"A semantic network or net is a graphic notation for representing knowledge in patterns of interconnected nodes and arcs. Computer implementations of semantic networks were first developed for artificial intelligence and machine translation, but earlier versions have long been used in philosophy, psychology, and linguistics. What is common to all semantic networks is a declarative graphic representation that can be used either to represent knowledge or to support automated systems for reasoning about knowledge. Some versions are highly informal, but other versions are formally defined systems of logic. ...The oldest known semantic network was drawn in the 3rd century AD by the Greek philosopher Porphyry in his commentary on Aristotle's categories."
– from John F. Sowa, Semantic Networks, revised and extended version of article originally written for the Encyclopedia of Artificial Intelligence, edited by Stuart C. Shapiro, Wiley, 1987, second edition, 1992.
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A couple of weeks ago, I met Francois Vanderseypen, a Graph Data Science consultant. We decided to join forces and start a Graph Machine learning blog series. This blog post will present how to perform knowledge graph completion, which is simply a multi-class link prediction. Instead of just predicting a link, we are also trying to predict its type. For knowledge graph completion, the underlying graph should contain multiple types of relationships.
Let's take a look at the visualization of the knowledge graph we built. We do this by clicking the Graph tab at the top bar, where the page will then display the graph diagram (based on a random sample of the entire network). The red nodes represent the drugs, while the blue nodes represent the side effects. We can easily see that Aspirin has the highest number of side effects based on node size. When we select a single side effect, e.g., hyporeflexia, we can see which drugs can potentially cause that specific side effect.
Enterprise machine learning deployments are limited by two consequences of outdated data management practices widely used today. The first is the protracted time-to-insight that stems from antiquated data replication approaches. The second is the lack of unified, contextualized data that spans the organization horizontally. Excessive data replication and the resulting "second-order effects" are creating enormous efficiencies and waste for data scientists in most organizations. According to IDC, over 60 zettabytes of data were produced last year, and this is forecast to increase at a CAGR of 23 percent until 2025.
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.
Neuro-Symbolic AI, which is alternatively called composite AI, is a relatively new term for a well-established concept with enormous significance for almost any enterprise application of Artificial Intelligence. By combining AI's statistical foundation (exemplified by machine learning) with its knowledge foundation (exemplified by knowledge graphs and rules), organizations get the most effective cognitive analytics results with the least amount of headaches--and cost. Pairing these two historical pillars of AI is essential to maximizing investments in these technologies and in data themselves. By itself, rules-based symbolic reasoning doesn't improve over time. Together, these AI approaches create total machine intelligence with logic-based systems that get better with each application.
While the natural language processing (NLP) field has been growing at an exponential rate for the last two years -- thanks to the development of transfer-based models -- their applications have been limited in scope for the job search field. LinkedIn, the leading company in job search and recruitment, is a good example. While I hold a Ph.D. in Material Science and a Master in Physics, I am receiving job recommendations such as Technical Program Manager at MongoDB and a Go Developer position at Toptal which are both web developing companies that are not relevant to my background. This feeling of irrelevancy is shared by many users and is a cause of big frustration. Job seekers should have access to the best tools to help them find the perfect match to their profile without wasting time in irrelevant recommendations and manual searches...
If you are an experienced reader of such digests (or previous posts) then you know pretty well the abundance of KG-augmented LMs published at every conference and uploaded to arxiv weekly. If you feel lost -- I can assure you're not the only one. This year, we finally have a sound framework and taxonomy of various KG LM approaches! The authors define 3 big families: 1 no KG supervision, probing knowledge encoded in LM params with cloze-style prompts; 2 KG supervision with entities and IDs; 3 KG supervision with relation templates and surface forms. Each family has a few branches For instance, let's have a look at 4 entity-aware models illustrated below.
In this section, we will introduce KG by asking some simple but intuitive questions about KG. In fact, we will cover the what, why, and how of the knowledge graph. We will also go through some real-world examples. This is the very first and a valid question anyone will ask when introduced to KG. We will try to go through some points wherein we compare KG with normal graphs and even other ways of storing information. The aim is to highlight the major advantages of using KG.
Data representation and data itself is the main prerequisite for a successful design and operation of a machine learning model. Data as the input of AI-based systems, such as input signals to a non-AI-based system, are typically correlated with other data elements. Incorrect data collection and representation similar to wrong feature extraction from data is why AI projects do not achieve a mature state as a product. A good example is the collected data from various sensors of an autonomous vehicle, which are related to one another in the time or space domain and whose analysis could help make a more precise prediction of possible events in AI components. A graph contains nodes connected by edges, and it is a visual representation of a network.