Semantics, the study of meaning, is playing an increasingly important role in the development of knowledge management tools across a variety of industries, and some of the most interesting developments are coming from the media world.
Semantic search is one broad area within the higher realm of semantic technologies, which also includes knowledge storage, information extraction and reasoning, among other topics. The goal of semantic search is to improve search result accuracy by understanding the searcher’s intent and the contextual relationships between the terms used in the search.
We spoke with Evan Sandhaus, lead architect of semantic platforms at The New York Times Company, and Jeff Catlin, CEO of text analytics company Lexalytics, to better understand how semantic search is affecting news and social media.
“All websites are in the business of capturing people’s attention,” said Sandhaus, recalling a recent presentation he had attended. This is especially true for news organizations and blogs, which push out piles upon piles of online articles each day. In the end, the news isn’t exactly useful if no one reads it. So, the goal is to make content as findable as possible.
The fundamentally challenging structure of the web, Sandhaus says, isn’t exactly helping the cause, though. The web is predominantly written in HTML, a markup language that focuses on expressing how information on a webpage should look, not what it means. As a result, important pieces of information within webpages, such as headlines, bylines and publish dates in news articles, are formatted within HTML, but aren’t explicitly labeled as “headline, “byline” and “publish date.” “As a consequence,” Sandhaus explains, “it makes it difficult for a wider web ecosystem to have an idea of the structured nature of content.” That is, while webpages are formatted for humans to easily read them, machines can’t easily determine the underlying meaning of content on a page if it doesn’t follow a consistent structure. Thus, devaluing the utility of data.
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