Home<\/a><\/span> \u00bb Natural Language Processing<\/span><\/span><\/div>\n\n\n

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Bridge the gap between human language and structured databases by converting unstructured data into graph intelligence<\/h2>\n\n\n\n
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Understanding Language<\/a><\/div>\n\n\n\n
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For decades, we have stored the majority of our data and knowledge in natural language. And we want machines to help us discover, reuse and utilize this knowledge. Even more, we can only cope with the sheer volume of data to be processed with the help of machines. But natural language is not made for machines. For them it can be tricky to interpret because natural language contains issues such as ambiguity in the same word, contextual knowledge which is covered between the lines and implicit knowledge which is hidden in how a fact is expressed.<\/p>\n<\/div>\n<\/div>\n\n\n\n
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Machines that understand your content<\/h3>\n\n\n\n
With Graph NLP natural language documents will be deconstructed into it\u2019s components to identify, extract and structure facts, relationships and assertions so that they can be further processed as text data by machines.<\/p>\n\n\n\n
Graph NLP uses knowledge graphs and semantic standards to process the context of the text to be analyzed, which can then be embedded in an even broader context. It is an advanced methodology for automatic text understanding, based on a number of technologies that are being fused together:<\/p>\n<\/div>\n\n\n\n
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Understand syntax by grammatical processing<\/h3><\/div>\n\n\n\n
In Graph NLP rule based pre-processing supports in extraction of terms and phrases following word order, punctation and grammar.<\/p><\/div>\n<\/div><\/div>\n<\/div>\n\n\n\n
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Morphology computed with NLP techniques<\/h3><\/div>\n\n\n\n
Stemming or lemmatization are some of he NLP techniques applied in Graph NLP to compute word-form and word-structure.<\/p><\/div>\n<\/div><\/div>\n<\/div>\n\n\n\n
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Semantics represented by a graph<\/h3><\/div>\n\n\n\n
The meaning of entities and their relation to other entities in a piece of text is extracted and reproduced in an neural format.\u00a0 Whereby recognition of named entities and text classification is based on graph-enhanced machine learning.<\/p><\/div>\n<\/div><\/div>\n<\/div>\n\n\n\n
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Usage context embeded into language pragmatics<\/h3><\/div>\n\n\n\n
The graph-based approach allow to embed the usage context already into the knowledge model. You get educated machines which are\u00a0 better prepared to the specifics of the pragmatic natural language application.<\/p><\/div>\n<\/div><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Applying these technologies allows machines to deconstruct natural language and transform it into computable metadata to interpret and classify words, sentences and even entire paragraphs.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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The value of Graph NLP<\/h2>\n\n\n\n
Once the machine has understood the text you provide with service portals, reports, information, or your own legacy content, you can leverage the value of natural language processing and text mining.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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Reduce transformation costs<\/h3>\n\n\n\n
The preparation of content from natural language for automated processing normally requires human interpreters. Graph NLP can take over here and significantly reduce personnel costs and processing time.<\/p>\n\n\n\n
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Controlled automatic tagging<\/li>\n\n\n\n
Categorisation of documents or paragraphs<\/li>\n\n\n\n
Sentiment and sense identification<\/li>\n<\/ul>\n<\/div>\n<\/div>\n\n\n\n
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Effective processes by non-ambiguous text understanding<\/h3>\n\n\n\n
If processes are to be controlled by natural language, then ambiguities and misunderstandings should be eliminated. Different interpretations of the same facts can have costly consequences in process control. Graph NLP ensures that unambiguous conclusions are drawn from natural language \u2013 without human interpretation blurring.<\/p>\n\n\n\n
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Trigger-word or trigger-pharse detection<\/li>\n\n\n\n
Intent identification<\/li>\n\n\n\n
Fact extraction<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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Gaining an overview and maintaining focus<\/h3>\n\n\n\n
Graph NLP helps to keep track of large volumes of content such as social media, customer support tickets, the voice of the employee and the voice of the customer and to react to those signals that are essential. Graph NLP is a filter that can be set to detect threats and risks.<\/p>\n\n\n\n
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Sieve relevant data<\/li>\n\n\n\n
Thread and risk alarming<\/li>\n\n\n\n
Rating and clustering<\/li>\n<\/ul>\n<\/div>\n<\/div>\n\n\n\n
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Access to hidden knowledge by bridging terminological barriers<\/h3>\n\n\n\n
Graph NLP breaks down natural language into understandable concepts and not just readable terms. This makes content accessible that would otherwise remain undiscoverable for use because a different jargon or the vocabulary of a different area excludes it from search and discoverability.<\/p>\n\n\n\n
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Detection of synonyms and variants<\/li>\n\n\n\n
Entity matching<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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For more information about specific features and benefits about Graphwise Semantic Analytics, refer to our product page ><\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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Experience the major benefits of Graphwise Semantic Analytics<\/h2>\n<\/div>\n\n\n\n
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Advanced Natural Language Processing using concepts<\/h3>\n\n\n\n
Graphwise Semantic Analytics<\/a> can extract meaningful phrases and entities as concepts instead of simple terms. Unique to semantic technologies, a concept allows a user to \u201cpackage\u201d a word together with all its synonyms, alternative labels, and multilingual labels.<\/p>\n\n\n\n
With concepts, a Sneaker is not just a sneaker, it is a Trainer, Tennis Shoe, Sportschuhe, zapatillas.<\/p>\n<\/div>\n\n\n\n
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More than an annotation<\/h3>\n\n\n\n
Unlike other extractors on the market, a principal advantage to Graphwise NLP is that extracted concepts are connected to a thesaurus (taxonomy). The hierarchical structure of the taxonomy notes the \u201cplace\u201d of a concept in relation to other concepts, in other words, it contextualizes them. By leveraging this structure, users can overcome language discrepancies that might otherwise be there.<\/p>\n\n\n\n
Based on the concepts around it (i.e backpack, sportswear, etc.), the computer will understand that Puma refers to the sneaker brand and not puma, the animal.<\/p>\n<\/div>\n<\/div>\n\n\n\n
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Document intelligence<\/h3>\n\n\n\n
Typically, graph-based knowledge extraction is executed against rules expressed via SHACL language. Based on this approach, complex constraints and relevant relations between business objects can be formulated and used to extract and filter out important paragraphs from large text documents.<\/p>\n<\/div>\n\n\n\n
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Automatic classification of content<\/h3>\n\n\n\n
Using machine learning algorithms and a graph knowledge model, Graphwise Semantic Analytics<\/a> can automatically classify content into its correct knowledge domain. With a small set of training data, you can have an extractor that is dedicated to each of your sectors (e.g.: legal vs. HR vs. marketing) so that when a new document is connected to the thesaurus, it will be sorted to the correct domain. Even if your document doesn\u2019t explicitly say it, concepts will be classified into the appropriate classes so that tagging the content is more precise.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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Graphwise NLP in a nutshell<\/h2>\n\n\n\n
Our Graph NLP is realized as a chain of interconnected API calls.<\/p>\n<\/div>\n\n\n\n
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Demonstrates how Knowledge Graphs enable algorithms to better analyze text by not only processing words, but also understanding the underlying concepts and their context.<\/p>\n Read more<\/a>\n <\/div>\n <\/div>\n <\/div>\n
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