The Question Asked

What Does Hakia Have that Google Does Not and What Does Google Have that Hakia Does Not?

Google’s Basics of Search tips say that words like who, what, and how are summarily dropped from Google search queries simply because this is how keyword-centric engines operate. I now know this is occasionally the reason for weak and untargeted results that send me clicking on over to to give the semantic search engine a drive. But maybe this is exactly what I’ll do the rest of my search years. There are instances in which Google returns more satisfying results and vice versa, so which search engine is better? Maybe it’s exactly in the question asked.

Who, what, how, and why questions clearly get more specific results with Hakia, but not always enough to fully answer my question and some that have left me with nothing, still.

My simplistic question posed to both, who defines a minority student? clearly illustrates the divergent results. Google is able to return a couple of results that happen to directly reflect my query with the phrase define minority students, but without any direct association with a who.

Hakia, on the other hand specifically returns results, highlighted, too, but specifically associated with the who part of the query.

Proprietary Processes Hakia Boasts

A deeper dip into Hakia reveals a bit of the proprietary processes on which this search engine is built:

OntoSem, or Ontological Semantic parser is “a linguistic theory of meaning in natural language.” OntoSem maintains a highly developed “language-independent ontology of thousands of interrelated concepts; an ontology-based English lexicon of 100,000 word senses, and counting (plus, the lexicons for several other languages under construction); and an ontological parser which ‘translates’ every sentence of the text into its text meaning representation, approximating the complete understanding of the sentence by the native speaker.”

QDEX, or Query Detection and Extraction, is an does a thorough “decomposition” of the WWW prior to any search queries being posited and stores all its possible queries waiting for a user to ask some semantic twist of its data. “The critical point in QDEX system is to be able to decompose sentences into a handful of meaningful sequences without getting lost in the combinatory explosion space.” QDEX interfaces with OntoSem in the miasma of semantic meaning. OntoSem is able to determine which of the billions of semantic options are most meaningful and worthy of indexing.

Hakia’s QDEX

Semantic Rank, if it sounds similar to Google’s Page Rank, the similarity stops there. While Google is very good at determining the authority (may not indicate relevancy) of a webpage based on linking strategies, Hakia and semantic search engines have no such algorithmic variables. Semantic Rank then ranks results by pure meaning, “based on advanced sentence analysis and concept match between the query and the best sentence of each paragraph.

Hakia SemRank

Petaflop Supercomputer: What Big Questions Will it Solve?

Thanks to a big OOPS, the world is now privy to pretty specific info on the IBM-National Science Foundation deal–the one where the NSF grants IBM the right to build the world’s biggest supercomputer. Well, IBM currently holds the record with its Blue Gene/L. The next-gen super-duper will apparently take over with the larger (read “more important/costly”) condundrums of the moment. Already, according to a NYT article, there’s a line at this oracle’s gate. Magic 8-Ball….

Perhaps….Not Likely…..Ask Again Later……

BTW: define: petaflop-“one thousand trillion mathematical operations a second.” NYT, IBM Near Supercomputer Contract