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_0: IBM Research undertook a challenge to build a computer system that could compete at the human champion level in real time on the American TV Quiz show, Jeopardy! The extent of the challenge includes fielding a real-time automatic contestant on the show, not merely a laboratory exercise. The Jeopardy! Challenge helped us address requirements that led to the design of the DeepQA architecture and the implementation of Watson. After 3 years of intense research and development by a core team of about 20 researches, Watson is performing at human expert-levels in terms of precision, confidence and speed at the Jeopardy! Quiz show. Our results strongly suggest that DeepQA is an effective and extensible architecture that may be used as a foundation for combining, deploying, evaluating and advancing a wide range of algorithmic techniques to rapidly advance the field of QA.
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@cite_12: We demonstrate Sig.ma, both a service and an end user application to access the Web of Data as an integrated information space. Sig.ma uses an holistic approach in which large scale semantic web indexing, logic reasoning, data aggregation heuristics, ad hoc ontology consolidation, external services and responsive user interaction all play together to create rich entity descriptions. These consolidated entity descriptions then form the base for embeddable data mashups, machine oriented services as well as data browsing services. Finally, we discuss Sig.ma's peculiar characteristics and report on lessions learned and ideas it inspires.
Knowledge-bases have many applications -- see for example, Google's Knowledge Graph @cite_16 which is used to provide users with concise summaries to queries about entities, semantic search engines @cite_12 , question answering systems such as IBM Watson @cite_0 , etc. Our aim is to build a domain-specific knowledge-base (in this case, computer science) which helps in these kinds of applications, but in addition can serve as a resource for learning, including, for example, generating a reading order of concepts in order to learn a new one, automatically generating test or quiz questions about concepts to test student understanding, etc.