![]() ![]() This technique reduces the cold start period for machine learning. This training set is part of the business domain and should therefore be more accurate than the generic Wikipedia page. This produces a new training set of M applications. The M applications most similar to the Wikipedia page are then examined by a human, who has to decide whether Energy is indeed a good label or not. We rank these, and pick out the first M applications, with M < N. We train the model on N applications and for each application we have a similarity score with the training set. We use a linear support vector machine (SVM) for the model training. ![]() We start with the Energy page from Wikipedia and define it as the training set. In our example case we are looking for all applications in the energy sector. So let’s start with a training set obtained from somewhere else, say a Wikipedia page about the new topic. We want to avoid introducing a human again. The system now has to learn that certain documents should be labeled with the new label. So what do we do if we want to introduce a new topic? Finding topics is one thing, but defining new topics through the ontology is something else. The system must continuously learn new topics and slowly changing contexts. However, there is no guarantee the training set is representative of new documents. For those data we know the topics are OK, because we have human-labeled them via the found topics. The topics were found using a limited data set, the training set. No need to read through all the documents anymore! It's all automated. Once we have the topics-the ontology-each new document now can be automatically classified. Some found topics are manually labeled as non-topic because they describe the document or its contexts itself, instead of the business domain that the content is about. Using Natural Language Processing (NLP) techniques, such as topic modelling and named-entity recognition, one can quickly find the important topics and entities buried in the texts. #Wikidata graph builder example manualThe documents themselves tend to sleep and only kept alive for reference.Īfter time, when business changes, these documents can not be used in a new context unless they are relabelled and reprocessed, which is cumbersome in a manual procedure. Labels and metadata are then stored in a database, together with a link to the original document. At the same time, some metadata is usually added to the documents. Humans read the documents and label them. Processing these documents often entails categorising the information contained in them. Many organisations have large amounts of information contained in free-text documents. Learn how you can leverage artificial intelligence to use that dark data and turn it into valuable business insights, using a Knowledge Graph. From unstructured dark data to valuable business insightsĭo you have a lot of text documents stored on hard disks or in the cloud, and you don't use its textual information directly in your business? Then this article is for you. ![]()
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