- What is semantic search?
- Semantic search benefits
- Semantic search example
- Difference between keyword search and semantic search
- Difference between lexical search and semantic search
- The technology behind semantic search
- 1. Natural language processing (NLP)
- 2. Semantic analysis
- 3. Knowledge graphs and ontologies
- 4. Machine learning and deep learning
- 5. Indexing and retrieval
- 6. Ranking algorithms
- 7. Feedback loops
- Semantic search use cases
- Conclusion
- Frequently asked questions
Semantic search empowers e-commerce websites to provide relevant and accurate content to the users by understanding the searcher’s intent and the contextual meaning of search queries.
What is semantic search?
Semantic search is a type of search technology that seeks to understand the intent and contextual meaning of a search query, rather than focusing solely on keyword matching.
Semantic search engines often yield the most accurate results for search queries by getting a deeper understanding of contextual meaning based on query context, search intent, and the relationship between words. A semantic search engine applies user search intent and the meaning (or semantics) of words and phrases to find the right content. This means that it can understand users in a human-like way and provide a unique search experience each time.
It goes beyond keyword matching by using information that might not be present immediately in the text (the keywords themselves) but is closely tied to what the searcher wants. It can appear as if it properly understands human language. Consequently, it helps deliver the best-performing and relevant search results quickly.
Semantic search benefits
The main benefit is the semantic search ability to find results even if the entered search terms are not precise. Based on context, the search is able to provide relevant results despite the inclusion of vague search terms. Another benefit is that people can use descriptions as search terms in case they can’t figure out the proper naming conventions or simply forget the proper search term. This significantly improves the user experience thanks to the removal of frustration during a search session.
Semantic search example
For a typical keyword search, a customer may write keywords like ‘sweater’ in the search bar to find a sweater. However, the semantic search will better serve queries like ‘warm clothing’ or ‘How can I keep my body warm in the winter?’. It will understand the intent of the input keywords and come up with accurate results for the topic at hand while maintaining high search relevance. The most obvious use case of this is in internet search engines, such as Google semantic search.
Difference between keyword search and semantic search
A keyword search retrieves all the documents from the database that have a specific search term present in the query. Unlike keyword search, the semantic search takes into account the meaning of the words according to their context. Let’s compare the two side by side.
Keyword search
- Synonyms could be neglected during the search.
- The user needs to carefully pick the keywords for search.
- The information retrieved depends on keywords and page ranking algorithms that can generate spam results.
Semantic search
- As it incorporates the meaning of words, semantic search technology comprehends synonyms well.
- The search query is automatically enriched by latent encoding.
- The information retrieved is independent of keywords and page rank algorithms that improve search accuracy, yielding exact results.
Difference between lexical search and semantic search
Lexical search involves retrieving documents that contain exact matches or variants of the query words without interpreting the overall meaning of the query. In contrast, semantic search processes the query to deliver results that align with the searcher’s intent.
Lexical search
- Focuses on literal matches or variations of the query words.
- May overlook the broader context or intent behind the search query.
Semantic search
- Considers the overall meaning and context of the query.
- Tailors results to match the searcher's intent, enhancing relevance and accuracy.
For example, a search for “shoe store near me” in a semantic search engine yields results for the closest shoe stores, while a lexical search might return stores named ‘Near Me’ or located in a place called ‘Near Me’.
The technology behind semantic search
Semantic search is kind of like a super-smart buddy for your search queries. Instead of just looking for exact words you type in, semantic search tries to figure out what you really mean. It’s like having a conversation where the other person gets the gist of what you’re saying, even if you don’t use the perfect words.
So, how does it do this cool stuff? It uses a mix of clever tech like vector search, artificial intelligence (AI), machine learning (ML), and natural language processing (NLP). Think of AI and ML as the brains that learn from mistakes and get better over time at figuring out what you’re looking for. NLP, on the other hand, is all about making your search box smart enough to really ‘get’ what your products are all about.
In simple terms, semantic search is like a step up from the old-school keyword search. It doesn’t just look at the words; it tries to understand the whole idea behind your search. This means you end up with search results that are spot on, because it’s all about catching the intent and context of what you’re asking. It’s a complex dance of understanding language, knowing how users think, and using some pretty advanced algorithms to bring you the best search results possible.
Let’s take an in-depth look at what else semantic search includes to perform its job.
1. Natural language processing (NLP)
- Tokenization and lemmatization: The search query is broken down into individual elements (tokens) and reduced to their base or dictionary form (lemmatization).
- Part-of-speech tagging: Identifies whether a word is a noun, verb, adjective, etc., to understand the role each word plays in the query.
Dependency parsing: Analyzes the grammatical structure of a sentence, helping to understand how different words in a query relate to each other.
2. Semantic analysis
- Named entity recognition (NER): Identifies and categorizes key information in text into predefined categories like names of people, organizations, locations, etc.
