Data analysis comes at the tail end of the data lifecycle. Directly after or simultaneously performed with data integration (in which data from different sources are pulled into a unified view). Data analysis involves cleaning, modelling, inspecting and visualizing data.
The ultimate goal of data analysis is to provide useful data-driven insights for guiding organizational decisions. And without data analysis, you might as well not even collect data in the first place. Data analysis is the process of turning data into information, insight, or hopefully knowledge of a given domain. (more…)
Most of the world’s knowledge is encoded in natural language (e.g., news articles, books, emails, academic papers). It is estimated that 80 percent of business-relevant information originates in unstructured form, primarily text. However, the ambiguous nature of human communication makes it difficult for software engineers and data scientists to leverage this information in their applications.
After years of research, we are proud to announce the Diffbot Natural Language API, a new product to help businesses convert their text documents into knowledge graphs. Knowledge graphs represent information about real-world entities (e.g., people, organizations, products, articles) via their relationships with other entities (e.g., founded by, educated at, was mentioned in). This is the same production-grade technology that we use to build the world’s largest knowledge graph from the web, and we are making it available to all.
The original robots who caught my attention had physical human characteristics, or at least a physically visible presence in three dimensions: C3PO and R2D2 form the perfect duo, one modeled to walk and talk like a bookish human, the other with metallic, baby-like cuteness and it’s own language.
Both were imagined, but still very tangible. And this imagery held staying power. This is how most of us still think about robots today. Follow the definition of robot and the following phrase surface, “a machine which resembles a human.” A phrase only followed by a description of the types of actions they actually undertake.
Most robots today aren’t in the places we’d think to look based on sci-fi stories or dictionary definitions. Most robots come in two types: they’re sidekicks for desktop and server activities at work, or robots that scour the internet to tag and index web content.
All-in-all robots are typically still digital. Put another way, digital robots have come of age much faster than their mechanical cousins.
The story: sentiment of news mentions of Gaza fluctuate by as much as 2000% a week. 90% of news mentions about Minneapolis have had negative sentiment through the first week in June 2020 (they’re typically about 50% negative). Positive sentiment news mentions about New York City have steadily increased week by week through the pandemic.
Locations are important. They help form our identities. They bring us together or apart. Governance organizations, journalists, and scholars routinely need to track how one location perceives another. From threat detection to product launches, news monitoring in Diffbot’s Knowledge Graph makes it easy to take a truly global news feed and dissect how entities being talked about.
In this story by DQL discover ways to query millions of articles that feature location data (towns, cities, regions, nations).
How we got there: One of the most valuable aspects of Diffbot’s Knowledge Graph is the ability to utilize the relationships between different entity types. You can look for news mentions (article entities) related to people, products, brands, and more. You can look for what skills (skill or people entities) are held by which companies. You can look for discussions on specific products. (more…)
We will get justice. We will get it. We will not let this door close.
– Philonise Floyd, Brother of George Floyd
News coverage this week centered on George Floyd, police, and Donald Trump. COVID-19 related news continue to dominate globally.
That’s the macro story from all Knowledge Graph article published in the last week. But Knowledge Graph article entities provide users with many ways to traverse and dissect breaking news. By facet searching for the most common phrases in articles tagged “George Floyd” you see a nuanced view of the voices being heard.
In this story hopefully you can begin to see the power of global news mentions that can be sliced and diced on so many levels. Wondering how to gain these insights for yourself? Below we’ll work through how to perform these queries in detail.
There are only a handful of publicly available knowledge graphs. And among those, only a few provide data with enough breadth to in some way represent the entire internet, and with enough granularity to be useful. (more…)
The entrepreneurs at Topic saw many of their customers struggle with creating trustworthy SEO content that ranks high in search engine results.
They realized that while many writers may be experts at crafting a compelling narrative, most are not experts at optimizing content for search. Drawing on their years of SEO expertise, this two-person team came up with an idea that would fill that gap.
They came up with Topic, an app that helps users create better SEO content and drive more organic search traffic.They had a great idea. They had a fitting name. The next step was figuring out the best way to get their product to market.
Many cornerstone providers of martech bill themselves out as “databases of the web.” In a sense, any marketing analytics or news monitoring platform that can provide data on long tail queries has a solid basis for such a claim. There are countless applications for many of these web databases. But what many new users or those early in their buying process aren’t exposed to is the fact that web-wide crawlers can crawl the exact same pages and pull out extensively different data.
The Google Knowledge Graph is one of the most recognizable sources of contextually-linked facts on people, books, organizations, events, and more.
Access to all of this information — including how each knowledge graph entity is linked — could be a boon to many services and applications. On this front Google has developed the Knowledge Graph Search API.
While at first glance this may seem to be your golden ticket to Google’s Knowledge Graph data, think again. (more…)
Google introduced to the general public the term Knowledge Graph (“Things not Strings”) when they added the information boxes that you see to the right-hand side of many searches. However, the benefits of storing information indexed around the entity and its properties and relationships are well-known to computer scientists and have been one of the central approaches to designing information systems.
When computer scientist Tim-Berners Lee originally designed the Web, he proposed a system that modeled information as uniquely identified entities (the URI) and their relationships. He described it this way in his 1999 book Weaving the Web:
I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web – the content, links, and transactions between people and computers. A “Semantic Web”, which makes this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. The “intelligent agents” people have touted for ages will finally materialize.
You can trace this way of modeling data even further back to the era of symbolic artificial intelligence (Good old fashioned AI”) and the Relational Model of data first described by Edgar Codd in 1970, the theory that forms the basis of relational database systems, the workhorse of information storage in the enterprise.
