1. Considering Semantic similarity using ontologies in the realm of web search Name: siming sun UNI: SS3489

2. How classical Information Retrieval system works. Most popular model in IR system— —vector space model

3. Vector space model 1. Document d viewed as is “weights” of word in document “n” is the total number of distinct words in the dictionary. 2. depends generally on ◦ Term frequency : number of times that appears in ◦ Document frequency : number of docs that contains ti in database D

4. Vector space model Query is considered as a document with most term weights equal to 0 and some specific term weights not 0.

5. Why do we need to compute the semantic similarity in web search The drawbacks of today IR system ◦ It is based on lexicographic term matching “Computers~computer”It does not take into account the semantic similarity of the two terms. “car~automobile”, “yahoo, google” Computing semantic similarity to adjust the term weights.

6. Methods to compute semantic similarity edge counting-based (or dictionary/thesaurus- based) methods （ WordNet ） and information theory-based (or corpus-based) methods(Wiki). WordNet

7. WordNet ISA hierarchy

8. Wiki descriptions—— “yahoo”& “google” Google Inc. is an American public corporation, earning revenue from advertising related to its Internet search, e-mail, online mapping, office productivity, social networking, and video sharing services as well as selling advertising-free versions of the same technologies. Yahoo! Inc. (NASDAQ: YHOO) is an American public corporation headquartered in Sunnyvale, California, (in Silicon Valley), that provides Internet services worldwide. The company is perhaps best known for its web portal, search engine, Yahoo! Directory, Yahoo! Mail, news, and social media websites and services.

9. Web search Architecture

10. References M Uschold, “Where Are the Semantics in the Semantic Web?”, AI MAGAZINE, 2003,Where Are the Semantics in the Semantic Web? Rajesh Thiagarajan, Geetha Manjunath, and Markus Stumptner, “Computing Semantic Similarity Using Ontologies”, HPL-2008-87 G Varelas, E Voutsakis, P Raftopoulou, “Semantic similarity methods in wordNet and their application to information retrieval on the web ”, Proceedings of the 7th annual ACM international workshop on …, 2005Semantic similarity methods in wordNet and their application to information retrieval on the web Y. Li, Z. A. Bandar, and D. McLean. An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources. IEEE Trans. On Knowledge and Data Engineering, 15(4):871–882, July/Aug. 2003.