- Sentiment analysis: Determines the sentiment or tone behind a query, which can be particularly important in understanding the user’s intent.
- Word sense disambiguation: Determines the meaning of a word based on its context, crucial for understanding queries with polysemous words (words with multiple meanings).
3. Knowledge graphs and ontologies
- A knowledge graph is a structured representation of facts about entities (like people, places, and things) and their interrelations.
- Ontologies define the types, properties, and interrelationships of the entities in a domain.
These structures enable the system to understand the context and relationships between different concepts.
4. Machine learning and deep learning
- Vector space models: Words and phrases are converted into vectors in a high-dimensional space. Words with similar meanings are closer together in this space.
- Neural networks: Used for understanding complex patterns in data. Transformer-based models like BERT (Bidirectional Encoder Representations from Transformers) are particularly effective in understanding the context of a query.
5. Indexing and retrieval
- Semantic indexing: Unlike traditional indexing which focuses on keywords, semantic indexing involves understanding the context and topics of a document.
- Query expansion: Automatically modifies a query to include synonyms, related terms, or more specific terms based on the semantic understanding of the original query.
6. Ranking algorithms
Uses a combination of semantic relevance and other factors (like page authority, freshness, user engagement) to rank search results.
7. Feedback loops
Continuous learning from user interactions (clicks, time spent on a page, etc.) to refine and improve the accuracy of search results.
Semantic search use cases
Semantic search technology, with its ability to understand the context and meaning behind queries, has numerous applications across different industries. Here are five key use cases:
E-commerce
In the e-commerce sector, semantic search enhances the shopping experience by understanding customer queries in natural language. This means that when a shopper types in something like "comfortable running shoes for marathons," the search engine can interpret the intent and context, showing results that are more tailored to the specific needs of the customer. This level of understanding significantly boosts customer satisfaction and conversion rates.
Customer support and help desk
Semantic search is instrumental in improving the efficiency of customer support services. By understanding the intent behind customer queries, it can quickly direct them to the most relevant answers or resources. This is especially useful in FAQ sections and support forums, where customers can get immediate, accurate responses to their questions.
Healthcare information retrieval
In the healthcare industry, semantic search plays a vital role in sifting through vast amounts of medical literature and patient data. It helps healthcare professionals find relevant information quickly, whether they're looking up treatment options, drug interactions, or the latest research on a particular condition.
Legal and regulatory compliance
The legal field benefits greatly from semantic search in managing and navigating through vast repositories of legal documents, case laws, and regulatory guidelines. Lawyers and legal researchers use semantic search to find relevant case precedents, interpret legal texts, and ensure compliance with various regulations.
Recruitment and HR
In the recruitment sector, semantic search helps in matching job descriptions with suitable candidates. By understanding the nuances of job titles, skills, and experience, it can more accurately align potential candidates with job vacancies, streamlining the recruitment process.
Conclusion
So, is semantic search the right option for you? It can be a game changer for many e-commerce websites, but also other types of websites if applied correctly, but it might also be considered redundant if deployed in the wrong use case. Consider using it if you think it can bring something of value to your business and improve your website’s user satisfaction. Other businesses might benefit the most from solutions like Luigi’s Box since they provide an affordable solution for e-commerce product search & discovery.
Frequently asked questions
What is semantic search?
A semantic search engine tries to understand the intent of the user and the contextual meaning of a query to deliver results that match what users are looking for.
Semantic search technology knows the different ways a concept can be expressed and in what context a term is used. It uses this knowledge to help you find more relevant content faster.
Where is semantic search used?
People use different ways, languages, and tones to look for a product or content. Moreover, search queries can be ambiguous in nature. Semantic search is used to understand the relationships between words. It works by drawing links between words and phrases.
This way, it is able to interpret digital content in a more ‘human’ language. When that’s achieved, it can offer the searcher more personalized and accurate search results. Today, many industries are using semantic search, such as e-commerce, entertainment, streaming media, and more.
How does semantic search improve the e-commerce shopping experience?
Semantic search enhances e-commerce by interpreting customer queries contextually, providing relevant product suggestions. It goes beyond keywords to understand the intent and nuances of search terms, thereby improving customer satisfaction and potentially increasing sales.
What are the challenges addressed by semantic search in e-commerce?
Semantic search addresses challenges like query ambiguity and the diversity of language in e-commerce. It deciphers the varied ways customers express their needs, ensuring that searches yield relevant and precise results, even for complex or vague queries. This leads to a more efficient and user-friendly shopping experience.
Alex is a wordsmith for Luigi's Box where he works as a product marketing specialist. He used to work as a graphic designer while getting his degree in Media Communication. His other interests include photography, reading, art, philosophy, and psychology. Besides being a part of the Luigi's Box team, he does video translations for the Art You Can Eat video portal about contemporary artists from Slovakia.
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