PPT-Deep Representation of Biological Knowledge for Question Answering

Vinay K Chaudhri 1 Outline Introduction KBBio101 Biology textbook knowledge base Representing structure and function Representation and Reasoning needs Upper ontology Representing structure. Introduction. Domain specific knowledg. e is needed to solve some problems.. Knowledge base – representation.. Inference techniques. Use to prove facts.. Use to answer queries. Knowledge Representation Schemes. What is Question Answering?. 2. Question Answering. One of the oldest NLP tasks (punched card systems in 1961). Simmons, Klein, . McConlogue. . 1964. Indexing and Dependency Logic for Answering English Questions. American Documentation 15:30, 196-204. The Edwin Smith papyrus. Title:. Instructions for treating a fracture of the cheekbone.. Symptoms:. If you examine a man with a fracture of the cheekbone, you will find a salient and red fluxion, bordering the wound.. Patricia A. Alexander. In all affairs it's a healthy thing now and then to hang a question mark on the things you have long taken for granted. . BERTRAND RUSSELL. GOALS. Overview certain assumptions about question asking and question answering. Part II. Description Logic . & . Introduction to Protégé. Jan Pettersen Nytun. 1. The Semantic . Web. Knowledge Representation Part II, JPN, UiA. 2. ". The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation.“. Lin Ma, . Zhengdong. Lu, and Hang Li. Huawei Noah’s Ark Lab, Hong Kong. http://. www.ee.cuhk.edu.hk. /~lma. /. . Mine the relationships between multiple modalities. Association different modalities. Spivak. /Devi. Representation and its meanings. a) to re-present, as in the work of imagination that re-presents reality in literature; . b. ) to represent, as in to stand in for, to speak for, to speak as, in the realm of politics. . Chitta. . Baral. Arizona State University. Apologies. For not being here physically on this special occasion. My early history with SMS. My . Ph.D. during 1987-1991. Finding the “right” semantics of negation in logic programming was an important topic . . Artificial . intelligence problems span a very broad spectrum. They appear to have very. little in common except that they are hard. Are there any techniques that are appropriate. for the solution of a variety of these problems? The answer to this question is yes, there. Kai-Wei Chang. CS @ University of Virginia. kw@kwchang.net. Couse webpage: . http://kwchang.net/teaching/NLP16. 1. CS6501-NLP. Question answering. CS6501– Natural Language Processing. 2. credit: ifunny.com. IST597: Foundations of Deep Learning. The Pennsylvania State . University. Thanks to . Sargur. N. Srihari, . Rukshan. . Batuwita. , . Yoshua. . Bengio. Manual & Exhaustive Search. Manual Search. Topic 3. 4/15/2014. Huy V. Nguyen. 1. outline. Deep learning overview. Deep v. shallow architectures. Representation learning. Breakthroughs. Learning principle: greedy layer-wise training. Tera. . scale: data, model, . Why PETA has used a woman in its vegetarianism campaign? (15 marks). In your answer you must:. -Analyze the representation of women in the source. - Make judgements about why PETA has opted to use this approach for the campaign.. Concepts, Category, Networks, and Schemas. Concepts- . an idea about something that provides means of understanding the world. Organization of Declarative Knowledge. Category – .