# How Computers Calculate Probabilities in Large Knowledge Bases > A method for finding answers in a database of uncertain facts by ignoring probabilities to find a solution first, then calculating how likely that solution is based on the underlying evidence. - **Patent:** US 9361579 - **Original title:** Large scale probabilistic ontology reasoning - **Owner:** International Business Machines Corp - **Granted:** 2016 - **Status:** Active - **Times cited:** 1 - **Field:** ai_ml, software, telecommunications ## What it does This patent describes a way to query a knowledge base where facts (axioms) come with a probability score. Instead of trying to calculate complex probabilities for the entire database at once, the system first ignores the probability scores to find a logical solution using standard reasoning. Once it identifies the 'justification'—the minimal set of facts needed to prove that answer—it then applies the probability scores only to that specific subset. This makes it computationally efficient to determine the 'net probability' of a conclusion in massive datasets that would otherwise be too slow to process. ## What it does NOT cover - Does not cover calculating probabilities for the entire knowledge base simultaneously without first finding a logical justification. - Does not cover systems that lack a defined set of axioms or logical rules for deriving answers. - Does not cover non-probabilistic reasoning systems that treat all facts as 100% certain. ## The clever bit The innovation is the 'divide and conquer' strategy: by decoupling the search for a logical answer from the mathematical calculation of its certainty, the system avoids the exponential complexity of calculating probabilities for every possible combination of facts. ## Real-world examples 1. Enterprise knowledge graphs used for automated decision support 2. Semantic search engines that rank results based on probabilistic confidence 3. Medical diagnostic software evaluating symptoms with varying levels of clinical certainty ## Why it matters As AI and expert systems deal with increasingly messy, real-world data, they often encounter conflicting or uncertain information. This approach allows developers to build systems that can provide 'likely' answers without getting bogged down in the massive mathematical overhead of traditional probabilistic modeling. It is a key technique for scaling semantic web technologies and enterprise knowledge graphs. ## Frequently asked questions ### What does How Computers Calculate Probabilities in Large Knowledge Bases cover? A method for finding answers in a database of uncertain facts by ignoring probabilities to find a solution first, then calculating how likely that solution is based on the underlying evidence. ### Who owns patent US 9361579? International Business Machines Corp owns this patent, granted in 2016. ### When does this patent expire? This patent is expected to expire on October 6, 2029, when the invention enters the public domain. ### What is patent US 9361579 cited by? This patent has been cited by 1 later patents that build on its ideas. ### What problem does this patent solve? As AI and expert systems deal with increasingly messy, real-world data, they often encounter conflicting or uncertain information. This approach allows developers to build systems that can provide 'likely' answers without getting bogged down in the massive mathematical overhead of traditional probabilistic modeling. It is a key technique for scaling semantic web technologies and enterprise knowledge graphs. ### What does this patent NOT cover? Does not cover calculating probabilities for the entire knowledge base simultaneously without first finding a logical justification. **Full plain-English explainer:** https://patentbrief.org/patent/us/9361579/large-scale-probabilistic-ontology-reasoning **Original patent:** https://patents.google.com/patent/US9361579 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [How AI Connects Different Databases Using Knowledge Graphs](https://patentbrief.org/patent/us/11507851/system-and-method-of-integrating-databases-based-on-knowledge-graph) — This patent describes a server-based method that uses artificial intelligence and two learning models to automatically find and integrate connections between data fields and data values across multiple databases that have different structures. - [Using Non-AI Systems to Improve AI Text Generation](https://patentbrief.org/patent/us/12353827/computer-implemented-methods-for-the-automated-analysis-or-use-of-data-including) — This patent describes a method where a traditional, rule-based computer system helps a Large Language Model (LLM) generate more accurate and reliable text by providing it with better context and fact-checking. - [How Computers Find Similar Text Using Compact Data Structures](https://patentbrief.org/patent/us/10878335/bert-bidirectional-encoder-representations) — This patent describes a method for efficiently identifying similar text records, like documents or product reviews, by using special compact data structures that store text terms probabilistically and then analyzing them with machine learning. - [How Computers Find Hidden Connections Between Different Fields of Knowledge](https://patentbrief.org/patent/us/6523026/google-search-query-processing) — A method for finding related ideas in completely different subjects by using math to map how words appear together, even when the subjects use different vocabulary. - [How Computers Match and Join Messy Data from Different Sources](https://patentbrief.org/patent/us/9607103/amazon-athena) — A method for merging datasets by identifying related but non-identical items using flexible matching rules rather than strict equality.