Semantic-level Knowledge Graph – Daza Matrix

Semantic-level Word Frequency analyzes the true word frequency of literatures based on Named Entity Recognition (NER), Part-of-Speech Analysis, Root Extraction, and TF-IDF derived from Natural Language Processing (NLP), as well as factors such as the impact factor value of the journal to which the literature belongs and the literature type. This enables you to quickly discover the truly important terms and information contained in massive volumes of literatures.

Semantic-level word frequency analysis is divided into two types: one analyzes the titles and abstracts of literatures; the other analyzes the keywords and Medical Subject Headings (MeSH) of literatures. Both types of word frequency analysis are presented in both Chinese and English to facilitate reading and comparison.

Species Name Recognition, based on FoodWake's curated Global Species Library and Convolutional Neural Networks (CNN), identifies all existing species mentioned in massive volumes of literatures and performs semantic-level word frequency sorting on the recognized species names. In fact, this function can not only accurately identify all existing species but also recognize newly discovered species names in the future. A species name, also known as the scientific name, Latin name, binomial nomenclature, or Linnaean name of a species. FoodWake's curated Global Species Library has included over 290,000 genera and more than 2 million species.

Essentially, for a disease or demand keyword, the Species Name Recognition function enables you to discover the most effective species for treating the disease or addressing the demand among global species.

All recognized species names are presented in both Chinese and English for easy viewing and comparison.

Supplement Recognition, based on FoodWake's curated Global Supplement Library and Convolutional Neural Networks (CNN), identifies all existing supplements mentioned in massive volumes of literatures and performs semantic-level word frequency sorting on the recognized supplements. In fact, this function can not only accurately identify all existing supplements but also recognize newly discovered supplements in the future. FoodWake's curated Global Supplement Library has included over 100,000 types of supplements.

Essentially, for a disease or demand keyword, the Supplement Recognition function enables you to discover the most effective supplements for treating the disease or addressing the demand among global supplements.

All recognized supplements are presented in both Chinese and English for easy viewing and comparison.

Disease Name Recognition, based on the Global Disease Name Library built on the International Classification of Diseases (ICD) and Convolutional Neural Networks (CNN), identifies all existing disease names mentioned in massive volumes of literatures and performs semantic-level word frequency sorting on the recognized disease names. In fact, this function can not only accurately identify all existing disease names but also recognize newly discovered disease names in the future. The Global Disease Name Library has included over 66,000 disease names.

Essentially, for a species or supplement keyword, the Disease Name Recognition function enables you to discover which diseases the species or supplement is most effective at treating among global disease names.

All recognized disease names are presented in both Chinese and English for easy viewing and comparison.

Keyword Context: After discovering a term, species, supplement, or disease name that interests you in Semantic-level Word Frequency, Species Name Recognition, Supplement Name Recognition, or Disease Name Recognition, you will certainly want to know the context (i.e., the contextual environment) in which the term, species, supplement, or disease name appears in the literatures. Simply click on the term, species, supplement, or disease name in Semantic-level Word Frequency, Species Name Recognition, Supplement Name Recognition, or Disease Name Recognition, then click Keyword Context to view the context of the term, species, supplement, or disease name in the literatures in four ways: Phrases, Clauses, Complete Sentences, and Abbreviations. Among them, Abbreviations are reconstructed abbreviated forms after parsing complete sentences according to grammatical structures. In addition to the four types of contexts, super analysis of the term, species, supplement, or disease name is also provided in two ways: bar charts and semantic-level word clouds. The complete data of the four types of contexts is available for download as a plain text file.

Relationship Graph Generation can be regarded as a graphical version of the Abbreviation method in Keyword Context. For example, if you have generated a knowledge graph for anti-cancer, and you found Ganoderma lucidum in Species Name Recognition. At this time, when you click Relationship Graph Generation, you will see interesting verbs such as "treat" and "improve" appearing in the generated relational verb list sorted by word frequency. Now, simply click "treat" in the verb list and then click Relationship Graph Generation, and a relationship graph will be generated with Ganoderma lucidum as the subject, "treat" as the relationship, and any entity parsed from the full-sentence abbreviations as the target.

Data (Literatures) for Semantic-level Knowledge Graph is obtained by real-time searching PubMed, the world's largest biomedical literature database. The Semantic-level Knowledge Graph directly calls the PubMed API and supports all PubMed search Boolean operators, parentheses, and double quotes. This allows you to combine and build keyword search logic ranging from the simplest and easiest to use to the most complex and powerful, and keywords support full English, full Chinese, and mixed Chinese-English searches.

