entire ecosystem of data use and structure across an organization, knowledge graphs excel to a noteworthy degree in support of augmentation of other data stores as well as specific tasks. 

Is It Really About All Three? 

There are pros and cons to using any three of the knowledge management frameworks listed above. And it’s often not a choice of either/or. Data fabrics benefit from a single point of connectivity that can serve up standardized and semantically-enriched data from disparate internal and external sources. A data mesh may be suitable for underlying portions of an organization where agility is more heavily prized. A data source of record can then be supplied for integration and release from a central point (data fabric) for other teams. 

Additionally, data held in knowledge graphs may make sense for certain use cases within an organization utilizing a data fabric and/or mesh. A focus on interoperability and easy integration makes knowledge graph data great for augmentation and enrichment of data sets in other formats. A focus on providing context for information supports explainability, making knowledge graph data a preferred choice for machine learning and data science-centered initiatives within an organization.

Care to learn more about the world’s largest commercially-available Knowledge Graph? Reach out to our sales team today. 

17 Uses of Natural Language Processing (NLP) In Business Settings

Posted on | by Merrill Cook

The Library of Alexandria was the pinnacle of the ancient world’s recorded knowledge. It’s estimated that it contained the scroll equivalent of 100,000 books. This was the culmination of thousands of years of knowledge that made it into the records of the time. Today, the Library of Congress holds much the same distinction, with over 170M items in its collection.

While impressive, those 170M items digitized could fit onto a shelf in your basement. Roughly 10 12 terabyte hard drives could contain the entirety.

For comparison, the average data center of today (there are 7.2M of them at last count) takes up an average of 100,000 square feet. Nearly every foot filled with storage.

With this much data, there’s no army of librarians in the whole world who could organize them…

Natural language processing refers to technologies and techniques that take unorganized data and provide meaning and structure at scale. Imagine taking a stack of documents on your desk, making them searchable, sortable, prioritizing them, or generating summaries for each. These are the sort of tasks natural language processing supports in business and research settings.

At Diffbot, we see a wide range of use cases using our benchmark-topping Natural Language API. We’ll work through some of these use cases as well as others supported by other technologies below.

Sentiment Analysis

These days, it seems as if nearly everyone online has an opinion (and is willing to share it widely). The velocity of social media, support ticket, and review data is astounding, and many teams have sought solutions to automate the understanding of these exchanges.

Sentiment analysis is one of the most widespread uses of natural language processing. This process involves determining how “positive” or “negative” a given text is. Common uses for sentiment analysis are wide ranging and include:

  • Buyer risk
  • Supplier risk
  • Market intelligence
  • Product intelligence (reviews)
  • Social media monitoring
  • Underwriting
  • Support ticket routing
  • Investment intelligence

While no natural language processing task is foolproof, studies show that analysts tend to agree with top-tier sentiment analysis services close to 85% of the time.

One categorical difference between sentiment analysis providers is that some provide a sentiment score for entire documents, while some providers can give you the sentiment of individual entities within the text. A second important factor about entity-level sentiment involves knowing how central an entity is to understanding the text. This measure is commonly called the “salience” of an entity.

Text Classification

Text classification can refer to a process internal to natural language processing tools in which text is grouped into related words and prepared for further analysis. Additionally, text (topic) classification can refer to the user output of greater business use.

The uses of text (topic) classification include ticket or call routing, news mention tracking, and providing contextuality to other natural language processing outputs. Text classification can function as an “operator” of sorts, routing requests to the person best suited to solve the issue.

Studies have shown that the average support worker can only handle around 20 support tickets a day. Text classification can dramatically increase the time before tickets reach the right support team member as well as provide this team member with context to solve an issue quickly. Salesforce has noted that 69% of high-performing support teams are considering the use of AI for ticket routing.

Additionally, you can think of text classification as one “building block” for understanding what is going on in bulk unstructured text. Text classification processes may also trigger additional natural language processing through identifying languages or topics that should be analyzed in a particular way.

Chatbots & Virtual Assistants

Loved by some, despised by others, chatbots form a viable way to direct informational conversations towards self service or human team members.

