When it comes to seeking answers, the times have truly changed. From the days of keyword-based search to today's intelligent search capabilities, knowledge discovery has transformed to the point where users can access deep knowledge about almost anything at the click of a button (and all on the same page!).
Consider Google – when you input "How can I grow my hair faster?" – it displays a rich blog snippet that shares helpful tips and even sponsored product recommendations on the side. When you scroll down, it displays a series of related questions and then a list of articles and links that match your query. But how does knowledge management work? And what does semantic search have to do with it?
Knowledge discovery and management - what it is
Question answering is a form of information retrieval in which a person or machine answers a question posed in natural language. In the search context, knowledge discovery refers to extracting useful information from a dataset. This includes customer-facing knowledge, such as product information and pricing, and behind-the-scenes operational data, such as shipping schedules and inventory levels.
Knowledge discovery aims to find hidden patterns and relationships in data that can be used to solve problems or make decisions. For example, a company might use knowledge discovery to analyze customer purchase data and discover trends that could help them improve its sales strategy.
Google has been doing knowledge discovery in searches for years, using its unique position as the world's largest search engine to uncover trends and relations hidden within the billions of queries typed into its system daily. This has led to significant advances in search, such as Google's ability to provide "instant answers" to certain types of queries and its recent foray into "semantic search" with the launch of Knowledge Graph.
While knowledge discovery in search is not a new concept, it is becoming increasingly important as we move towards an age where more and more information is generated daily.
Knowledge management starts with the search bar
The search bar is where most of the knowledge-seeking and discovery begins for many people. A robust knowledge management system is adept at addressing three prominent search use cases -
Classic search - This includes searches for a specific place, person, thing, service, or to accomplish a task, such as buying an item or streaming a video.
Questions and answers - This is when the user types in a specific question to look for a value-focused answer
Knowledge - Knowledge search is when a user is looking to 'study' or dive deeper into a topic or a subject. This could be with the intent of researching, learning new things, making informed decisions, and more.
This means an intelligent knowledge management system integrates all three of these use cases and provides access to the most helpful information about the user's query.
The connection between federated search and knowledge management
One powerful system for knowledge management is federated search, a type of multidimensional search that displays different kinds of information on a single interface. By aggregating the results from multiple data sources, federated search can provide users with more comprehensive and accurate results than traditional search techniques. The results are often displayed as hyperlinks, facets, filters, merchandising, and knowledge panels.
A federated search engine allows a user to enter a search query once and receive results from many different sources, including databases, websites, and digital libraries. Users can use federated search engines to find information about specific topics or products. They can also use them to compare prices, find contact information, or locate product manuals. Additionally, federated search engines can be used to monitor changes in content across multiple data sources. This allows organizations to keep track of new information as it becomes available and ensure that their knowledge base is up-to-date.
Semantic search and knowledge - how it works
When you enter a search query, the search engine analyzes the words in your query (and the meaning behind and the relationship between those words) and attempts to match them with other related content on the web. This process is called semantic analysis. Semantic analysis aims to understand the meaning of a piece of text so that it can be classified and indexed accordingly. This allows for more relevant results to be returned when a user enters a search query.
In knowledge management, semantic search can be used to find documents related to a particular topic or concept. This can be very helpful in finding information hidden in extensive collections of unstructured data. In question answering, semantic search can be used to find answers to questions that have been asked by users.
Semantic search algorithms use vectors to understand the relationships between different pieces of content. Vector space models are a type of mathematical model that is often used in semantic search. They work by representing concepts as vectors in a multidimensional space. The distance between two vectors in this space represents the similarity between the two concepts. This means that when a query is made, the vector space model can be used to find the closest match by calculating the shortest distance between the query vector and all other concept vectors in the space.
When a user asks a question, the algorithm will look for the most similar vectors in the vector space. Based on the relationships between those vectors, it will generate an answer. To deliver knowledge, semantic search relies on ontologies and Knowledge Graphs. These are structures of data that are used to model concepts and relationships between them. When a query is made, the ontology is used to understand what the user is looking for and return results accordingly.
Understanding knowledge graphs and vectors
A knowledge graph is a representation of real-world entities and the relationships between them. For a semantic search engine to understand the relationship between two documents, it must first understand the relationship between the entities within those documents. That's where knowledge graphs come in. By mapping out the relationships between entities, knowledge graphs allow semantic search engines to understand the context of a document and how it relates to other documents.
Semantic search engines crawl the web to build a knowledge graph and extract data from sources like structured databases, unstructured text, and images. This data is then organized into a graph structure with nodes representing entities and edges representing relationships between entities. The resulting graph can be used to answer questions or provide recommendations.
For example, imagine you have a knowledge graph about animals. If someone asks you, "What are the biggest animals?" you could use semantic search to find an answer even though there is no explicit data about size in the knowledge graph. This is because semantic search would understand that the question asks for a list of animals sorted by size. It would return the most relevant information from the knowledge graph accordingly.
In today's fast-paced and ever-changing business environment, having a sound knowledge management system can give organizations a significant competitive advantage. If customers can find the answers to their questions quickly and easily (and in multiple forms), they are more likely to have a pleasant experience on your website. Imagine offering a knowledge panel, multiple links, product suggestions, and product information – all on the same search interface.
With AI-powered search and discovery solutions like Zevi, businesses can offer impeccable search performance to their users. Zevi relies on state-of-the-art search technologies like semantic search, natural language processing, and machine learning algorithms to better understand user intent and deliver relevant and personalized results.
To try Zevi, book a free demo today.