In this lesson, you will learn about semantic search, its benefits, and its challenges.
What is semantic search?
Semantic search aims to understand search phrases' intent and contextual meaning, rather than focusing on individual keywords.
Traditional keyword search often depends on exact-match keywords or proximity-based algorithms that find similar words.
For example, if you input "apple" in a traditional search, you might predominantly get results about the fruit.
However, in a semantic search, the engine tries to gauge the context: Are you searching about the fruit, the tech company, or something else?
Context
Semantic search is dependent on understanding the context of the search. There are numerous strategies for understanding context, including:
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What other information is included in the search? For example, if the search phrase contains bank and river, the search is likely about waterways, not financial institutions.
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What is known about the user? Their search history and location can provide information about the context of the search. If they are in the UK, a search for "football" is likely about soccer, not American football.
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What scenario is being presented to the user? If the search is on a website about cars, a search for dash is likely about dashboards, not running quickly.
Results
The results of a semantic search are typically scored based on the perceived intent of the search.
The score can be a combination of:
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The relevance of the result to the search
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The popularity of the result
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The quality of the result
You would typically use the score to rank the results.
Why is semantic search useful?
Semantic search allows you to find and score related data. It is useful when finding similarities within unstructured data that rely on understanding the intent and contextual meaning of the search query.
Some typical use cases are:
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Customer Support and Chat-bots - Improving the ability of chat-bots and customer support systems to understand and respond to user queries in a more human-like and contextually relevant manner.
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Product Discovery and Recommendation - Enhancing product search by understanding the nuanced needs and preferences expressed in search queries, leading to better product recommendations.
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Recruitment and Talent Acquisition - Matching job descriptions with candidate profiles more effectively by understanding the deeper meaning and requirements of job postings and the skills and experiences described in resumes.
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Knowledge Management and Information Retrieval - Enhancing the retrieval of information from large databases or document management systems by understanding the context and meaning of the information being sought.
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Anomaly detection - Identifying transactions or messages which are out of the norm and may be fraudulent.
Considerations
Semantic search faces several challenges that stem from the complexity of natural language, the diversity of user intents, and the dynamic nature of information. Some of these challenges include:
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Understanding Context - Accurately grasping the context of queries can be difficult. Different users might use the same words to mean different things.
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Language Ambiguity - Natural language is inherently ambiguous. Words can have multiple meanings, and different models may interpret sentences differently.
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Fine tuning - To get the best result, you may need to invest significant effort in fine-tuning your model, data and search algorithms.
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Transparency - The complexity behind semantic search can make understanding how a score is determined or why a particular result is returned difficult.
Check Your Understanding
Transparency
True or False - You can easily identify how a semantic search engine determines the score for a particular result.
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❏ True
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✓ False
Hint
Semantic search is typically described as a black box process.
Solution
The statement is False. The complexity behind semantic search can make it difficult to understand how a score is determined or why a particular result is returned.
Lesson Summary
In this lesson, you learned about semantic search, its benefits, and its challenges.
In the next lesson, you will use semantic search in Neo4j to find movies with similar plots.