While historical chatbots have relied on makers plotting out ‘decision trees’ (e.g. a flow chart pattern where a specific input yields a specific choice), natural language processing allows chatbot users several distinct benefits:

  • The ability to input a nuanced request
  • The ability to type a request in informal writing
  • More intelligence judgment on when to hand off a call to an agent

As the quality of chatbot interactions has improved with advances in natural language processing, consumers have grown accustomed to dealing with them. The number of consumers willing to deal with chatbots doubled between 2018 and 2019. And more recently it has been reported that close to 70% of consumers prefer to deal with chatbots for answers to simple inquiries.

Text Extraction (Mining)

Text extraction is a crucial functionality in many natural language processing applications. This functionality involves pulling out key pieces of information from unstructured text. Key pieces of information could be entities (e.g. companies, people, email addresses, products), relationships, specifications, references to laws or any other mention of interest. A second function of text extraction can be to clean and standardize data. The same entity can be referenced in many different ways within a text, as pronouns, in shorthand, as grammatically possessive, and so forth.

Text extraction is often a “building block” for many other more advanced natural language processing tasks.

Text extraction plays a critical role in Diffbot’s AI-enabled web scraping products, allowing us to determine which pieces of information are most important on a wide variety of pages without human input as well as pull relevant facts into the world’s largest Knowledge Graph.

Machine Translation

Few organizations of size don’t interface with global suppliers, customers, regulators, or the public at large. “Human in the loop” global news tracking is often costly and reliant on recruiting individuals who can read all of the languages that could provide actionable intelligence for your organization.

Machine translation allows these processes to occur at scale, and refers to the natural language processing task of converting natural text in one language to another. This relies on understanding the context, being able to determine entities and relationships, as well as understanding the overall sentiment of a document.

While some natural language processing products center their offerings around machine translation, others simply standardize their output to a single language. Diffbot’s Natural Language API can take input in English, Chinese, French, German, Spanish, Russian, Japanese, Dutch, Polish, Norwegian, Danish or Swedish and standardize output into English.

Text Summarization

Text summarization is one of a handful of “generative” natural language processing tasks. Reliant on text extraction, classification, and sentiment analysis, text summarization takes a set of input text and summarizes it. Perhaps the most commonly utilized example of text summarization occurs when search results highlight a particular sentence within a document to answer a query.

Two main approaches are used for text summarizing natural language processing. The extraction approach finds a sentence(s) within a text that it believes coherently summarizes the main points of the document. The abstraction approach actually rewrites the input text, removing points it believes are less important and rephrasing to reduce length.

The primary benefit of text summarization is the preserving of time for end users. In cases like question answering in support or search, consumers utilize text summarization daily. Technical, medical, and legal settings also utilize text summarization to give a quick high-level view of the main points of a document.

Market Intelligence

Check out a media monitoring dashboard that combines Diffbot’s web scraping, Knowledge Graph, and natural language processing products above!

The range of data sources on consumers, suppliers, distributors, and competitors makes market intelligence incredibly ripe for disruption via natural language processing. Web data is a primary source for a wide range of inputs on market conditions, and the ability to provide meaning while absolving individuals from the need to read all underlying documents is a game changer.

Applied with web crawling, natural language processing can provide information on key market happenings such as mergers and acquisitions, key hires, funding rounds, new office openings, and changes in headcount. Other common market intelligence uses include sentiment analysis of reviews, summarization of financial, legal, or regulatory documents, among other uses.

Intent Classification

Intent classification is one of the most revenue-centered and actionable applications of natural language processing. In intent classification the input is direct communications from a prospect or customer. Using machine learning, intent classification tools can rate how “ready to buy” a given individual is during an interaction. This can prompt sales and marketing outreach, special offers, cross-selling, up-selling, and help with lead scoring.

Additionally, intent classification can help to route inquiries aimed at support or general queries like those related to billing. The ability to infer intentions and needs without even needing to prompt discussion members to answer specific questions enables for a faster and more frictionless experience for service providers and customers.

Urgency Detection

Urgency detection is related to intent classification, but with less focus on where a text indicates a writer is within a buying process. Urgency detection has been successfully used in cases such as law enforcement, humanitarian crises, and health care hotlines to “flag up” text that indicates a certain urgency threshold.

Because urgency detection is just one method — among others — in which communications can be routed or filtered, low or no supervision machine learning can often be used to prepare these functions. In instances in which an organization does not have the resources to field all requests, urgency detection can help them to prioritize the most urgent.

