Finding the Right Data Structure for Knowledge Representation

The most commonly used data structure today is the Row-based data structure where in one row represents details about an instance of an entity type. But in real-world the representation of an entity is not a flat structured. An object's representation contains several sub-attributes that those sub-attributes may have their own sub-attributes that make up the entire object (its attributes). But if we go by the row-based representation of the object we cannot represent the sub-attribute and their relation with the main object.

Consider an example where a Person has FirstName, Surname, Home Address (Street, Suburb, State, Post Code) and Work Address (Company Name, Street, Suburb, State, Post Code). If we use a row-based representation here then we find that our records look like this :

FirstName, Surname, HomeStreet, HomeState, HomePostCode, CompanyName, WorkStreet, WorkSuburb, WorkState, WorkPostCode.

The limitations here is unless we uniquely name the Street, PostCode, State attributes for both Home and Work Address we will not be able to distinguish their real meaning. On the other hand consider a Structure like this:
Person
  - Name
    - First Name
    - SurName
  - Home Address
    - Street
    - Suburb
    - PostCode
    - State
  - Work Address
    - Street
    - Suburb
    - State
    - Post Code

By looking at this structure we can easily tell that the home Address is made up of 4 attributes and Work address is made up of 5 sub-attributes. These in-turn can have their sub attributes as well that will define them in more detail.

It is evident that the hierarchical data structure provides more flexibility and room to grow than the flat row-based structure for representing a real-world object.

Until Next Time...!!!

How do we achieve Artificial Intelligence?

Artificial Intelligence, I am sure many readers are familiar with this buzzword created by research groups around the world not so long ago. Where we started dreaming about many things a machine can do that we do in our day to day life and will in turn make our life simple and easy. But what happened to most of those projects, they are either shelved or have very limited usability in our day-to-day life. Though there are few outcomes that we did find useful.

When I read about the artificial intelligence and where it went wrong, I ask a question as what went wrong? Where did it all go wrong?

Lets define the intelligence. The intelligence is art of making best choices based on what we know (or rather don't know). But what determines whether we know something or not. It is our ability to recall something we learnt in past. Learning is associating facts to a context. Context define how the entities are being linked together. The linking does not have to be static.

So in a nutshell, in order to build a system that can:

• Understand the context in which a particular fact is stated.
• Retrieve the most appropriate rule that can be applied to the available facts i.e. show some sort of intelligent behavior.
• The retrieve operation depends on how the raw data is structured.

In my opinion it all comes down to how the data is structured (represented) and the reasoning mechanism that works on the data.

Until Next Time..

Starting Point for Semantic Search

In my previous post on Semantic Search, I discussed about what is Semantic Search in general. One of the ideas that is revolving around is how to make the search efficient.

At an average it takes 3 google searches for someone to find what they are looking for. This is mainly because the google search engine has to scan their index table and it brings out all the documents that matches the keyword, of course ranked by the google page ranking algorithm also known as PigeonRank.

But if we keep the technology aside, then there are two possible ways we start searching for something.

• When we know what we are looking for. This is the simple and straightforward case where we are very well aware of our needs and we often get result faster.
• But there is this other situation when we don't know what we are looking for. We just have knowledge about few attributes, characteristics of the object we are searching.

A Semantic Search will have to operate taking both into account. The search engine will have to rank the results based on the criteria matching.

Until Next Time....!!!

Information and Knowledge

Does Information mean Knowledge or vice-versa? This is often the topic of discussion when I happen to talk about knowledge and its role in Semantic Web. More often than not we confuse information with knowledge.

So what is information? It is the smallest detail we have (fact in the system) about an entity. Today is Monday is an information, But Monday is first day of work week is not. In western countries first day of week is Monday but that is not true for middle-east, their week begins on Sunday.

So how do we define knowledge? Knowledge is the interpretation of information. In our previous example, Monday is the first day of the week or not depends in which country's context we are discussing Monday. We can define knowledge as Information related to a context. If not attached to a context, the information does not convey any meaning and that means the information is of no use hence cannot be classified as knowledge. So for an information to be classified as knowledge we must have the context attached to it.

Until Next Time...!!!

What is Semantic Search

If I ask this question in a group of 10 researchers, I will have 11 correct answers of this (including one of mine) for sure. But the question will remain unanswered as what exactly is "Semantic Search"?

During Semantic Technology Conference 2009, this topic was debated among search biggies as what exactly is next direction of search ? One thing that came out of the discussion is page ranking and keyword search is definitely NOT the way the searches are going to work in future. One of the most probable future for search engine is "It will be more like a conversation with the user".

Currently the search engine work in "Tell and show the result" mode. The user enters the keyword and the search engine dumps all possible matches (based on page rank, keyword matches etc) to the user. Most of the time the results do not make any relavance to the search intention at all. A conversation style of search seems to be more appropriate where the user refines their search criteria with continuous interaction with computer. This resembles more of how we search information in our day-to-day life. The search session will be more like a brainstorming where the user will feed more and more information about what they know about what they want. The system will then return a set of most relavant searches to the user. The user then will add more details of what they want and the steps will be repeated till the user finds what they want. At any point in time, they would also like to go back and start from scratch.

One of the positive step in this area is Semanti search engine. This search engine provides a list of suggested categories where a particular keyword is associated. This is just the starting point for semantic search. The semantic search engine should be able to let the user feed more (unlimited) details of what they know about what they are looking for. These details (provided by user) could be directly related to the result they are looking for or it might not describe the result at all. Knowledge reasoning is a major influencer in the search process. Reasoning is the core of the semantic search process and will determine how accurate the search engine is. While the search engine is designed for accuracy, the efficiency will have to be compromised at least for a while. One of the possible scenario these search engines will have to handle is percentage accuracy of result based on the search criteria. The more matching result should be displayed on top of the list.

So what is your idea of Semantic Search?

Until Next Time...!!!