Semantic and contextualized search is an important area which will impact our ability to quickly do “intelligent” searches around the web. As the information on web has grown – one came to realize that a vast amount of it was lying there with no context in the direct sense. When such information is searched just for key words, then one could get results that aren’t always useful or effective. But if one could “attach” context to the information, which is relevant to the person searching, suddenly the results returned have a far greater value.
For example, if I am searching for “Energy” in a scientific context and want to know its formulas or science behind it, Google’s search results may not be the most efficient. So in came Wolfram Search. It contextualizes search words – by asking you if you want to search it as a ‘Word”, formula or a physical quantity. The results are different and useful for the context they are used by the person searching. Compared to Google search this goes several notches higher in terms of search technology.
Some of the areas of Wolfram search include:
Mathematics » Elementary Math Numbers Plotting Algebra Matrices Calculus Geometry Trigonometry Discrete Math Number Theory Applied Math Logic Functions
Statistics & Data Analysis » Descriptive Statistics Regression Statistical Distributions Probability
Physics » Mechanics Electricity & Magnetism Optics Relativity Nuclear Physics Quantum Physics Particle Physics Statistical Physics Astrophysics Physical Constants
Chemistry » Elements Compounds Ions Quantities Solutions Reactions Chemical Thermodynamics
Materials » Alloys Minerals Plastics Woods Bulk Materials …
Engineering » Acoustics Aeronautics Electric Circuits Fluid Mechanics Steam Tables Structures
Astronomy » Star Charts Astronomical Events Planets Moons Minor Planets Comets Space Weather Stars Pulsars Galaxies Star Clusters Nebulae Astrophysics .
Food & Nutrition » Foods Dietary References
Words & Linguistics » Dictionary Lookup Anagrams Word Puzzles Morse Code Soundex Languages Number Names …
Culture & Media » Books Periodicals Movies Fictional Characters Television Networks Songs Awards …
People & History » People Genealogy Names Occupations Political Leaders Historical Events Historical Periods Historical Countries Historical Numerals Historical Money
Education »Universities Standardized Tests …
Organizations » Universities Companies Hospitals Foundations International Organizations …
Sports & Games » Football Baseball Olympics Lotteries Card Games …
Music » Musical Notes Intervals Chords Scales Songs …
Colors » Color Names Color Addition Color Systems Temperatures Wavelengths
There is another experiment in contextual search by ZCubes.com – where they have layered the Google search (and other search engines) to serve contextually relevant search results. Depending on who you are and what you are really looking for, you can get different search results.
Now, Yahoo has entered this world of intelligent searches via an experimental project called Time Explorer. This project is to be in conjunction with the broader Livingknowledge initiative. The site officially explains it like this:
The application will be eventually be a showcase for the functionality of the LivingKnowledge project. This current version is designed to demonstrate the current state of the project using the NYT collection as part of the HCIR challenge
What this experiment primarily does is to contextualize news items by time. Time, they believe, is an important element and if a news story can be followed across time then a better context emerges. It can also be used to predict the future. So, one goes into the past and then uses it to make future predictions. That, is what this new type of search engine tries to do.
In current news search engines, time is primarily used to boost the relevance of the most recent stories. While useful when users are interested in the latest news, it may hinder the search experience of those interested in a broader understanding of a particular news story. These users may benefit from a transversal organization of the topic across time so as to better view how the story has evolved and which people and places have shaped the evolution. Furthermore, these users may equally benefit from predictions on how the story might evolve into the future. When searching about a regional conflict, for example, a user should be able to identify what factors lead to the conflict, which people where most influential and when, and how the conflict is likely to evolve in the future.
Search Results are shown on a timeline scale which stretches several years back and forth. To use it, just move your mouse over the future timeline scale, and you can view the predictions for what was supposed to happen in that year given the past occurrences from as far back as 20 years ago.
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