Speech Recognition

In today’s world of smart homes and mobile connectivity, speech recognition opens up the door to natural language processing away from written text. By focusing on high fidelity speech-to-text functionality, the range of documents that can be fed to natural language processing programs expands dramatically.

In 2020, an estimated 30% of all searches held a voice component. Applying natural language processing detailed in the other points in this guide is a huge opportunity for organizations providing speech-related capabilities.

Search Autocorrect and Autocomplete

Search autocorrect and complete may be the area most individuals deal with natural language processing most readily. In recent years, search on many ecommerce and knowledge base sites has been entirely rethought. The ability to quickly identify intent and pair it with an appropriate response can lead to better user experience, higher conversion rates, and more end data about what users want.

While 96% of major ecommerce sites employ autocorrect and/or autocomplete, major benchmarks find that close to 30% of these sites have severe usability issues. For some of the largest traffic volume sites on the web, this is a major opportunity to employ quality predictive search using cutting-edge natural language processing.

Social Media Monitoring

Of all media sources online, social can be the most overwhelming in velocity, range of tone and conversation type. Global organizations may need to field or monitor requests in many languages, on many platforms. Additionally, social media can provide useful inputs into external issues that may affect your organization, from geopolitical strife, to changing consumer opinion, to competitor intelligence.

On the customer service and sales fronts, 79% of consumers expect brands to respond within a day on social media requests. Recent studies have shown that across industries only 29% of brands regularly hit this mark. Additionally, the cost of finding new customers is 7x that of keeping existing customers, leading to increased need for intent monitoring and natural language processing of social media requests.

Web Data Extraction

Rule-based web data extraction simply doesn’t scale past a certain point. Unless you know the structure of a web page in advance (many of which are changing constantly), rules specified for which information is relevant to extract will break. This is where natural language processing comes into play.

Organizations like Diffbot apply natural language processing for web data extraction. By training natural language processing models around what information is likely useful by page type (e.g. product page, profile page, article page, discussion page, etc.), we can extract web data without pre-specified rules. This leads to resiliency in web crawling as well as enables us to expand the number of pages we can extract data from. This ability to crawl across many page types and continuously extract facts is what powers our Knowledge Graph. Interested in web data extraction? Be sure to check out our automatic extraction APIs or pre-extracted firmographic, demographic, and article data within our Knowledge Graph.

Machine Learning

See how ProQuo AI utilizes our web sourced Knowledge Graph to speed up predictive analytics

While machine learning is often an input to natural language processing tools, the output of natural language processing tools can also jumpstart machine learning projects. Using automatically structured data from the web can help you skip time-consuming and expensive annotation tasks.

We routinely see our Natural Language API as well as Knowledge Graph data — both enabled with natural language processing technology — utilized to jump start machine learning exercises. There are few training data sets as large as public web data. And the range of public web data types and topics makes it a great starting point for many, many machine learning journeys.

Threat Detection

See how FactMata uses Diffbot Knowledge Graph data to detect fake news and threats online

For platforms or other text data sources with high velocity, natural language processing has proven to be a good first line of defense for flagging hate speech, threatening speech, or false claims. The ability to monitor social networks and other locations at scale allows for the identification of networks of “bad actors” and a systemic protection from malicious actors online.

We’ve partnered with multiple organizations to help combat fake news with our natural language processing API, site crawlers, and Knowledge Graph data. Whether as a source for live structured web data or as training data for future threat detection tools, the web is the largest source of written harmful or threatening communications. This makes it the best location for training effective natural language processing tools used by non-profits, governmental bodies, media sites looking to police their own content, and other uses.

Fraud Detection

Natural language processing plays multiple roles in fraud prevention efforts. The ability to structure product pages is utilized by large ecommerce sites to seek out duplicate and fraudulent product offerings. Secondly, structured data on organizations and key members of these organizations can help to detect patterns in illicit activity.

Knowledge graphs — one possible output of natural language processing — are particularly well suited for fraud detection because of their ability to link distinct data types. Just as human research-enabled fraud investigations “piece together” information from varying sources and on various entities, Knowledge Graphs allow for machine accumulation of similar information.

Native Advertising

For advertising embedded in other content, tracking what context provides the best setting for ad placement allows for systems to generate better and better ad placement. Using web scraping paired with natural language processing, information like the sentiment of articles, mentions of key entities as well as which entities are most central to the text can lead to better ad placement.

