Why Search Is Broken, and How Semantic Search Fixes It

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Why Search Is Broken, and How Semantic Search Fixes It

A human gets information by asking a question to get an answer, but online, we’ve been forced to learn “keyword” searches.

The thinking was that we could extract meaning from several abstract words (aka keywords) most closely related to what we were seeking. The problem is that this does not work in getting a real answer. As we’ve started to see with new developments in search techniques from EyePlorer, Wolfram Alpha, and even Twitter’s value in search, the market is aggressively looking for a more meaningful approach. To boil it down, the major issues with the keyword search model are:

  1. Results are not completely relevant to the original query
  2. Lack of accuracy leads to an overabundance of results
  3. Too time consuming to comb through that much information

Let’s look at an example of how search works today. Search for “best cat food,” and you’ll get more than 93 million pages including these keywords, prioritized using the secret sauce of the search engine.

search broken

While looking over the universe of information around best cat food, I wonder:

  1. Have I figured out what information is out there? Am I able to match the resulting content to a user’s request?
  2. Did I really need 93 million results, or just the right information? Is there an accuracy issue?
  3. If I have to then analyze even 1% of the information, is there a lack of understanding in what I’m looking for?

We should not have to do a “search”… on our search results.

In order to understand what a user is really looking for, we need to use a system where you ask real questions to get real answers. We need to use commonsense reasoning, looking at natural language processing and the inflection and semantics. If a system looks at all of the information available and digests its full meaning, it can take that semantic understanding and match it to the users’ meaning to produce results that make sense – the first time.

In addition to new companies, several university projects are taking on this concept as well, like the University of Maryland’s SHOE Search Engine and University of Maryland-Baltimore Campus’ Swoogle. However, both are still a ways off from producing the true meaningful experience that the user will come to trust.

Until we fix the core – the true understanding behind why and how we search – we’re left with 93 million links about cat food.

As the Chief Technology Officer for, produced by Saber Seven, Jim Anderson leads the company’s search, content delivery, social and mobile strategies, as well as technological vision. The mixture of his experience, with leading advances in Artificial Intelligence, Natural Language Processing, Machine Learning Technologies, and his hundreds of patents have all been fertile training for his new challenge – creating as a premiere web destination, helping users to find real answers to their searches.


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