7 Ways Semantic Technologies Make Data Make Sense
As unstructured data piles up, semantic technologies help organizations drive business value through a better understanding of the data they have, its value, and the relationships pieces of information have to each other.
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The volume and variety of data is exploding, and the race is on for organizations to make better sense of it all. Still, many organizations are struggling to drive value from their unstructured data. Semantic technologies can help companies understand all of their data and the value of it, and enable insights that were not available before. Businesses are also using semantic technologies to unearth precious nuggets of information from vast volumes of data and to enable more flexible data use. Semantic data analysis is about identifying the meaning and tone in unstructured text.
"Unstructured data is becoming more critical to manage for the purpose of completing a business process or to provide better customer support," said Steve Butler, general manager of artificial intelligence platform provider AI Foundry. "People are reading and processing unstructured documents, but if you can automate that, you can save money and improve customer support."
Semantic technology isn't new, but it's rapidly gaining momentum as more companies attempt to compete with data. Semantic technology has always been about the meaning of data, its context, and the relationships between pieces of information. Recruiting and job search site CareerBuilder has been using semantic technologies for more than a decade to improve the relevance of job and candidate searches. Recently, the company acquired a majority stake in semantic recruitment-technology company TextKernel to fortify its semantic search and data analytics capabilities.
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"If you think about semantic search, it's not what you type, it's what you mean. The relevancy has always got to be there," said Matt Ferguson, CEO of CareerBuilder. "What we're trying to do is maintain the highest level of relevancy while shortening the time [necessary to find a job or fill a position]."
Some organizations are using semantic technologies to overcome the limitations of rules-based systems and ordinary keyword search, since both of those exclude that which was not explicitly defined.
"Analysts are concerned about the freshness and completeness of data [as well as] the merging of structured and unstructured data. People want to be able to understand the context of their data and what questions they can ask of it. They [also] want flexibility in the way they're looking at data and asking questions of it," said Sean Martin, CTO and founder of smart data platform provider Cambridge Semantics.
Click through to see some examples of what you can do with semantic technologies. Then tell us what you think in the comments.
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Companies want to understand which data sources are available, the quality of the data flowing from those sources, and any data dependencies. Given the large number of databases so many organizations have today, those companies need an efficient way of cataloging the databases and understanding the data that resides in them. Semantic models provide an effective way of doing that.
"You can use semantic models to annotate what's in the data and to describe the business meaning of the data. Because a semantic model is a graph representation, it's really much easier to represent data in the way people think about it," said Sean Martin, CTO and founder of Cambridge Semantics.
Social media data is far less reliable than many other sources of data. When a signal is misinterpreted, the result may be a regrettable action on the part of an employee or an entire organization.
For example, when Scotland held a referendum on whether to secede from the UK in 2014, the Bank of England feared a run on the banks. As part of its risk management strategy, it started to track terms and keywords on Twitter. One morning, there was a dramatic spike on "RBS" -- which seemed to mean the Royal Bank of Scotland. However, the chatter was actually about "RBs," specifically Minnesota Vikings' running backs tweeted about during a football game.
"Without a semantic model to understand the context of the data, you can be bombarded by false signals that are a distraction and can make your analysis invalid. Semantic models and technologies like deep learning can help you understand the context of content so you can remove it from the analysis or focus on it," said Tim Barker, CEO of Big Data platform provider DataSift.
Recruiters spend an inordinate amount of time trying to match qualified candidates with open positions. Ten to fifteen years ago, when CareerBuilder began using semantic technology, it was able to improve search efficiency by 5% to 10%. Now, a search that used to take 20 minutes can be done in 5 minutes.
"We have this aspirational concept we call 'zero time to fill.' If you're a recruiter, you're under a lot of time pressure. [Nevertheless], you have to go through a lot of documents and resumes to find the best match," said Matt Ferguson, CEO of CareerBuilder. "When a recruiter uploads a job description to the system, [he or she] wants to compare it against all external resume databases, bring back those documents, and rank-order them quickly."
