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Overview of Semantic Search  Semantic search is a search technology that uses machine learning and vector search to provide more relevant results by analyzing the meaning behind a user's query.  Some semantic search techniques include:  Semantic query parsing Identifies concepts in a query and offers relevant product discovery. For example, it can identify named entities like brands or manufacturers, or specifications like price range.    Natural language processing (NLP) Helps surface relevant comments related to a search query.    Knowledge graph Captures the meaning of search terms by representing the connections between various data sources.    Embedding models Create vector representations of words to capture their semantic meaning. This creates a "meaning space" where words with similar meanings are represented by nearby vectors.    Semantic matching Finds the query and document pairs that are most similar .    1. Importance of Semantic Searc

Abstract: AI-powered search techniques include:    Personalization AI-powered search engines can provide personalized results for users based on their location, preferences, and behavior.    Semantic search This AI-powered search uses natural language processing to understand the meaning of a search and provide relevant results. For example, it can correct typos, find synonyms, and show similar products.    ChatGPT Bing uses generative AI models to provide more conversational and context-aware answers to user queries.    Information retrieval AI-powered algorithms can analyze user behavior and large amounts of data to generate more relevant search results.    Some other types of search algorithms include: Depth-First Search (DFS) Breadth-First Search (BFS) Uniform Cost Search (UCS) Heuristic Search Pathfinding Optimization Game Playing  Keywords:  Search Techniques, Depth-First Search (DFS), Breadth-First Search (BFS),  Uniform Cost Search (UCS), Heuristic Search, Pathfindi

Abstract: Problem solving is the process of overcoming obstacles to achieve a goal. It's a skill and ability that can be used to resolve issues in many different situations, including personal, school, and work settings. The ability to solve problems is a basic life skill and is essential to our day-to-day lives, at home, at school, and at work. We solve problems every day without really thinking about how we solve them. For example: it’s raining and you need to go to the store. What do you do? There are lots of possible solutions. Take your umbrella and walk. If you don't want to get wet, you can drive, or take the bus. You might decide to call a friend for a ride, or you might decide to go to the store another day. There is no right way to solve this problem and different people will solve it differently. Here are some steps you can take to solve a problem: 1. Identify the problem Clearly state the problem, including the circumstances, timing, and behavior that ma

Abstract Knowledge representation and reasoning (KRR) is a field of artificial intelligence (AI) that focuses on how to represent information in a way that computers can use to solve problems. KRR is important because it allows AI programs to make deductions and answer questions intelligently, similar to how humans do.    Here are some key aspects of KRR:    Representation KRR uses techniques like ontologies and knowledge graphs to represent information in a way that computers can understand. These techniques allow for the representation of relationships and hierarchies within the data.    Reasoning KRR uses reasoning engines to perform logical operations on the represented knowledge to derive new information. This includes drawing conclusions, making inferences, and solving problems.    Applications KRR can be used for a variety of applications, such as path planning, diagnosing medical conditions, and having natural language dialogs.    Incorporation of other fields KRR incorpora