Hakia and Semantic Search
Hakia is a semantic search technology company.
The mission of hakia is to deploy semantic search solutions to meet the challenges of elevated user expectations, business efficiency, and lowest cost.
hakia was founded in 2004 and is funded by private institutional and angel investors. Headquartered in Manhattan, New York, hakia has offices and datacenters in Silicon Valley, Maryland, and Tennessee. They use some seious sources like pubmed.gov.
As described by Copeland Communications in google-search-has-hakia-competition , “for 2 years nows Hakia has been providing search results based on meaning-match, or semantics, as opposed to Google’s popularity of search terms or keyword matches.
The company has spent millions and built two trademarked tools, QDEX (Query Detection and Extraction) and SemanticRank Algorithm, which use fuzzy logic, computational linguistics and math to allow the engine to perform these semantic analyses of Web pages and arrive at meaning-based search results.
This past year Hakia went through a refresh changing its interface but more importantly the search capabilities and the way the results are presented. ….You are searching in a true semantic fashion through Qdexing (coined by Hakia).”
Also, “for short queries, like a word or two, Hakia categorizes your results into Web, Galleries, Credible sources, Pubmed, News, Blogs, Twitter, Wikipedia, Images, and Videos.” Something link insight is doing inside wordpress.
According to the article, “you would have to run as many individual searches in traditional indexing search to arrive at such a volume of categorized results. So it’s not just different, it’s faster.”
Semantic Search, from Hakia
1- Handling morphological variations
A semantic search engine is expected to handle all morphological variations (like tenses, plurals, etc.) on a consistent basis. In other words, the results should not change whether you type “improve, improves, improving, improved, improvement”.
2- Handling synonyms with correct senses
A semantic search engine is expected to handle synonyms (cure, heal, treat,.. etc.) in the right context and with correct word senses. For example, the word “treat” can mean doing social favors as in trick and treat, which would not be correct in the medical sense.
3- Handling generalizations
A semantic search engine is expected to handle generalizations (disease = GERD, ALS, AIDS, etc.) where the user’s query is expressed in generalized form and the result is expected to be specific.
4- Handling concept matching
Perhaps the most challenging functionality among all, a semantic search engine is expected to recognize concepts and bring relevant results. Usually, the depth of this capability is increased in verticals of operation, and it would be unrealistic to expect coverage in all subjects under the sun.
5- Handling knowledge matching
Very similar to the previous item, a semantic search engine is expected to have embedded knowledge and use it to bring relevant results (swine flu = H1N1, flu=influenza.) Knowledge match and concept match are similar in principle, yet different in practice in the way the capability is acquired.
6- Handling natural language queries and questions
A semantic search engine is expected to respond sensibly when the query is in a question form (what, where, how, why, etc.) Note that a “search engine” is different than a “question answering” system. Search engine’s main task is to rank search results in the most logical and relevant manner whereas a question answering system may produce a single extracted entity.
7- Ability to point to uninterrupted paragraph and the most relevant sentence
Unlike conventional search engines where a query points to documents, semantic search is expected to do much better. A query must point not only to documents but also to relevant sections of them. This eliminates 2nd search where the user is supposed to open the documents to find the relevant sections.
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