"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.
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.
Are you making the most of your collected data? The data you accumulate through your products and services can be a game-changer for your organization. Imagine if you can put that information to the proper use! Knowledge Graphs can allow you to make the most of your information to access, search, and utilize data for your enterprise search needs. A Knowledge Graph is a progressive way of interconnected search, an accurate query search resolution system that combines entities like people, objects, and places.
Tiernan Ray has been covering technology and business for 27 years. He was most recently technology editor for Barron's where he wrote daily market coverage for the Tech Trader blog and wrote the weekly print column of that name. "For the first time, I am trying to say exactly what kinds of value are created by networks," Bob Metcalfe, inventor of Ethernet, told a small group during a soiree on the sidelines of The Knowledge Graph conference. He predicts decentralized knowledge graphs, which marry knowledge graph databases with connectivity, will create new forms of value. When Bob Metcalfe was selling Ethernet to the world as a new networking technology in the 1980s at 3Com Corp., he had a clever sales pitch: You'll get more value out of the product the more of it you buy.
Medical diagnosis is the process of making a prediction of the disease a patient is likely to have, given a set of symptoms and observations. This requires extensive expert knowledge, in particular when covering a large variety of diseases. Such knowledge can be coded in a knowledge graph – encompassing diseases, symptoms, and diagnosis paths. Since both the knowledge itself and its encoding can be incomplete, refining the knowledge graph with additional information helps physicians making better predictions. At the same time, for deployment in a hospital, the diagnosis must be explainable and transparent.
Biomedical knowledge graphs (BioMedKGs) are essential infrastructures for biomedical and healthcare big data and artificial intelligence (AI), facilitating natural language processing, model development, and data exchange. For many decades, these knowledge graphs have been built via expert curation, which can no longer catch up with the speed of today's AI development, and a transition to algorithmically generated BioMedKGs is necessary. In this work, we introduce the Biomedical Informatics Ontology System (BIOS), the first large scale publicly available BioMedKG that is fully generated by machine learning algorithms. BIOS currently contains 4.1 million concepts, 7.4 million terms in two languages, and 7.3 million relation triplets. We introduce the methodology for developing BIOS, which covers curation of raw biomedical terms, computationally identifying synonymous terms and aggregating them to create concept nodes, semantic type classification of the concepts, relation identification, and biomedical machine translation.
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. In general, however, traditional job search engines are based on simple keyword and/or semantic similarities that are usually not well suited to providing good job recommendations since they don't take into account the interlinks between entities.
It's been ten years since Google (now a child of holding company Alphabet) coined the term "knowledge graph" and described (in general terms) how their knowledge graph worked. And it's been over 20 years since Tim Berners-Lee, James Hendler and Ora Lassila published their first article to describe the semantic web they envisioned. Many knowledge graphs have been built using the semantic standards the W3C subsequently put in motion a decade or more ago. It's interesting to ponder what's happened since. Over the past decade, Alphabet has grown consistently to become one of the top six companies globally to achieve a market capitalization (total stock value of shares outstanding) of over $1 trillion.
I love constructing knowledge graphs from various sources. I've wanted to create a government knowledge graph for some time now but was struggling to find any data that is easily accessible and doesn't require me to spend weeks developing a data pipeline. At first, I thought I would have to use OCR and NLP techniques to extract valuable information from public records, but luckily I stumbled upon UK Gazette. The UK Gazette is a website that holds the United Kingdom's official public record information. All the content on the website and via its APIs is available under the Open Government License v3.0.
To date, there are no effective treatments for most neurodegenerative diseases. Knowledge graphs can provide comprehensive and semantic representation for heterogeneous data, and have been successfully leveraged in many biomedical applications including drug repurposing. Our objective is to construct a knowledge graph from literature to study relations between Alzheimer's disease (AD) and chemicals, drugs and dietary supplements in order to identify opportunities to prevent or delay neurodegenerative progression. We collected biomedical annotations and extracted their relations using SemRep via SemMedDB. We used both a BERT-based classifier and rule-based methods during data preprocessing to exclude noise while preserving most AD-related semantic triples. The 1,672,110 filtered triples were used to train with knowledge graph completion algorithms (i.e., TransE, DistMult, and ComplEx) to predict candidates that might be helpful for AD treatment or prevention. Among three knowledge graph completion models, TransE outperformed the other two (MR = 13.45, Hits@1 = 0.306). We leveraged the time-slicing technique to further evaluate the prediction results. We found supporting evidence for most highly ranked candidates predicted by our model which indicates that our approach can inform reliable new knowledge. This paper shows that our graph mining model can predict reliable new relationships between AD and other entities (i.e., dietary supplements, chemicals, and drugs). The knowledge graph constructed can facilitate data-driven knowledge discoveries and the generation of novel hypotheses.
The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and mapping properties. However, existing approaches can only capture some of them with insufficient modeling capacity. In this work, we propose a more powerful KGE framework named HousE, which involves a novel parameterization based on two kinds of Householder transformations: (1) Householder rotations to achieve superior capacity of modeling relation patterns; (2) Householder projections to handle sophisticated relation mapping properties. Theoretically, HousE is capable of modeling crucial relation patterns and mapping properties simultaneously. Besides, HousE is a generalization of existing rotation-based models while extending the rotations to high-dimensional spaces. Empirically, HousE achieves new state-of-the-art performance on five benchmark datasets. Our code is available at https://github.com/anrep/HousE.