Many brands suffer from underperforming advertising spending as well as brand safety (placement in suitable locations), problems that natural language processing helps to aid at scale.

Dear Diffy, Find Me A Coworking Space

Posted on | by Merrill Cook

Disclaimer: this article is about a very mundane consumer search. With this said, how knowledge work and fact accumulation are often performed have wide-reaching implications for knowledge work flows.

The other day I was searching for coworking spaces.

As in many domains of knowledge, data coverage online was largely human curated. Lists with some undisclosed methodology provided the writer’s favorite coworking spots by city.

Sure, search engines will return a list plotted to a map in any major search engine. But I’m sure we’ve all run into the following.

  1. Load map…
  2. Pan slightly to surface more results…
  3. Zoom slightly to surface more results…
  4. Pan the opposite direction to try and find a result that had caught our eye…
  5. Try to recall the name that caught our eye in a new search…

Five steps to seek further data points on a single search result. Devoid of context, data provenance, and the ability to analyze at scale.

Sure, consumer search works in many, many cases. So do phone books.

If you’re a power user, a data hoarder, or a productivity buff, you can likely see the appeal of a search that actually returns comprehensive data. If you’re building an intelligent application or performing market intelligence, using search that won’t let you explore the underlying data is just a waste of time.

So after this predictable foray in which I ignored the advice of several articles, scrolled around a map, and got sidetracked once or twice, I decided to resort to a different sort of search: Diffbot’s Knowledge Graph.

Prerequisites

  • The title of our article may not make much sense if you haven’t been acquainted with Diffy, Diffbot’s web-reading bot
  • You see the promise of external web data for many applications… if it were structured (or at least felt disappointment at consumer search engines keeping you from public web data)

Opening the Knowledge Graph, it took all of 20 seconds to return data on over 4,000 coworking spaces. And sure, unless you’re selling a service to coworking space, you may wonder why anyone would need all this data as a personal consumer…

4000+ coworking space entities in ~20s

Maybe it’s simple curiosity. Maybe it’s the principle of it all; the fact that all of this information is publicly available online, but not in a structured format. Maybe this is just an analogy for non-consumer searches that also can’t be performed on major search engines. Any way you take it, search of the present is flawed for many uses, and it’s still our primary collective data source.

So what does search in the Knowledge Graph look like?

Well it starts with entities.

Knowledge graphs are built around entities (think people, places, or things) and relationships between entities. The types of relationships that can occur between entities, and the types of facts attached to entities are prescribed by a schema. One of the major “selling points” for knowledge graphs is that they have flexible schemas. That is — more so than other types of databases — they can adapt to what types of facts matter out in the world.

The Importance of Structured Web Data

At their core knowledge graphs (the category of graphs) can be built from any underlying data set. In the case of Diffbot’s Knowledge Graph, it’s the world’s largest structured feed of web data. Diffbot is one of only a handful of organizations to crawl the web. And using machine vision and natural language processing we’re able to pull out mentions of entities as well as infer facts and relationships.

Why is this important?

The web is largely made up of unstructured or semi-structured data. This means you can’t easily filter, sort, or manipulate this data at scale. While the internet is our largest collective source of knowledge, it’s not organized for modern knowledge work.

Diffbot’s products center around organizing the world’s information, whether through our AI-enabled web scrapers, our Knowledge Graph, or our Natural Language API. The ability to source the information from the web in a structured way provides the bedrock for machine learning initiatives, market intelligence, news monitoring, as well as the monitoring of large ecommerce datasets.

The State of Coworking Spaces As Told By AI

So what can you learn from a coworking space dataset that’s much more explorable than consumer search?

It turns out a lot.

While each individual data point is all available online, it’s not aggregated anywhere else in quite as explorable of a format.

In our case we can start with a simple facet query. Faceted search provides a summary view of the value of one fact type attached to a set of entities. So with this sort of query we can quickly discover what locations have the most coworking spaces.

By simply adding facet:locations.city.name we can turn over 4,000 unique results into an observation. While data found about these coworking spaces across the web would be in many different formats (and in many languages), knowledge graphs help to consolidate similar entities around standard fields.

An additional strength of knowledge graphs is that data points can be consolidated from many different sources with data provenance and then built off of. Using natural language processing and machine learning, fields can be computed or inferred from many underlying data sources. Our original query looked at organization entities with “coworking spaces” as part of their description. But an AI-generated field of “descriptors” allows for additional granularity. Let’s look at a facet view of the most common services offered by coworking spaces.

