With the continuous development of the Internet, the volume of data is soaring sharply; the question-answering system plays an increasingly important role in our lives.
The current system knowledge base is constructed manually, costing a lot of human and material resources and hindering the expansion of the application of the question-answering system from a single field to the whole area.
Therefore, the construction of domain lexicon, knowledge base, and semantic word ranking (SWR) algorithms based on the relation degree of the words is critical not just for Google, the primary provider of search results, but for companies trying to rank high.
The semantic ranking is an extension of the query execution pipeline that improves precision by reranking the top matches of an initial result set.
The semantic ranking is backed by large transformer-based networks, trained for capturing the semantic meaning of query terms, as opposed to linguistic matching on keywords.
In contrast with the default similarity ranking algorithm, the semantic ranker uses the context and meaning of words to determine relevance.
However, not all ranking algorithms are made equal, and the latest generation of algorithms improves the knowledge base’s accuracy and construction.
In this article, we’ll look at the world’s top 5 semantic ranking algorithms and their use cases.
- – Google Ranking Algorithms
While the current Google Semantic Search algorithm builds on Page Rank and Google Hummingbird algorithms launched almost a decade ago, there’s far more than just a name or a search algo type.
Google’s algorithm is extremely complex, using well in excess of 200 proprietary ranking factors.
However, Google’s search algorithms can be classified based on their mechanism of searching into three types of algorithms: linear, binary, and hashing.
Linear search algorithms check every record for the one associated with a target key linearly.
Binary, or half-interval, searches repeatedly target the center of the search structure and divide the search space in half.
Comparison search algorithms improve linear searching by successively eliminating records based on comparisons of the keys until the target record is found and can be applied to data structures with a defined order.
Digital search algorithms work based on the properties of digits in data structures using numerical keys. Finally, hashing maps keys to records based on a hash function.
Google search algorithm is designed to power up search engines, particularly Google, and it “competes” with Microsoft’s ranking algorithm by providing 93% of the world’s results to date.
- – LARA Media Group
The London-based company LARA Media Group has just announced the launch of its proprietary ranking algorithm named the SEMSEA 1.1.
Developed in collaboration with the University of Oxford’s ACT group, the algorithm is specifically designed for large media corporations by providing real-time access to hundreds of Google ranking parameters.
The semantic algorithm was tested on LARA Media Group’s beauty and fashion magazines, showcasing a 250% increase in Google ranking results, 9x more clicks compared to RankMath, and similar SEO-driven publishing algorithms.
SEMSEA 1.1 stands out from the competition by extracting binary elements from text, images, video, and audio and combining them with entity saturation, search queries, intent, link quality and relevancy.
SEMSEA 1.1’s humanlike understanding of highly complex queries, multimodal construct able to handle diverse media types, and granular output will allow large media companies to create high-quality, relevant, top-ranking content.
- – Phasecraft
After announcing the most significant seed funding round for a UK quantum computing company last year in 2020, the UCL and University of Bristol spin-out Phasecraft crafts a ranking algorithm for real-world applications of quantum computing.
Devised by Lov Grover in 1996 specifically for quantum computing, Grover’s algorithm, also known as the quantum search algorithm, refers to a quantum algorithm for unstructured search that finds with high probability the unique input to a black box function that produces a particular output value.
Phasecraft tackles the biggest challenge in quantum computing: developing new algorithms that maximize the capabilities of today’s quantum hardware to evolve quantum computing from experimental demonstrations to practical applications.
To date, Phasecraft has established partnerships with leading quantum hardware companies, including Google and Rigetti, industry partners such as Johnson Matthey, and leading academics to develop a new era of computing.
- – Microsoft
Azure Cognitive Search is the only cloud search service with built-in AI capabilities that enrich all types of information to help you identify and explore relevant content at scale.
Microsoft uses cognitive skills for vision, language, and speech and allows the use of custom machine learning models to uncover insights from all types of content.
Azure Cognitive Search also offers semantic search capability, using advanced machine learning techniques to understand user intent and contextually rank the most relevant search results.
As the company puts it, “spend more time innovating and less time maintaining a complex cloud search solution.”
- – Ahrefs
The search engine toolkit company Ahrefs confirmed they’ve been working on their own search engine called Yep.
After an investment of $60 million, the new search engine runs its own search algorithm rather than relying on APIs from Google or Bing.
The company spins up its own data centers, with over 1,000 servers storing over 100 petabytes of data.
The search engine project has a team of 11 — including data scientists, backend engineers, and front-end developers.
