NLP Applications — 11 applications of Knowledge Graphs
Question Answering, Recommender Systems, Information Retrieval, etc.
Knowledge graphs (KGs), i.e. representation of information as a semantic graph, got wide consideration in both the industrial and academic world. Thanks to their ability to provide semantically structured information, they brought significant solutions for many tasks including question answering, recommendation, and information retrieval.
While being less flexible and robust to noise compared to deep learning models, knowledge graphs are natively developed to be explainable and are a promising solution for the issue of understandability in machine learning.
Let’s see now some examples of applications of knowledge graphs. Have a look at the paper A Survey on Application of Knowledge Graph to read about more applications.
Question Answering System
Semantic information from KGs can be used to enhance search results in semantic-aware question answering services. Watson, a question answering system using several knowledge bases as its data source, has been developed by IBM to defeat human experts in a quiz television show. Structured knowledge is also an important component of chatbots and virtual assistants such as Cortana and Siri.
Recommender Systems
Collaborative Filtering is a type of recommender system that performs recommendations based on users’ common preferences and historical interactions. This method usually suffers from the sparsity of users’ data (i.e. user-item interactions) and the cold start problem, but KGs can help solve such problems by using side information.
In general, using KGs in recommender systems helps to improve the accuracy and increase the diversity of recommended items, as well as brings interpretability to recommendations.
Information Retrieval
More and more commercial web-based search engines today are incorporating entity data from KGs to improve their search results. For instance, Google incorporates data from Google Plus and Google Knowledge Graph, while Facebook uses Graph Search.
Human knowledge about real-world entities in KGs assists search engines by improving their ability of understanding queries and documents. Such entity-oriented search improves with the development of large-scale KGs. KGs can be used in different components such as query representation, document representation, and ranking of a search system.
Domain-specific applications
- Medical: textual medical knowledge is occupying a more and more important position in healthcare information systems. Therefore, there have been efforts in integrating textual medical knowledge into knowledge graphs, aiming at retrieving specific information using inference.
- Cybersecurity: KGs can be combined with cybersecurity by providing context information useful to detect and predict dynamic attacks and safeguard people’s cyber assets.
- Financial: it’s possible to build an enterprise KG by crawling the news of each company, identifying named entities, and extracting business relations between relevant stocks. This can then be combined with the news sentiment of correlated stocks in order to predict stocks’ price movement.
- News: news language is highly condensed and full of knowledge entities and common sense, which are definitively areas where KGs can help. Moreover, fake news detection can be posed as a link-prediction task in KGs.
- Education: some studies have adopted KGs for learning resource recommendation and concept visualization. For example, KnowEDU is a system to construct KG for education automatically. Unlike general KGs in which nodes represent real-world entities, nodes in educational KGs represent instructional concepts that learners should master.
- Social Network: KGs have been applied to social network de-anonymization, where they help to determine and measure privacy disclosure.
- Classification: considering image classification, for example, it’s possible to leverage knowledge graphs to study the relationship between categories in the image, and use the semantic information extracted from the graph to guide the image classification task. Moreover, neural networks cells can be modified to integrate with external knowledge, which directly contributes to the predictions.
- Geoscience: while most geoscientific research work focuses on processing georeferenced quantitative data, some researchers are trying to extract information and knowledge discovery from textual geoscience data. Such work consists of processing geological documents and extracting knowledge directly, building a KG.
