The Cognonto demo is powered by an extensive knowledge graph called the KBpedia Knowledge Graph, as organized according to the KBpedia Knowledge Ontology (KKO). KBpedia is used for all kinds of tasks, some of which are demonstrated by the Cognonto use cases. The KBpedia Knowledge Graph is a structure of more than 39,000 reference concepts linked to 6 major knowledge bases and 20 popular ontologies in use across the Web. Unlike other knowledge graphs that analyze big corpuses of text to extract "concepts" (n-grams) and their co-occurrences, KBpedia has been created, is curated, is linked, and evolves using humans for the final vetting steps. KBpedia and its build process is thus a semi-automatic system.

We interview LinkedIn about their recently published LinkedIn Knowledge Graph which connects their many millions of members, jobs, companies, and more.

A shorter version of this post first appeared on Pulse, our main publishing platform at LinkedIn. At LinkedIn, we use machine learning technology widely to optimize our products: for instance, ranking search results, advertisements, and updates in the news feed, or recommending people, jobs, articles, and learning opportunities to members. An important component of this technology stack is a knowledge graph that provides input signals to machine learning models and data insight pipelines to power LinkedIn products. This post gives an overview of how we build this knowledge graph. LinkedIn's knowledge graph is a large knowledge base built upon "entities" on LinkedIn, such as members, jobs, titles, skills, companies, geographical locations, schools, etc.

Knowledge graph construction consists of two tasks: extracting information from external resources (knowledge population) and inferring missing information through a statistical analysis on the extracted information (knowledge completion). In many cases, insufficient external resources in the knowledge population hinder the subsequent statistical inference. The gap between these two processes can be reduced by an incremental population approach. We propose a new probabilistic knowledge graph factorisation method that benefits from the path structure of existing knowledge (e.g. syllogism) and enables a common modelling approach to be used for both incremental population and knowledge completion tasks. More specifically, the probabilistic formulation allows us to develop an incremental population algorithm that trades off exploitation-exploration. Experiments on three benchmark datasets show that the balanced exploitation-exploration helps the incremental population, and the additional path structure helps to predict missing information in knowledge completion.

LinkedIn knowledge graph is a large knowledge base built upon "entities" on LinkedIn, such as members, jobs, titles, skills, companies, geographical locations, schools, etc. These entities and the relationships among them form the ontology of the professional world and are used by LinkedIn to enhance its recommender systems, search, monetization and consumer products, business and consumer analytics. Creating a large knowledge base is a big challenge. Web sites like Wikipedia and Freebase primarily rely on direct contributions from human volunteers. Other related work such as Google's Knowledge Vault and Microsoft's Satori focuses on automatically extracting facts from the Web by leveraging the data redundancy nature of big data for constructing knowledge bases.

Two years ago, our ambitious team of data scientists, engineers and visualisation experts set out to tackle the challenging problem of interconnecting the entire universe of research. Using this incredibly powerful knowledge graph, we aimed to provide breakthrough insights about the past and present of research, and by applying predictive techniques we sought to outline the future of research at a global scale. Using big data analytics, machine learning and artificial intelligence, our team worked determinedly for two years piecing together the world's most comprehensive and continuously updating knowledge graph. Today, we are excited to introduce wizdom.ai, Our goal is to utilise this powerful research graph, representing the collective knowledge of human civilisation to answer the most fundamental questions for researchers, research institutions, publishers, funding organisations, businesses and governments – explore the extensive range of questions addressed by our team on the wizdom.ai

GloVe is essentially a log-bilinear model with a weighted least-squares objective. The main intuition underlying the model is the simple observation that ratios of word-word co-occurrence probabilities have the potential for encoding some form of meaning. For example, consider the co-occurrence probabilities for target words ice and steam with various probe words from the vocabulary. As one might expect, ice co-occurs more frequently with solid than it does with gas, whereas steam co-occurs more frequently with gas than it does with solid. Both words co-occur with their shared property water frequently, and both co-occur with the unrelated word fashion infrequently.

SEO is a constant source of anxiety among business owners and marketers; especially in recent years, as Google has introduced and increased its focus on "contextual" search. The Knowledge Graph is one of the best examples, because it presents a tremendous opportunity for quicker and more detailed viewer engagement--but only if you know how it works and how to get yourself listed. With the above in mind, in this article we'll go over what the Knowledge Graph is, why it should matter to you, and how to best optimize your website for inclusion. The Knowledge Graph is a knowledge base used by Google. It was created in 2012 by Google so that it could better understand the world the way people do by using entity-based searches.

Despite the tremendous amount of information on Wikipedia, only a very small amount is structured. Most of the information is embedded in unstructured text and extracting it is a non trivial challenge. In this paper, we propose a full pipeline built on top of DeepDive to successfully extract meaningful relations from the Wikipedia text corpus. We evaluated the system by extracting company-founders and family relations from the text. As a result, we extracted more than 140,000 distinct relations with an average precision above 90%.

Sum-Product Networks (SPNs) are a class of expressive yet tractable hierarchical graphical models. LearnSPN is a structure learning algorithm for SPNs that uses hierarchical co-clustering to simultaneously identifying similar entities and similar features. The original LearnSPN algorithm assumes that all the variables are discrete and there is no missing data. We introduce a practical, simplified version of LearnSPN, MiniSPN, that runs faster and can handle missing data and heterogeneous features common in real applications. We demonstrate the performance of MiniSPN on standard benchmark datasets and on two datasets from Google's Knowledge Graph exhibiting high missingness rates and a mix of discrete and continuous features.