Depending on your experience with a range of coworking spaces, descriptors such as “expat,” “civil & social organization,” or “self improvement” may be novel. By amalgamating tens of thousands of online mentions, articles, and entries into this subset of org entities, the Knowledge Graph dramatically cuts down on time of fact accumulation.

One final area in which consumer search is severely lacking (or just in practice unpractical) is that of market research. Industry-specific events such as funding rounds, openings of new offices, key executive hires or leavings, or clues as to private organization revenue can be hard to pinpoint across the web. Softer signals like sentiment around topics or velocity of news coverage can also be informative.

Diffbot’s article index is roughly 50x the size of Google News. Unlike traditional content channels, you aren’t presented with content that’s gamed the system or paid to get your attention. Additionally, where consumer search engines are siloed by language or location, Diffbot’s article index is pan-lingual. With articles augmented by additional filterable fields underlying articles can become unique observations on sentiment, key happenings, and more. All underlying article data is returned as well, supporting the ability to mine in once you’ve found an interesting angle.

For a deeper dive into creating custom news feeds around organizations and events be sure to check out our Knowledge Graph news monitoring test drive.

Takeaways

Maybe you don’t buy the segue from what really is a consumer search (“coworking spaces near me”) and the copious coworking data available in the Knowledge Graph. But the fact of the matter is that a great deal of knowledge work still relies on human fact accumulation. Without automated ways to structure unstructured data, there’s a definite floor to the cost per fact.

Knowledge graphs provide a bedrock for knowledge workflows reengineered from the ground up. In particular:

  • Knowledge graphs mirror what we care about “in the world” (entities and relationships)
  • Knowledge graphs provide flexible schemas allowing for fact types attached to entities to change over time (as the world changes)
  • Automated knowledge graphs provide one of the only feasible ways to structure market intel and news monitoring data that can be spread across the web
  • Knowledge graphs that don’t expose their underlying data aren’t suitable for use in intelligent applications or machine learning use cases
  • Knowledge graphs that provide additionally computed fields (sentiment, tags, inferences on revenue or events) provide additional value for market intelligence and news monitoring

The Top 50 Most Underrated Startups as Told by AI

Posted on | by Merrill Cook

While Diffbot’s Knowledge Graph has historically offered revenue values for publicly-held companies, we recently computed an estimated revenue value for 99.7% of the 250M+ organizations in the KG.

What does this mean?

Most organizations are privately-held, and thus have no public revenue reporting requirement. Diffbot has utilized our unrivaled long-tail organization coverage to create a machine learning-enabled estimated revenue field. This field looks at the myriad fact types we’ve extracted and structured from the public web and infers a revenue from a range of signals.

Estimated revenue is just that… a machine learning-enabled estimate. But with a training set the size of our Knowledge Graph, we’ve found that a great majority of our revenue values are actually quite accurate.

How can I use estimated revenue?

Revenue — even if estimated — is a huge marker for determining size and valuation. In it’s absence it’s hard to effectively segment organizations. We see this field used in market intelligence, finance, and investing use cases. And it’s as simple as filtering organizations using the revenue.value field.

Where Does Diffbot Get It’s Data?

Diffbot is one of only a handful of organizations to crawl the entire web. We apply NLP and machine vision to crawled web pages to find entities and facts about them. These entities are consolidated in the world’s largest Knowledge Graph along with data provenance, linkages between entities, and additional computed fields (like sentiment, or estimated revenue). In this ranking we looked at organization entities. But organization entities are just the “tip of the iceberg” for Diffbot data, which comprises articles, products, people, events, and many other entity types.

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Generating B2B Sales Leads With Diffbot’s Knowledge Graph

Posted on | by Merrill Cook

Generation of leads is the single largest challenge for up to 85% of B2B marketers.

Simultaneously, marketing and sales dashboards are filled with ever more data. There are more ways to get in front of a potential lead than ever before. And nearly every org of interest has a digital footprint.

So what’s the deal? 🤔

Firmographic, demographic, technographic (components of quality market segmentation) data are spread across the web. And even once they’re pulled into our workflows they’re often siloed, still only semi-structured, or otherwise disconnected. Data brokers provide data that gets stale more quickly than quality curated web sources.