FoodWake's Semantic-level Knowledge Graph – Daza Matrix is a powerful AI tool for you to find natural therapeutic species for diseases and demands; a powerful AI tool to quickly master and understand important information about any term and species; a powerful AI tool to query the efficacy, application fields, and side effects of any drug, supplement, food, or food additive.

Super Data, Super Analysis, Super Species, Super Supplements, and Super Diseases: Super Data breaks through the 10,000-record limit of the PubMed API through a batch retrieval and acquisition mechanism. Regardless of whether the number of eligible retrieved literatures is tens of thousands, hundreds of thousands, or even millions, all can be fully acquired.

Combined with Super Data's batch retrieval and acquisition mechanism, Super Analysis can efficiently generate super semantic-level knowledge graphs (Super Semantic Analysis, Super Species Recognition, Super Supplement Recognition, Super Disease Recognition, Super Keyword Context) for hundreds of thousands of literatures through a batch processing mechanism.

When Daza Matrix completes all data batches, based on FoodWake's curated Global Species Library, Global Supplement Library, and Convolutional Neural Networks (CNN), combined with Correlation Coloring implemented by LLM/LSTM+SHA, it can find the most effective Super Species and Super Supplements for your current demands and diseases. Based on the Global Disease Name Library and Convolutional Neural Networks, you can view a complete graph of which diseases the current species or supplement is most effective at treating.

Super Formula Design: Using Super Formula Design in Daza Matrix's Super Data, you can directly select the most effective species and supplements from over 2 million global species + over 100,000 global supplements based on the analysis results of Super Species Recognition and Super Supplement Recognition in Daza Matrix's Super Data. Combined with Large Language Models' capabilities in knowledge integration and retrieval, logical reasoning and analysis, data processing and prediction, and risk avoidance and ethical boundary control, complete formulas with significant effects, safety and reliability, full-path multi-target, and multi-path multi-mechanism synergy based on medical literatures are generated.

Four Large Language Models are currently supported: DeepSeek-R1, DeepSeek-V3, Doubao-1.5-pro, and DeepSeek-R1 (Volcano).

My Super Formulas (Custom Formula Pool): Using My Super Formulas (Custom Formula Pool) in Daza Matrix's Super Data, you can further select the ingredients you most want to use from Super Species Recognition and Super Supplement Recognition to build your own formula pool. By clearly viewing the correlation between species/supplements and diseases or demands, as well as literature titles and abstracts, you can easily select the most effective species and supplements and add them to your custom formula pool. Then, let the Large Language Model generate top-tier formulas directly based on your formula pool. Compared with directly using Super Formula Design, My Super Formulas (Custom Formula Pool) allows you to more precisely construct formula ingredients and generate top-tier formulas with the ingredients you most want to use.

Four Large Language Models are currently supported: DeepSeek-R1, DeepSeek-V3, Doubao-1.5-pro, and DeepSeek-R1 (Volcano).

When using My Super Formulas (Custom Formula Pool), you can not only specify which species/supplements are added to the formula pool but also fully control the final generated formula by setting Mandatory Ingredients, Number of Ingredients, Target Species, and Special Instructions.

Mandatory Ingredients are used to specify ingredients that must be used in formula design. You can even add ingredients not present in the formula pool to Mandatory Ingredients. Simply follow this format: separate each ingredient with ;; , and separate the English name and Chinese name of each ingredient with @ . If you do not know the Chinese name of an ingredient, just replace the Chinese name with the English name, like this: honey@honey.

Number of Ingredients will select 3 to 10 ingredients to build the formula by default. If you want to change the range of the number of ingredients, just enter two numbers separated by an English comma. For example, enter 8,15 to specify that the formula will be built using 8 to 15 ingredients.

Target Species : The formula is designed for human adults (or minors over 12 years old) by default. If the formula is designed for a specific period of human life (e.g., infants, toddlers, pregnant women) or for any other species (e.g., dogs, cats, parrots, etc.), simply enter it here.

Special Instructions : You can specify any additional important instructions here. For example, if you are designing a formula for treating gastric ulcers for patients with diabetes, you can state in Special Instructions that the formula is for diabetic patients. This way, honey that might otherwise be included in the formula will not be selected.