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Knowledge Graphs: Powerful Structures Making Sense of Data

by internationalbanker

By Cary Springfield, International Banker


Have you noticed these days that when you use Google to search for, say, a famous person, a panel of information about your chosen search query shows up on the righthand side of the page next to the web results? This panel will typically show a brief profile of the person that includes some basic but highly relevant information, such as birthdate, spouse and children, important works or achievements, social-media accounts and a list of other notable figures who have some connection to the subject and for whom people also search in conjunction with the person. All of this highly useful and easily presentable information is made possible through knowledge graphs.

Also known as knowledge bases, knowledge graphs are collections of interlinked descriptions of entities and objects, which, thanks to their semantic metadata, give them context and connect them with each other. Such contextualisation provides us with more knowledge about a certain topic, such as the attributes and characteristics that make it up, and ultimately enables us to understand more about the specific item we are querying. And by collecting important information about various objects and thus providing real-world context to various sources of data, knowledge graphs can have hugely significant impacts across many diverse applications. It also means that while the standalone objects themselves are undoubtedly important, it is from understanding the relationships between those objects that the most useful insights can be gleaned, which is what makes the knowledge graph so powerful.

Google launched its version of a knowledge graph in May 2012. By gathering information from a multitude of sources, the Google Knowledge Graph allows Google to improve the relevancy and accuracy of its search results greatly. “Things, not strings” is how Google itself describes the technology, in that the words being entered into the search engine are not simply strings of characters but rather real-world items with connections to each other. As such, the notion of searching by using a search engine is able to go beyond simply matching keywords to queries and instead delivers results that more intelligently understand real-world entities and their relationships to one another.

“The Knowledge Graph enables you to search for things, people or places that Google knows about—landmarks, celebrities, cities, sports teams, buildings, geographical features, movies, celestial objects, works of art and more—and instantly get information that’s relevant to your query,” explains Google. “This is a critical first step towards building the next generation of search, which taps into the collective intelligence of the web and understands the world a bit more like people do.”

Google is not the only tech giant utilising knowledge graphs. Facebook employed its own semantic search engine in 2013 called Facebook Graph Search. Although smaller in scope than the Google Knowledge Graph, Facebook’s version nonetheless has similarly improved the search function within its platform by connecting relationships among its users through the information it collects about such relevant objects as the music, movies, celebrities and places that interest its users. And in both cases, the end goal of their knowledge graphs is similar—to add value to the vast amount of data out there such that it can be utilised more meaningfully and intelligently in a real-world context, ultimately producing much smarter user experiences.

It should come as no surprise, then, that a rapidly expanding number of industries are now leveraging the power of knowledge graphs for meaningful insights. Given how heavily the financial-services industry relies on data—typically through the use of spreadsheets and databases—the potential to link much of this data and thus enable it to take on new dimensions of meaning and interpretation via knowledge graphs is immense. Indeed, Capco expects knowledge graphs to play a major role in shaping the financial-services industry in the future. The business-and-technology-management consulting firm forecasts that within the next two years, 80 percent of financial-services firms will be building graphs. For instance, the engineering of structured financial products can utilise knowledge graphs to fit reality more accurately. “The need to fit products into tabular structures limits their ability to flex to real-world needs,” Capco noted in its June 2020 publication “Knowledge Graphs: Building Smarter Financial Services”. “Too often products cannot be offered because the data model cannot represent the demand.”

Given that businesses and organisations are increasingly required to boost their capabilities to glean insights from often substantial volumes of raw data, for instance, the ability to identify meaningful relationships from data that was initially dispersed across widely distributed sources, locations and even simply different divisions within the same organisation could be a game-changer in terms of unlocking previously untapped utility and value.

Enterprise Knowledge, a consulting firm specialising in knowledge and information management, discussed on its website in 2019 its work with a global development bank, which was required to find a better way to disseminate information and expertise to the bank’s staff to boost the efficiency of projects and enable employees to share knowledge to solve complex project challenges without rework and knowledge loss. In response, the company developed a semantic hub, leveraging a knowledge graph, to collect organisational content, user context and project activities. “This information then powers a recommendation engine that suggests relevant articles and information when an email or a calendar invite is sent on a given topic or during searches on that topic,” according to Enterprise Knowledge. “This will eventually power a chatbot as part of a larger AI Strategy. These outputs were then published on the bank’s website to help improve knowledge retention and to showcase expertise via Google recognition and search optimization for future reference.”

Using knowledge graphs based on this linked data strategy, according to Enterprise Knowledge, has enabled the development bank to connect all of their knowledge assets in a meaningful way to increase the relevancy and personalization of search; enable employees to discover content across unstructured content types, such as webinars, classes or other learning materials based on factors such as location, interest, role, seniority level; and further facilitate connections between people who share similar interests, expertise or locations.

And by enabling linkages between data items that would have otherwise remained disparate and siloed off from each other, moreover, knowledge graphs could represent crucial technology for helping to solve some of the world’s most pressing and complex data-related challenges. The British Standards Institute (BSI), the national standards body of the United Kingdom, is currently working with nongovernmental and aid organisations to maximize the availability, quality and security of pharmaceutical and vaccine products to ensure they reach end patients safely and securely. To do this, the company has partnered with inter-organisational data-exchange specialist Trace Labs to implement a data-management hub based on Trace Lab’s OriginTrail decentralised knowledge graph (DKG) using blockchain technology. Ultimately, the solution “enables product authentication, tracks patient utilization, and highlights any diversion or waste issues”.

Indeed, by making the knowledge graph decentralized, it takes the control of the underlying data out of the hands of a centralised entity such as Google or Facebook, which could otherwise conceivably dictate who has access to the data, as well as the money it can charge by providing access and/or insights derived from the data. The singular, centralised nature of such control can also elicit many serious privacy concerns for users, as was the case with Facebook and its notorious data-harvesting activities with Cambridge Analytica prior to the 2016 US presidential election.

In contrast, Trace Labs’ decentralised knowledge graph utilises blockchain technology to enable trusted data exchange that cannot be controlled by any single entity. It is already being applied across a broad spectrum of use cases to solve long-running challenges, particularly pertaining to global supply-chain matters. According to Trace Labs’ general manager, Jurij Škornik, the solution allows supply-chain partners to share and link data across different organisations and information-technology (IT) systems as well as employ well-established global data standards. “This not only ensures an end-to-end supply chain view, but also significantly increases trust in data, as every part of the DKG can be verified utilizing blockchain—who issued a specific dataset, when, and that the dataset has not been tampered with,” Škornik recently told participants at logistics exhibition WOF Expo. The knowledge graph also allows supply-chain entities to “granularly define who has access to what data—i.e., data can be made fully public, shared with specific supply chain partners, or completely private”.

Ultimately, then, knowledge graphs can provide data items within a meaningful, real-world context instead of having them simply exist with no relationship to each other. And in a world in which data itself is perhaps becoming the most valuable asset of the 21st century, the power of such technology for unlocking value, insight and knowledge about our world cannot be underestimated.


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