Semantic Search Engine

The challenge with search engines

Today, search engines have evolved from simply understanding keywords to understanding the context—or semantics—behind search intent and behaviour. This is because natural language, i.e. language that humans use, is full of complications.

For instance, homonyms necessitate search engines to look at the context of the question, so it understands that “Local bands from Singapore” refers to music, not their fabric or latex counterparts. They must also be smart enough to understand synonyms, abbreviations and acronyms. This means that solutions need to pull out resources on ‘Artificial intelligence’ for a search query on ‘AI’.

What if a question has multiple layers? For example, asking “How old was Freddie Mercury when he joined Queen?” The search engine would have to pick apart the query, find the relevant facts for each hidden question, and piece together an answer.

There are infinite iterations of a search query, but also different variables behind a single ask. This is why asking questions in a search bar, just like how one would to a human, poses an immense challenge for conventional search engines. With such ambiguity, search engines must be able to understand the relationships between words, and the user intent behind the words present in the search query, to then pull out an appropriate response.

What is semantic search?

Semantic search denotes search with meaning, as distinguished from lexical search where the search engine looks for literal matches of the query words or variants of them, without understanding the overall meaning of the query.

Features of semantic search?

Semantic search engines are able to

  • Use natural language processing to understand the user intent and contextual meaning behind each query
  • Build and refer to a domain ontology that lists out all the concepts related to that domain along with the relation
  • Crawl and classify unstructured information from a repository

Why semantic search matters for enterprise search engines

Big data is growing at an alarming rate, with the amount of data growing by almost 5,000% in the past decade from 2010 to 2020, and now predicted to hit 181 zettabytes by 2025. All this data holds powerful insights and value—only if it’s actually usable. That’s only possible with cleaning, organising, indexing, processing and connecting massive pools of data semantically, a challenge that search engines are taking on.

Being able to enhance search accuracy by understanding the intent and context behind each query is essential, particularly in the workplace. Unlike public web search engines, enterprise search engines need to ingest a broader set of data beyond the capability of web crawlers. This becomes a matter of what connectors a search engine has to effectively ingest and index data, across domains and resource types, and within stricter security considerations.

Enterprise search engines also hold a larger stake, with valuable time wasted whenever staff play a guessing game with every search reattempt. And worse still, if there are ‘unknown unknowns’, i.e. if they need to search for something they’re unable to name, forcing many to manually parse through documents and repositories hoping for a chance at information discovery.

When semantically powered, enterprise search engines have an immense potential in breaking down ever-expanding data silos, from indexing and ingesting data, to understanding queries and relating contents. With this better data-connectivity comes greater decision-making and drastic improvements in workplace productivity. 

Semantic search use cases and applications:

  • Enterprise or intranet search
  • Content management
  • Records management
  • E-commerce search
  • Know-your-customer (KYC) screening
  • Recommendation system

What is Omnitive Search?

Omnitive Search is TAIGER’s semantic enterprise search engine tool. It utilises the meaning of information to provide more accurate search results, understand user queries, relate contents, expand searches and to retrieve the corresponding knowledge more efficiently and precisely.

At a glance: features of Omnitive Search

  • Keyword Search
    • Performs a basic search on terms or keywords.
  • Predictive Search
    • Also known as “search as you type” or “auto-complete”, suggests relevant terms to complete a search query or sentence.
  • Synonyms Search
    • Expands the search using common synonyms of the searched terms.
  • Boolean Search
    • Boolean search allows you to combine terms with operators such as AND, NOT, and OR. When searching terms using operators, it narrows down searches and produces more relevant results.
  • Syntax Search
    • Allows search using a search syntax language.
  • Cross-Language Search
    • Supports multi-lingual information access as it retrieves the searched term regardless of the annotation language and the one used in the search query.
  • Knowledge Graph Search
    • Displays ontology representation of concepts and their relations based on the search result.
  • Saved Search
    • Allows searches to be saved and re-executed from the list of saved searches

Suggested search

These features allow you to conduct search in natural language, correct spelling errors and receive suggestions.

  • Natural Language Search
    • Allows search query terms in natural language. Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret and manipulate human language.
  • Spell Correction
    • Displays orthographically correct sentences when the user mistypes. Spell correction supports, by default, up to 2 misspelled characters.
  • Concept Suggestion
    • Displays similar concepts to the search query terms or keywords.

‌Refining search results

These features allow you to refine and narrow down your search results.

  • Search Within
    • Narrow down searches by typing the searched query term that must be in the result or accept search from the suggested search term.
  • Search Filters
    • After performing a search query, results can be narrowed down by selecting filter items displayed in the Filters panel on the right. For example, country, topics, document format, or modified date. Once the filter is open, a list of terms within that category including a numeric indicator showing the number of times certain terms are mentioned appears.
  • Multi-Source Search
    • Display results for the searched query term according to the selected sources. You may choose individual sources to show only the applicable results or all sources to display all results related to the searched query.
  • Sort Results
    • Sort results by relevance, latest or earliest first.

Read our Omnitive Search product documentation here → 

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