Today more than ever, enterprises are dealing with fast-growing volumes of data. Trying to find meaningful answers in all that data is becoming a greater and greater challenge. It's no secret that we need a more thoughtful way to handle enterprise search.
For many enterprises, the solution appears to mean investing in advanced search capabilities. But the most common approach is to expand basic search by adding on advanced features. Over the long term, that approach has built-in limitations. Eventually, you're bound to run up against the law of diminishing returns.
To prepare for the intelligent search needs of the future, we need to rethink how we approach search. We need to let go of our preconceptions of how search has worked in the past and look at what users need today.
The terms "advanced search" and "advanced search engine" are sometimes used interchangeably. On the surface, they appear to be similar concepts. But dig a little deeper and it's easy to see fundamental differences.
Advanced search builds on the same idea as simple search software. In essence, you type a keyword or query and you get a list of answers. Advanced search expands on that same concept. It allows users to input additional criteria. For example, the user might specify a date range, location, author, or category. This narrows down the search results to provide more relevant answers.
An advanced search engine, on the other hand, is a leap far beyond the concepts of keywords and criteria. The user gets a UI with higher-level features that make search more convenient and more effective in every way. This may happen in particular because it utilizes natural language processing, query intent detection, and other artificial intelligence capabilities.
Today, most applications have an embedded search engine that operates on pure keyword search. Some applications have more elaborate and more sophisticated federated search engines. But most of them still use keywords.
Advanced search engines handle queries differently. They use natural language processing, which is an element of artificial intelligence. This allows the search engine to process written text and spoken words the way a human being does.
With NLP, a user can type in a query using natural language, including plurals, pronouns, or verbs in any form. This makes the search process more flexible. It's smarter and more intuitive than applications that rely strictly on keywords.
Since natural language processing happens internally, you can't directly observe it in action. But you can see that the search engine takes into account what you type and extrapolates from it. Because of that, you see higher quality results and get a better user experience than you would by just typing in keywords.
It's important to note that natural language processing tells the search engine what to search for. But it doesn't actually give the search engine an understanding of the user's intention. For that, we need to go a step further into something called query intent detection.
Advanced search engines are more effective because they try to understand the user's motivation. They look beyond the literal words used in the query. This process of query intent detection helps the search engine see what the user really meant to say, rather than what they said.
Is the user looking for an overview of the subject across many documents? Are they looking for a specific document? Or are they looking for very precise information inside a document?
Here's a simple example. If you use a document search engine to look for the latest corporate presentation, it's clear that you are trying to find a PPT or PowerPoint document. An advanced search engine understands the intention, even if you don't include "PPT" in your query.
In other words, an advanced search engine knows what the user wants even if they didn't specifically ask for it.
Here's another example. Let's say a user asks: "Who could help me understand machine learning?" The unspoken intent behind that query is that the user is searching for a person, not a document. They're looking for the name of a person who has expertise in machine learning.
Because the advanced search engine can understand the user's intent, it can show them a list of people with the right expertise.
In some cases, it can even anticipate what the user is really looking for before they finish typing a query. That's when we get into the realm of auto suggest.
You've already experienced auto suggest on the internet. You start typing a word, and the system automatically suggests different possible queries.
This advanced capability seems simple on the surface, but what happens behind the scenes is very complex. The suggestion may come from a wide variety of features. These could include dictionaries, previous queries, existing titles, your own preferences, or even compound words extracted from the text you're typing.
Although there are many different sources to populate or suggest queries, the result is the same. You type a few characters, and the advanced search engine suggests the most relevant query for you. Sometimes it can even give you a direct answer before you finish asking.
Advanced search engines also use more sophisticated versions of common features such as spelling correction and phonetic search. These features all work together to provide users with more flexibility, a more effortless experience, and better search results.
While today's advanced search engines are moving beyond keywords and forms, that's just the beginning. Tomorrow's advanced search engines will build on new types of user interactions such as speech to text, voice assistants, bots, and more.
For example, think about what happens when you look for a restaurant on Google maps. You might not type in any keywords. You might just click on the map, zoom in, and look around. What you're doing at that moment is actually a form of advanced search. Instead of mining text, you're mining a map. But in the end, you are still seeking a useful answer to your query.
Search engines will continue to grow and evolve to be even more innovative. The user interfaces of tomorrow may be dramatically different from what you've used in the past. But effective search engines will always be designed with the user's needs in mind.
Advanced search engines help enterprises boost employee productivity, build customer relationships, reduce risk and redundancy, and speed up innovation. They help users search across millions of documents and other sources instantly. Using elements of artificial intelligence such as natural language processing and query intent detection, they give users the answers they need faster and easier than ever before. And they give enterprises the leverage to excel in a highly competitive world of ever-growing data.
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