Semantic Suggestion

New Search “Signage” Fills the Gap Between Now and Later

On 9/28 the Hakia blog post stirred the pot of interesting semantic web issues–my essential takeway: are we at a point in which we are technologically mature enough to handle SW, or are our behaviors and satisfactions attached to technology (current search engines) too rooted to the Now?

Dr. Berkan proposes that SW is not an “if” but a “when” an undeniable force in the evolution of search.

This got me thinking about a couple current search tools that offer a wide range of semantic suggestion, search add-ons that point in the direction of SW:

  • Keyword search suggestion has been offering syntactic, sometimes clumsy semantic suggestions for awhile now–you start typing a word in the search box and a drop down menu offers increasingly granular suggestions with each passing keystroke–can be quite distracting, but seriously indicative of the wild assortment of search terms people use. Maybe we will reminisce about it someday–it’ll become an ancient and primitive syntactic tool–like cave hieroglyphics (though some would likely argue for the sophistication of hieroglyphics).
  • Answer Tips, from I just experienced a webpage enabled with Answer Tips. I can double click on any word on the page and get a spam-free pop-up window from that defines, describes, and otherwise offers a range of encyclopedic and dictionary reference–for ANY WORD ON THE PAGE.

Answer Tip window

  • Hakia’s ScoopBar makes it possible to semantically manage data. It involves downloading to the Firefox browser, but promises highlighted webpage text, and the Scoop tool allows users to save results in offline folders accessible later. This is a bit like Google Notebooks, but in an offline format. (I have not used ScoopBar yet, and I’m curious b/c I’ve tried G Notebooks on a couple occasions to see if I could find a more convenient and intuitive way to corral a lot of research data. But the task has just seemed more cumbersome than convenient.) So ScoopBar semantically manages, semantic results.
  • Google Maps allow users to personalize–overlay data in various formats that can be accessed via place markers. Click or mouseover a map placemarker and a pop-up data window opens like a new stratosphere of semantic information relative to a geographic location. G Maps can be made public, usable by the wider web search audience.

One syntactic foot in front of the semantic other.

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