But the fact persists, all the lead generation data you typically need is spread across the public web.

You just needs someone (or something 🤖) to find, read, and structure this data.

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Towards A Public Web Infused Dashboard For Market Intel, News Monitoring, and Lead Gen [Whitepaper]

Posted on | by Merrill Cook

It took Google knowledge panels one month and twenty days to update following the inception of a new CEO at Citi, a F100 company. In Diffbot’s Knowledge Graph, a new fact was logged within the week, with zero human intervention and sourced from the public web.

The CEO change at Citi was announced in September 2020, highlighting the reliance on manual updates to underlying Wiki entities.

In many studies data teams report spending 25-30% of their time cleaning, labelling, and gathering data sets [1]. While the number 80% is at times bandied about, an exact percentage will depend on the team and is to some degree moot. What we know for sure is that data teams and knowledge workers generally spend a noteworthy amount of their time procuring data points that are available on the public web.

The issues at play here are that the public web is our largest — and overall — most reliable source of many types of valuable information. This includes information on organizations, employees, news mentions, sentiment, products, and other “things.”

Simultaneously, large swaths of the web aren’t structured for business and analytical purposes. Of the few organizations that crawl and structure the web, most resulting products aren’t meant for anything more than casual consumption, and rely heavily on human input. Sure, there are millions of knowledge panel results. But without the full extent of underlying data (or skirting TOS), they just aren’t meant to be part of a data pipeline [2].

With that said, there’s still a world of valuable data on the public web.

At Diffbot we’ve harnessed this public web data using web crawling, machine vision, and natural language understanding to build the world’s largest commercially-available Knowledge Graph. For more custom needs, we harness our automatic extraction APIs pointed at specific domains, or our natural language processing API in tandem with the KG.

In this paper we’re going to share how organizations of all sizes are utilizing our structured public web data from a selection of sites of interest, entire web crawls, or in tandem with additional natural language processing to build impactful and insightful dashboards par excellence.

Note: you can replace “dashboard” here with any decision-enabling or trend-surfacing software. For many this takes place in a dashboard. But that’s really just a visual representation of what can occur in a spreadsheet, or a Python notebook, or even a printed report.

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Download This Dataset of 12,118 Yahoo Answers for $1

Posted on | by Jerome Choo

With only 2 weeks left till May 4th (be with you), the internet is bursting with excitement over all the work that needs to be done before Yahoo Answers finally 404s.

From scheduling a 2nd COVID vaccine to your annual panic attack at missing the tax filing deadline (you probably didn’t, it was extended to May 17 in the U.S.), there is nothing short of a lengthy agenda for everyone ahead of the shutdown of this iconic website.

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The 6 Biggest Difficulties With Data Cleaning (With Work Arounds)

Posted on | by Merrill Cook

Data is the new soil.

David Mccandless

If data is the new soil, then data cleaning is the act of tilling the field. It’s one of the least glamorous and (potentially) most time consuming portions of the data science lifecycle. And without it, you don’t have a foundation from which solid insights can grow.

At it’s simplest, data cleaning revolves around two opposing needs:

  • The need to amend data points that will skew the quality of your results
  • The need to retain as much of your useful data as you can

These needs are often most strictly opposed when choosing to clean a data set by removing data points that are incorrect, corrupted, or otherwise unusable in their present format.

Perhaps the most important result from a data cleaning job is that results be standardized in a way that analytics and BI tools can easily access any value, present data in dashboards, or otherwise make the data manipulatable.

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The 25 Most Covid-Safe Restaurants in San Francisco (According to NLP)

Posted on | by Merrill Cook

A few weeks ago, we ran reviews for a Michelin-reviewed restaurant through our Natural Language API. It was able to tell us what people liked or disliked about the restaurant, and even rank dishes by sentiment. In our analysis, we also noticed something curious. When our NL API pulled out the entity “Covid-19,” it wasn’t always paired with a negative sentiment.

When we mined back in to where these positive mentions of Covid-19 occurred in the reviews, we saw that our NL API appeared to be picking up on language in which restaurant reviewers felt a restaurant had handled Covid-19 well. In other words, when Covid-19 was determined to be part of a positive statement, it was because guests felt relatively safe. Or that the restaurant had come up with novel solutions for dealing with Covid-19.

With this in mind, we set to starting up another, larger analysis.

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