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Graph-Based Text Mining

The most precise method to create knowledge from unstructured data

About 80 to 90 percent of the information companies generate is extremely diverse and unstructured—stored in text files, e-mails or similar documents, what makes it difficult to search and analyze. An organization’s ability to process large amounts of text, gain insight from it, organize it, and use it to answer questions is of paramount importance. Without intelligent technologies like graph-based text mining, this can be a very hard task to maintain. Companies often find it very difficult to exploit their data because it would simply be too time-consuming to extract relevant knowledge from unstructured information.

PoolParty’s graph-based text mining 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 combines machine learning and NLP techniques with knowledge graphs to enable algorithms to better analyze text by not only processing words, but understanding the underlying concepts and their context.

Gartner predicts that

“by 2024, companies using graphs and semantic approaches for natural language technology (NLT) projects will have 75% less AI technical debt than those that don’t.

Gartner, Inc: ‘Predicts 2020: Artificial Intelligence — the Road to Production’ (Anthony Mullen et al, December 2019)

The biggest challenges for organizations not using graph-based text mining.

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Language obstacles

Content is often made up of natural language which can be tricky to interpret because it contains issues such as ambiguity in the same word meaning a different thing, i.e. apple like the fruit and Apple like the tech company. This leads machines to misinterpret the meaning, a common problem e.g. with virtual assistants who often take information at face value and do not have the ability to read between the lines.

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Lack of background knowledge

Since organizations produce significant amounts of unstructured data, it is prone to missing knowledge linkages and understanding. Without context, many words are floating around a database seemingly unconnected because there is no clear information there to link the words together.

Useful Resources

HR Recommender Demo: connect employees, shows them relevant projects, and much more.

Named Entity Recognition Demo: automatically extract concepts and terms from text.

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Case Study: Knowledge Graphs in the Banking and Insurance Sectors

The biggest changes organizations can leverage with PoolParty’s graph-based text mining.

This approach leads to, amongst other benefits, extremely accurate automated tagging, which not only saves time by avoiding the manual tagging of large amounts of documents, but also improves knowledge discovery and decision making by making information easier to find and analyze.

For business and content users who heavily deal with large text documents and contracts, PoolParty Sense Extraction makes contract management much easier. With semantic extraction tools, these users can derive information from contracts to minimize risk, stay compliant, uncover hidden costs, etc.  Check out our free Contract Intelligence demo to see how the extractor works.

Experience the major benefits of graph-based text mining with PoolParty Semantic Suite now.

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Get context from your data

PoolParty’s graph-based text mining uses knowledge graphs and semantic standards to process the context of the text, which can then be embedded in an even broader context. It combines machine learning and NLP techniques with knowledge graphs to enable algorithms to better analyze text by not only processing words, but understanding the underlying concepts and their context.

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Document intelligence

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.

Auto-tagging and document classification

Graph-based text mining enables recognition of concepts and named entities that can be used to classify documents and texts. PoolParty’s comprehensive Named Entity Recognizer can extract concepts and important terms as well as classify documents by their genre based on trained knowledge models to automate the extraction and classification processes.