Knowledge graphs enable us to comprehend how different points of knowledge relate, giving us an extensive understanding of a field or topic. These graphs help us to discern how individual pieces of knowledge come together to form the larger picture. Clearly, constructing and visualising knowledge graphs can be an effective approach to many fields. In this article, we describe a process to generate new knowledge graphs by leveraging the largest publicly available graph that deals with human knowledge: Wikipedia. We will fully automate the generation process with Python, allowing us to create a scalable approach to generating knowledge graphs for any field of interest.

While Knowledge Graph hype is nowhere near as loud as AI hype, there is no question that more and more organizations are turning to knowledge graphs to solve real-world problems. However, just as with any data solution, there are times when, after the initial acquisition of a knowledge graph solution, companies and IT managers, particularly, wonder what exactly it is they have acquired. All too often, this can result in knowledge graph solutions sitting largely under-utilized because no one can figure out what it's for. Knowledge graphs can make a big difference, but you need to understand these going in and be willing to commit to the project for the long haul. A knowledge graph is, in many ways, a garden, something that you plant and carefully tend, with the dividends coming out over years rather than necessarily all at once.

In their paper The Lifecycle of "Facts": A Survey of Social Bias in Knowledge Graphs, Angelie Kraft and Ricardo Usbeck conducted a critical analysis of literature concerning biases at different steps of a knowledge graph lifecycle. Here, Angelie tells us more about knowledge graphs, how social biases become embedded in them, and what researchers can do to mitigate this. We wanted to understand where and how social biases enter knowledge graphs and how they take effect. To achieve this, we conducted a literature survey that considered a knowledge graph's full lifecycle, from creation to application. Knowledge graphs can be used to represent factual information in a structured way.

Abstract: In recent years, there have been valuable efforts and contributions to make the process of RDF knowledge graph creation traceable and transparent; extending and applying declarative mapping languages is an example. One challenging step is the traceability of procedures that aim to overcome interoperability issues, a.k.a. In most pipelines, data integration is performed by ad-hoc programs, preventing traceability and reusability. However, formal frameworks provided by function-based declarative mapping languages such as FunUL and RML FnO empower expressiveness. Data-level integration can be defined as functions and integrated as part of the mappings performing schema-level integration.

Structured and unstructured data and facts about drugs, genes, protein, viruses, and their mechanism are spread across a huge number of scientific articles. These articles are a large-scale knowledge source and can have a huge impact on disseminating knowledge about the mechanisms of certain biological processes. A knowledge graph (KG) can be constructed by integrating such facts and data and be used for data integration, exploration, and federated queries. However, exploration and querying large-scale KGs is tedious for certain groups of users due to a lack of knowledge about underlying data assets or semantic technologies. A question-answering (QA) system allows the answer of natural language questions over KGs automatically using triples contained in a KG.

Stardog, the leading Enterprise Knowledge Graph platform provider, announced it has achieved System and Organization Controls (SOC) 2 Type 1 compliance, demonstrating the company's commitment to providing the most robust data security and privacy for its growing customer base. The SOC 2 Type I audit, conducted by Riskpro, is an independent review assessing Stardog's internal controls involving security, availability, and confidentiality of the data processed on behalf of its customers. A widely recognized auditing standard developed by the American Institute of Certified Public Accounts (AICPA), SOC 2 compliance confirms Stardog's controls and processes meet AICPA Trust Service Criteria. "As enterprise organizations use knowledge graph in the cloud to help them democratize data access and scale analytics insight, they need confidence that their data is secure," said Mike Grove, SVP, Engineering & Information Security of Stardog. "Achieving SOC 2 Type 1 certification eliminates the burden on our customers of securing data, allowing them to focus on driving business outcomes."

Dr. Guilin Qi is a professor at Southeast University, China, where he also serves as director of the Institute of Cognitive Intelligence and of the Knowledge Science and Engineering Lab. His research interests include knowledge representation and reasoning, knowledge graphs, uncertainty reasoning, and the semantic web. Prof. Qi is an editorial board member of the Journal of Web Semantics, and has co-edited special issues for the Annals of Mathematics and Artificial Intelligence, International Journal of Approximate Reasoning and Journal of Applied Logic. He has over 20 years of research experiences in knowledge engineering and has led many national and industrial projects on knowledge graphs. Prof. Qi has published more than 100 papers on knowledge engineering and knowledge graphs and holds two patents.

The universal applicability of enterprise knowledge--across use cases, domains, and languages--is widely understood. And, it's likely the main reason adoption rates for knowledge graphs have steadily inclined of late, making them one of the most utilitarian forms of AI available today. True knowledge graphs are extensible and predicated on standards designed to share data of any type. Such graphs are inherently composable, enabling users to either combine them or enrich them with knowledge of all sorts. These options are critical for not only simplifying the management of enterprise knowledge for Natural Language Understanding deployments, but also for redoubling the value organizations reap from knowledge graphs across a burgeoning array of use cases.

Data management agility has become of key importance to organizations as the amount and complexity of data continues to increase, along with the desire to avoid creating new data silos. The concept of creating a'data fabric' as an agile design concept has been proposed by leading analysts, such as Mark Beyer, Distinguished VP Analyst at Gartner. "The emerging design concept called'data fabric' can be a robust solution to ever present-day management challenges, such as the high-cost and low-value of data integration cycles, frequent maintenance of earlier integrations, the rising demand for real-time and event-driven data sharing, and more," says Mark Beyer. As a data fabric readily connects and provides singular access to all data sources distributed throughout the enterprise, semantic knowledge graphs provide the foundation that makes this design possible. Semantic knowledge graphs and aspects of AI are necessary for the data fabric architecture to work.

I have already demonstrated how to create a knowledge graph out of a Wikipedia page. However, since the post got a lot of attention, I've decided to explore other domains where using NLP techniques to construct a knowledge graph makes sense. In my opinion, the biomedical field is a prime example where representing the data as a graph makes sense as you are often analyzing interactions and relations between genes, diseases, drugs, proteins, and more. In the above visualization, we have ascorbic acid, also known as vitamin C, and some of its relations to other concepts. For example, it shows that vitamin C could be used to treat chronic gastritis.