Daniel Newman of Forbes recently wrote, “Businesses today are largely online and they have in droves taken their web presence from where it was a few years ago which was likely an “Online Brochure” to some type of second generation website that considers trends such as social media, content marketing, and of course search engine optimization. The reason we as business owners do all of this isn’t because we love technology (not all of us, at least), but rather because we know that people are doing more and more of their research about what they want to buy, and who from, online.” Read more
Posts Tagged ‘semantic search’
Aviva Rutkin of New Scientist recently wrote, “Rumours have been circulating that the Chinese search engine is developing a bike that could drive itself through packed city streets. The project isn’t ready to be launched yet but Baidu confirmed it is exploring the idea.The news is intriguing, and not just because self-navigating bikes would be cool. Research into autonomous vehicles is yet another way that Baidu is following Google’s model of pushing at the boundaries of artificial intelligence.” Read more
Senzari Unveils MusicGraph.ai at the GraphLab Conference 2014, Showcasing the Music Industry’s First Graph Analytics and Intelligence Engine
SAN FRANCISCO–(BUSINESS WIRE)–Today Senzari® introduced MusicGraph.ai, the first web-based graph analytics and intelligence engine for the music industry at the GraphLab Conference 2014, the annual gathering of leading data scientists and machine learning experts. MusicGraph.ai will serve as the primary dashboard for MusicGraph®, where API clients will be able to view detailed reports on their API usage and manage their account. More importantly, through this dashboard, they will also be able to access a comprehensive library of algorithms to extract even more value from the world’s most extensive repository of music data.
“We believe that in MusicGraph’s first iteration we democratized music data access, and now, with MusicGraph.ai, we are democratizing music knowledge. Most impressively, our team was able to make this leap possible in just months, not years.” Read more
AlphaSense’s Advanced Linguistics Search Engine Could Buy Back Time For Financial Analysts To Do More In-Depth Research
When Raj Neervannan, CTO and co-founder of financial search engine company AlphaSense, thinks about search, he thinks about it “as a killer app that is only growing…..People want answers, not noise. They want to ask more intelligent questions and get to the next level of computer-aided intelligence.”
For AlphaSense’s customers – analysts at large investment firms and banks or any other industry, as well as one-person shops – that means search needs to get them out of ferreting through piles of research docs for the nuggets of information they really need. Neervannan knows the pain of trying to interpret a CEO’s commentary to understand what he or she was really saying when making the point that numbers were going down when referring to inventory turns. (Jack Kokko, former analyst at Morgan Stanley, is AlphaSense’s other co-founder.)
“You are essentially digging through sets of documents [using keyword search], finding locations of terms, pulling them in piece by piece and constructing a case as to what the company’s inventory turn was really like – what other companies’ similar information was, how that matches up. You have to do quantitative analysis and benchmarks, and it can take weeks,” he says.
Megan Williams of Business Solutions recently wrote, “It’s a simple fact of the current state of healthcare that most providers are not using the data they gather via EHRs as best they could. The case of missed technological opportunities is nothing new to healthcare, but in the case of search engines, not employing them to mine existing information could be costing your clients, and their patients, unnecessary tests, wasted time, and missed information that would have been helpful in achieving desired patient outcomes.”
Williams goes on, “A study published by the Journal Of The American College Of Radiology titled “Optimizing Emergency Department Imaging Utilization Through Advanced Health Record Technology” addresses the application of search functions to clinical environments, specifically the emergency department (ED). This department presents special challenges because of the nature of the work involving evaluating complex patients while under time pressure. These challenges are made worse by the incomplete medical history that typically comes with patients who enter the department.”
She adds, “The study covers QPID, which is a programmable health record intelligence system that adds semantic search and knowledge management layers to an EHR system. QPID works as an extension of its data repository, facilitating extraction from it. While most EHR systems do include databases that handle rudimentary data retrieval, QPID allows users to pull data by topic-related ‘packages’ of data and concepts in saved, operable queries that can be used on both structured and unstructured data sources.”
Image: Courtesy Flickr/ Yann Ropars
Frederick Vallaeys of Search Engine Land recently wrote, “By late August, Product Listing campaigns for AdWords will be retired in favor of Shopping campaigns, so if you haven’t started migrating, don’t delay much longer. You can run both campaign types simultaneously, so start tweaking Shopping campaigns now so that they’ll be performing great by the time PLAs go away later this summer… Unlike with Search ads which are entirely managed in AdWords, a lot of the settings for Shopping ads are handled outside of the AdWords interface. They get their titles, images, descriptions and promotions from feeds in the Google Merchant Center. While you can use the AdWords interface to set bids, structure campaigns and set up product groups, you will need to work with your product feed if you want to have ads appear for different keywords. How to manipulate the feed depends on its size and how it is generated.” Read more
Dave Lloyd of ClickZ recently wrote, “2014 has been heralded the year of content marketing. At the same time, we’re optimizing our search marketing practices for the semantic search environment. Together, there’s a need to merge the two different objectives into a unified strategy. From a search marketing perspective, it makes sense to integrate content marketing and semantic search optimization practices. The introduction of Hummingbird has taught us to deploy search optimization strategies that contextualize queries. Digital marketing with content, on the other hand, is deployed to drive traffic and engage prospects. You can see where the two might combine to form a natural single-track strategy, right? Read more
Megan Geuss of Ars Technica reports, “At a Wednesday press conference in Seattle, Amazon announced a service that would go along with its newly debuted Fire Phone. Called Firefly, this new technology is packaged in an app that can identify up to 100 million objects. For the most part, this feature will integrate with the Amazon marketplace, allowing you to take photos of products and buy them from Amazon, but the technology used to make it run will also be available to developers in an SDK available now… Using Firefly, a button on the side of the Fire Phone will instruct the camera to recognize a phone number, a book, a DVD, a URL, a QR code, and more. Additionally, Firefly will be able to listen for music (like Shazam) and identify a song that’s playing in the ambient noise around you.” Read more
Schemaless structured document search system SIREn (Semantic Information Retrieval ENgine) has posted some impressive benchmarks for a demonstration it did of its prowess in searching complex nested documents. A blog here discusses the test, which indexed a collection of about 44,000 U.S. patent grant documents, with an average of 1,822 nested objects per doc, comparing Lucene’s Blockjoin capability to SIREn.
The finding for the test dataset: “Blockjoin required 3,077MB to create facets over the three chosen fields and had a query time of 90.96ms. SIREn on the other hand required just 126 MB with a query time of 8.36ms. Blockjoin required 2442% more memory while being 10.88 times slower!”
SIREn, which was launched into its own website and community as part of SindiceTech’s relaunch (see our story here), attributes the results to its use of a fundamentally different conceptual model from the Blockjoin approach. In-depth tech details of the test are discussed here. There it also is explained that while the focus of the document is Lucene/Solr, the results are identically applicable to ElasticSearch which, under the hood, uses Lucene’s Blockjoin to support nested documents.
The Semantic Web Blog also checked in with SindiceTech CEO Giovanni Tummarello to get a further read on how SIREn has evolved since the relaunch to enable such results, and in other respects.
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