Let’s take the airline industry, where by using Natural Language Search, we should easily be able to find answers like these ones:

Q: can I bring my dobermann onboard

A: I've found information about Carriage of domestic pets

Q: do you serve vegetarian food in the plane

A: I've found information about Special on-board meals

Q: can I buy a ticket for somebody else

A: How can I buy a ticket for another person?

Once the appropriate knowledge base has been put in place, and the right Semantic Search Engine is made available to the users, the whole website is transformed in a real online customer support portal.

However, not all the questions are easily found in most of the knowledge bases. Let’s take these examples:

• is the strike affecting flights to Paris?
• are there volcano ashes on Madrid airport?
• open job opportunities

What do these questions have in common? All of them refer to issues that are tied to real-time events.

Having a knowledge base, or even a website, permanently updated with these issues is simply impractical.

Yet, most companies are offering valuable information related to real-time events through Twitter, which provides the flexibility to basically “broadcast” valuable last-minute information in real-time, without the hassle of having to update the company’s website, which is a process that may take hours (often days) to take effect.

Let’s take a look at these tweets that have been recently published:

• The list of long-haul flights affected by the SEPLA pilots' union strike on Jan. 9 & 11 is available at http://ow.ly/8gGPJ.
• Good morning! The volcanic ash cloud is still over Spain. Right now 8 airports are closed
• Iberia Express Opens Pilot Recruitment Process http://ow.ly/87pNa #IbExpress #Job #Employment

This is valuable information that any passenger about to fly would be interested in knowing indeed. Yet, to have this information, users just need to follow that airline Twitter timeline and pay attention to what is going on.

But, what if I miss the tweet that was relevant to me? What if I am not following that company on Twitter? What if I don’t even have a Twitter account? I would simply miss this information.

The solution is basically to consider that the company’s Twitter Timeline is actually an intrinsic part of the the knowledge base, and the natural language search has a key role here.

By doing so, questions about real-time events could still be answered, online, using the current company website, by feeding the knowledge base with all this valuable information.

Consider now the following question/answer dialog:

Q: is the strike affecting flights to Paris?

A: The list of long-haul flights affected by the SEPLA pilots' union strike on Jan. 9 & 11 is available at http://ow.ly/8gGPJ. /1 (source: Twitter Timeline)

Q: are there volcano ashes on Madrid airport?

A: Good morning! The volcanic ash cloud is still over Spain. Right now 8 airports are closed (source: Twitter Timeline)

Q: open job opportunities

A: Iberia Express Opens Pilot Recruitment Process http://ow.ly/87pNa #IbExpress #Job #Employment (source: Twitter Timeline)

The answers are absolutely relevant. There are no identical words between the question and the answer, but the meaning is very similar. This is thanks to the magic behind Natural Language Processing and Semantic Search.

Besides, implementing this feature is easy. The only requirements are:

• Reading the Twitter Timeline, all informative tweets published by the company and
• Having a semantic search engine armed with a real-time indexer

As companies are looking at social media more and more as an effective way to provide customer service, we believe that real-time indexing and Natural Language processing will have an increased role and importance in many industries that have to deal with real-time events.

Because your customers just can’t wait.

Jordi Torras // CEO Inbenta // http://twitter.com/@jtorras