In this paper, we describe the construction of TeKnowbase, a knowledge-base of technical concepts in computer science. Our main information sources are technical websites such as Webopedia and Techtarget as well as Wikipedia and online textbooks. We divide the knowledge-base construction problem into two parts -- the acquisition of entities and the extraction of relationships among these entities. Our knowledge-base consists of approximately 100,000 triples. We conducted an evaluation on a sample of triples and report an accuracy of a little over 90 . We additionally conducted classification experiments on StackOverflow data with features from TeKnowbase and achieved improved classification accuracy.
@cite_11: In today's computerized and information-based society, we are soaked with vast amounts of natural language text data, ranging from news articles, product reviews, advertisements, to a wide range of user-generated content from social media. To turn such massive unstructured text data into actionable knowledge, one of the grand challenges is to gain an understanding of entities and the relationships between them. In this tutorial, we introduce data-driven methods to recognize typed entities of interest in different kinds of text corpora (especially in massive, domain-specific text corpora). These methods can automatically identify token spans as entity mentions in text and label their types (e.g., people, product, food) in a scalable way. We demonstrate on real datasets including news articles and yelp reviews how these typed entities aid in knowledge discovery and management.
* Entity extraction One of the important aspects of building domain-specific knowledge-bases is that a dictionary of terms that are relevant to the domain should be acquired. It is possible that such dictionaries are already available (for example, lists of people), but for others, we need techniques to build this dictionary. @cite_11 gives an overview of supervised and unsupervised methods to recognize entities from text. We follow a more straightforward approach -- we specifically target technology websites and write wrappers to extract a list of entities related to computer science.