Large companies may have millions of resumes at their disposal, some of which they have immediate access to and some of which are gathered from job search sites. The goal is to reduce an unwieldy number of resumes to a manageable subset of 25 or 50 relevant ones. Recruiters can then take advantage of machine learning to further narrow or expand the concepts. To allow them to do that effectively, CareerBuilder allows recruiters to see the input the machine used and its reasoning so they can make adjustments as necessary.
Database queries, like search, are limited by human thought and bias. What's known about data is encoded into software and database schemas, but the underlying system doesn't understand semantics. When semantic knowledge is built into the database, it's possible to do smarter queries and understand relationships between or among pieces of information that were not evident before.
"People have been storing facts in databases and running queries, but you can only look things up that were stored explicitly. With semantics, you can get answers [by] getting the database to do some of the reasoning for you, as well as to answer more complex and interesting questions," said Joe Pasqua, executive vice president of products at MarkLogic.
NBC, a MarkLogic customer, took advantage of semantics when building its Saturday Night Live (SNL) 40th anniversary iPhone app. The goal was to maximize user engagement by anticipating the video clips individual users wanted to see. To do that effectively, it added semantic information to the SNL video clips including who the actors were, which characters they were playing, and the era from which the skit came.
Organizations are understandably concerned about turning their data lakes into data swamps. Poor quality data leads to substandard analytics and erroneous conclusions. Nevertheless, some organizations are losing control of the meaning and context of their data as it's stored. Semantic models provide a standards-based means of putting the context and meaning back in.
"One of the things we're able to do with semantics is connect metadata models and describe where the data is to the ETL [extract, transform, and load] data ingestion process so you can create semantic models that describe the flow of data and the transformation of data. Then, you can operationalize that," said Sean Martin, CTO and founder of Cambridge Semantics.
Traditional business intelligence is labor-intensive. One has to think about the questions up front, what the data warehouse requirements are, the ETL necessary to populate the data warehouse, and the reporting requirements. The entire process is time-consuming, and the scope of entity types is comparatively limited in light of modern advances.
"Graph analytics, the application of semantic models and RDF [Resource Description Framework] graph format, allows you to consider all the entities you care about simultaneously. As an end-user you have random access to it so you're [never limited] by what's been prepared for you. You can keep loading more data in to answer your questions," said Sean Martin, CTO and founder of Cambridge Semantics.
The result is iterative, flexible ad-hoc analysis on the fly.
Keyword search is a fairly effective way to navigate unstructured data, but its efficiency is affected by the user's ability to select search terms that align with the desired content. The outcome is often an overabundance of results or lack of relevant results. Anything that was not explicitly stated in the search remains hidden. Semantic search is different because it's is not limited to explicit statements. It understands the meaning of information, its context, and its relationship to other pieces of information to deliver more precise results. But, like anything else, semantic technology also has limitations.
"We found semantic technology is great for certain things or certain types of data that you want to get at, but not everything -- a table in a document, for example," said Drew Warren, CEO of Recognos Financial. "The part of big data that's often neglected is this notion of little data, the data in an organization that is of paramount importance to a firm, so we created a platform that will actually structure unstructured data."
Keyword search is a fairly effective way to navigate unstructured data, but its efficiency is affected by the user's ability to select search terms that align with the desired content. The outcome is often an overabundance of results or lack of relevant results. Anything that was not explicitly stated in the search remains hidden. Semantic search is different because it's is not limited to explicit statements. It understands the meaning of information, its context, and its relationship to other pieces of information to deliver more precise results. But, like anything else, semantic technology also has limitations.
"We found semantic technology is great for certain things or certain types of data that you want to get at, but not everything -- a table in a document, for example," said Drew Warren, CEO of Recognos Financial. "The part of big data that's often neglected is this notion of little data, the data in an organization that is of paramount importance to a firm, so we created a platform that will actually structure unstructured data."
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