What is striking is that these ideas of representing information as a set of entities and their relations are not new, but are so very old. It seems as if there is something very natural and human about representing the world in this way. So, the problem we are working on at Diffbot isn’t a new or hypothetical problem that we defined, but rather one of the age-old problems of computer science, and one that is found within every organization that tries to represent the information of the organization in a way that is useful and scalable. Rather, the work we are doing at Diffbot is in creating a better solution to this age-old problem, in the context of this new world that has increasingly large amounts of complex and heterogeneous data.
The well-known general knowledge graphs (i.e. those that are not verticalized knowledge graphs), can be grouped into certain categories: the search engine company maintained KGs: Google, Bing, and Yahoo knowledge graph, community-maintained knowledge graphs: like Wikidata, and academic knowledge graphs, like Wordnet and ConceptNet.
The Diffbot Knowledge Graph approach differs in three main ways: it is an automatically constructed knowledge graph (not based on human labor), it is sourced from crawling the entire public web and all its languages, and it is available for use.
The first point is that all other knowledge graphs involve a heavy amount of human curation – involving direct data entry of the facts about each entity, selecting what entities to include, and the categorization of those entities. At Google, the Knowledge Graph is actually a data format for structured data that is standardized across various product teams (shopping, movies, recipes, events, sports) and hundreds of employees and even more contractors both enter and curate the categories of this data, combining these separate product domains together into a seamless experience. The Yahoo and Bing knowledge graphs operate in the similar way.
A large portion of the information these consumer search knowledge graphs contain is imported directly from Wikipedia, another crowd-sourced community of humans that both enter and curate the categories of knowledge. Wikipedia’s sister project, Wikidata, has humans directly crowd-editing a knowledge graph. (You could argue that the entire web is also a community of humans editing knowledge. However–the entire web doesn’t operate as a singular community, with shared standards, and a common namespace for entities and their concepts–otherwise, we’d have the Semantic Web today).
Academic knowledge graphs such as ConceptNet, WordNet, and earlier, CyC, are also manually constructed by crowd-sourced humans, although to a larger degree informed by linguistics, and often by people employed under the same organization, rather than volunteers on the Internet.
Diffbot’s approach to acquiring knowledge is different. Diffbot’s knowledge graph is built by a fully autonomous system. We create machine learning algorithms that can classify each page on the web as an entity and then extract the facts about that entity from each of those pages, then use machine learning to link and fuse the facts from various pages to form a coherent knowledge graph. We build a new knowledge graph from this fully automatic pipeline every 4-5 days without human supervision.
The second differentiator is that Diffbot’s knowledge graph is sourced from crawling the entire web. Other knowledge graphs may have humans citing pages on the web, but the set of cited pages is a drop in the ocean compared to all pages on the web. Even the Google’s regular search engine is not an index of the whole web–rather it is a separate index for each language that appears on the web . If you speak an uncommon language, you are not searching a very big fraction of the web. However, when we analyze each page on the web, our multi-lingual NLP is able to classify and extract the page, building a unified Knowledge Graph for the whole web across all the languages. The other two companies besides Diffbot that crawl the whole web (Google and Bing in the US) index all of the text on the page for their search rankings but do not extract entities and relationships from every page. The consequence of our approach is that our knowledge graph is much larger and it autonomously grows by 100M new entities each month and the rate is accelerating as new pages are added to the web and we expand the hardware in our datacenter.
The combination of automatically extracted and web-scale crawling means that our knowledge graph is much more comprehensive than other knowledge graphs. While you may notice in google search a knowledge graph panel will activate when you search for Taylor Swift, Donald Trump, or Tiger Woods (entities that have a Wikipedia page), a panel is likely not going to appear if you try searches for your co-workers, colleagues, customers, suppliers, family members, and friends. The former category are the popular celebrities that have the most optimized queries on a consumer search engine and the latter category are actually the entities that surround you on a day-to-day basis. We would argue that having a knowledge graph that has coverage of those real-life entities–the latter category–makes it much more useful to building applications that get real work done. After all, you’re not trying to sell your product to Taylor Swift, recruit Donald Trump, or book a meeting with Tiger Woods–those just aren’t entities that most people encounter and interact with on a daily basis.
Lastly, access. The major search engines do not give any meaningful access to their knowledge graphs, much to the frustration of academic researchers trying to improve information retrieval and AI systems. This is because the major search engines see their knowledge graphs as competitive features that aid the experiences of their ad-supported consumer products, and do not want others to use the data to build competitive systems that might threaten their business. In fact, Google ironically restricts crawling of themselves, and the trend over time has been to remove functionality from their APIs. Academics have created their own knowledge graphs for research use, but they are toy KGs that are 10-100MBs in size and released only a few times per year. They make it possible to do some limited research, but are too small and out-of-date to support most real-world applications.
In contrast, the Diffbot knowledge graph is available and open for business. Our business model is providing Knowledge-as-a-Service, and so we are fully aligned with our customers’ success. Our customers fund the development of improvements to the quality of our knowledge graph and that quality improves the efficiency of their knowledge workflows. We also provide free access to our KG to the academic research community, clearing away one of the main bottlenecks to academic research progress in this area. Researchers and PhD students should not feel compelled to join an industrial AI lab to access their data and hardware resources, in order to make progress in the field of knowledge graphs and automatic information extraction. They should be able to fruitfully research these topics in their academic institutions. We benefit the most from any advancements to to the field, since we are running the largest implementation of automatic information extraction at web-scale.
We argue that a fully autonomous knowledge graph is the only way to build intelligent systems that successfully handle the world we live in: one that is large, complex, and changing.