DoubleVerify Launches Semantic Science Division

These processing steps include, for example, language recognition, sentence segmentation, tokenization, lemmatization, part-of-speech analysis and noun-phrase recognition. Based in Helsinki, DV’s Semantic Science division is comprised of dozens of language and software development specialists whose key competency is advanced content classification, based on semantic understanding of text. Semantics and language processing methods are used to extract relevant information from unstructured data streams and texts, identify structures and create links between the data itself and with other data sources. In a sense, the primary goal is to implement business intelligence for text—and that requires innovative technologies. This data is, by nature, highly unstructured and based largely on natural language-either directly or, as in the case of audio and video content, after transcription or similar pre-processing steps.

Traditional data mining or business intelligence methods cannot be applied to analyze this type of data. In a world full of words, phrases and acronyms with multiple interpretations, it may be challenging for automated systems to determine the correct meaning of online copy and text. There have been significant advances in machine learning and artificial intelligence over the years. To do that, we need a specific knowledge model, which can often be created from existing data (more below). In the simplest case, the knowledge model consists of relevant concepts taken from the area of application, for example, the names of products drawn from a product catalog, suppliers and customers from CRM, or relevant materials and components as identified by the R&D department.

Existing Systems Provide Input

Accordingly, the general definition of “big data,” as suggested by Gartner, also considers the complexity and variety of data, thus focusing on the relevance of highly heterogeneous and unstructured information and how it can be analyzed in various forms. If you want to analyse your imagery, you can take all the image assets you’ve ever created and note down the particular elements you used in each, then check to see if there are any patterns which relate those choices to your ad performance. In the climate of the current ‘data boom’, audience targeting naturally takes precedence, with the majority (55%) of marketers saying ‘better use of data’ for audience targeting is their priority in 2019, according to Econsultancy. Carrying out all of these steps parses the relevant text, creating an annotation structure similar to adding hand-written notes containing detailed comments to a printed copy. And, luckily, the ability to see what indisputably resonates the most with our audience – and drives our bottom-line – is already in our hands.

By using the above-mentioned technologies, information inside an organization can be found faster and more accurately, drastically reducing redundancy. Moreover, when searching in an organization’s internal data sources, a domain-specific knowledge model overcompensates for the lack of links as used to determine relevance in web searches. DV’s Semantic Science team provides the industry’s most accurate content classification system, ensuring advertisers appear beside appropriate and relevant media. On a daily basis, we’re faced with countless blogs, podcasts, speakers and everything in-between promising that if we perfectly optimise our targeting, our messaging will beat the daunting odds of the 0.9% CTR cited by WordStream.

Fact, Failure, or Fantasy: Navigating How to Win with AI in Knowledge Management

Leiki’s semantic AI software engine provides high-definition analysis of any piece of text — a complex news article, for example, or contextual video data. DoubleVerify then uses this analysis data to protect brand reputation throughout the media transaction (pre-and post-bid) and to enable proactive contextual targeting of content aligned with a brand’s equity or target audience profile. DV is the first verification company to leverage semantic AI to serve advertisers at scale. Empolis Smart Information Management (SIM) combines component content management and knowledge management. SIM represents comprehensive creation, management, analysis, intelligent processing and provision of all information relevant to a company’s business processes, regardless of source, format, user, location or device. This enables organizations to optimize business-critical processes, to make founded decisions in extremely flexible and dynamic markets, and to better understand and recognize emerging developments and issues, in order to be able to react correctly and in time.

• This lack of knowledge – despite all the tools and techniques we use to offer insight – is what we at Datasine call the ‘black box’ because when it comes to understanding why, we are left in the dark.
• Sponsored content is created for and in partnership with an advertiser and produced by the Drum Studios team.
• This data is, by nature, highly unstructured and based largely on natural language-either directly or, as in the case of audio and video content, after transcription or similar pre-processing steps.
• In a sense, the primary goal is to implement business intelligence for text—and that requires innovative technologies.
• Leiki’s semantic AI software engine provides high-definition analysis of any piece of text — a complex news article, for example, or contextual video data.
• And so, we dedicate hours and hours every week to creating personas, hypothesising about audiences, segmenting users and running lengthy A/B tests to find the piece of content that our audience love.

The Empolis Information Access System (IAS) is the highly scalable, semantic platform for value-adding knowledge management solutions, which integrates the above-mentioned technologies and linguistic processes. IAS has the ability to “understand” unstructured data, e.g. text, and transforms the data into so-called “smart information” with semantic annotations. IAS allows for massive parallel processing utilizing linguistic methods for information extraction. These, in turn, form the basis for Empolis’ Smart Information Management solutions, which transform unstructured content into structured information that can be automatically processed with the help of content analysis. Core to the Semantic Science division, DV is integrating the technology gained through the recent acquisition of contextual intelligence and content classification platform, Leiki.

We also know that analysing our data to very specifically target audiences is crucial for great ROI. But we rarely put the two together and use the data available to actually analyse what content works – and why. This is why DoubleVerify is excited to officially announce the launch of its Semantic Science division. The Semantic Science division will combine AI textual analysis with deep human insight and expertise to understand the “meaning” of language so that it can be classified at an even more granular level.

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And we can use these features to keep creating great campaigns that we further optimise as our understanding of customer content preferences grows. Sponsored content is created for and in partnership with an advertiser and produced by the Drum Studios team. To crack open the black box, we need to start conducting in-depth semantic analysis of our content. In fact, the term is rather misleading because it seems to relate only to the volume of data, while ignoring its heterogeneous nature.

• As experienced marketers, we come prepackaged with a deep understanding of – and fascination with – psychology and our audience, meaning we’ve already got the skills on paper to analyse our content.
• These processing steps include, for example, language recognition, sentence segmentation, tokenization, lemmatization, part-of-speech analysis and noun-phrase recognition.
• AI models can extract all of these elements in seconds by analysing image or text semantically to look at content like humans do.
• Empolis Smart Information Management (SIM) combines component content management and knowledge management.

Semantic Analysis of Unstructured Data

As experienced marketers, we come prepackaged with a deep understanding of – and fascination with – psychology and our audience, meaning we’ve already got the skills on paper to analyse our content. This lack of knowledge – despite all the tools and techniques we use to offer insight – is what we at Datasine call the ‘black box’ because when it comes to understanding why, we are left in the dark. Just looking at results doesn’t give us the insight needed to truly understand content preferences in an actionable way.

And so, we dedicate hours and hours every week to creating personas, hypothesising about audiences, segmenting users and running lengthy A/B tests to find the piece of content that our audience love. We add to our already-complex marketing stacks tools that tell us what messaging has been more successful, in order for us to optimise. By embracing semantic content analysis and working collaboratively with AI, we can feel confident in understanding exactly what content is going to work before we hit send. If we have just a few campaigns on the go, content analysis is easier, but it gets harder as we scale. It stops being practical to expect humans to spend days, weeks, even months labelling what goes into each piece of content. KMWorld is the leading publisher, conference organizer, and information provider serving the knowledge management, content management, and document management markets.

Gaining structured, usable information from big data sources requires the use of linguistics, or language technology. Information is extracted and structures are identified through the in-depth analysis of texts and data flows. This involves several processing steps that are initially carried out at the linguistic level only, independent of the specific application domain.