READIT - 2009 - Indira Gandhi Centre for Atomic Research

More documents

Recommendations

Info

Proceedings of READIT-2009, Kalpakkam - 603 102, 29-30 Dec 2009 traditional web search, but it will not replace it. Semantic search is a process used to improve online searching by using data from semantic networks to disambiguate queries and web text in order to generate more relevant results. The Semantic Search Engine or Third-Generation Search Engine is a search engine in which Semantic technologies and / or Natural Language Processing (NLP) techniques are applied. The semantic search engine informs its user using a rich framework for conceptual interpretation married with the best of modern indexing and search techniques. Differently from traditional search engines, a semantic search engine stores semantic information about Web resources and is able to solve complex queries, considering as well the context where the Web resource is targeted. The major semantic search engines are the following: 2.1. HAKIA –WWW.HAKIA.COM Hakia is a semantic search engine that brings relevant results based on concept match rather than keyword match or popularity ranking. I.e. Hakia is an ontological semantic and natural language processing (NLP) based search engine. Hakia was founded by Riza Berkan, a nuclear scientist by training with a specialization in artificial intelligence and fuzzy logic, and Pentti Kouri, a New Yorkbased economist and venture capitalist. Hakia is a general purpose "semantic" search engine, dedicated to quality search experience. Hakia’s quality search results satisfy three criteria simultaneously: They (1) come from credible Web sites recommended by librarians, (2) represent the most recent information available, and (3) remain absolutely relevant to the query. Hakia semantic search is essentially built around three evolving technologies: 1. OntoSem (sense repository) 2. QDEX (Query indexing technique) 3. Semantic Rank algorithm 99
Proceedings of READIT-2009, Kalpakkam - 603 102, 29-30 Dec 2009 Figure 1 Hakia Home page 2.2. SWOOGLE – WWW. SWOOGLE.UMBC.EDU Swoogle-the semantic web search engine and metadata service provider, searching over 10,000 ontologies. Swoogle was developed at and is hosted by the University of Maryland, Baltimore County (UMBC) with funding from the US DARPA and National Science Foundation agencies. Swoogle employs a system of crawlers to discover RDF (Resource Description Framework) documents and HTML documents with embedded RDF content. Swoogle reasons about these documents and their constituent parts (e.g., terms and triples) and records and indexes meaningful metadata about them in its database. Swoogle provides web scale semantic web data access service, which helps human users and software systems to find relevant documents, terms and triples, via its search and navigation services. 100
Page 1 and 2:
National Conference on Recent Advan
Page 3 and 4:
Foreword The 7th biennial national
Page 5 and 6:
access to information resources. RF
Page 7 and 8:
Organising Committee READIT-2009 Ch
Page 9 and 10:
Invited Presentations: CONTENTS Ses
Page 11 and 12:
C. Jaykumar, E. Soundararajan, V. R
Page 13 and 14:
Session-I: Semantic Web Technologie
Page 15 and 16:
Proceedings of READIT-2009, Kalpakk
Page 17 and 18:
Page 19 and 20:
Page 21 and 22:
Page 23 and 24:
Session-III: Open Source Solutions
Page 25 and 26:
Page 27 and 28:
Page 29 and 30:
Session-IV: Knowledge Management Pr
Page 31 and 32:
Page 33 and 34:
Page 35 and 36:
Page 37 and 38:
Page 39 and 40:
Page 41 and 42:
totally around Internet protocols a
Page 43 and 44:
Linux has been under continuous dev
Page 45 and 46:
Page 47 and 48:
Page 49 and 50:
Page 51 and 52:
Page 53 and 54:
Page 55 and 56:
Page 57 and 58:
Page 59 and 60:
Page 61 and 62:
Page 63 and 64:
Page 65 and 66: Proceedings of READIT-2009, Kalpakk
Page 115: Proceedings of READIT-2009, Kalpakk
Page 163 and 164: upon by indwelling and is the basis
Page 165 and 166: new, and its combination of new ide
Page 167 and 168:
o Knowledge: Both explicit and taci
Page 169 and 170:
Knowledge Management Practices in L
Page 171 and 172:
To these paraphrases of Macintosh
Page 173 and 174:
(iii) Healthy interactions among pr
Page 175 and 176:
9. CONCLUSION Knowledge management
Page 177 and 178:
Ontologies define a common vocabula
Page 179 and 180:
to have an environment that allows
Page 181 and 182:
CDPS PC System Based SS-I SS-II RAA
Page 183 and 184:
Preservation of Manuscripts with Sp
Page 185 and 186:
- (Many more) 3 Chemical Factors a.
Page 187 and 188:
6.2.2 Software The Softwares requir
Page 189 and 190:
7.1.3 Current Work As the above pro
Page 191 and 192:
Treatment process 7.2.6 Drying Afte
Page 193 and 194:
Before Editing & Cropping After Edi
Page 195 and 196:
Knowledge Management - An Organizat
Page 197 and 198:
5. KNOWLEDGE MANAGEMENT ACTIVITIES
Page 199 and 200:
Management System and is made avail
Page 201 and 202:
Services 3.0 make it easier than ev
Page 203 and 204:
ecover data when a disk fails and r
Page 205 and 206:
Symmetric Multi Processing (SMP) Se
Page 207 and 208:
Knowledge management practice in li
Page 209 and 210:
3. KNOWLEDGE MANAGEMENT: SOME DEFIN
Page 211 and 212:
It is to create a set of effective
Page 213 and 214:
REFERENCES 1. Banka Bihari Chand (2
Page 215 and 216:
2 Fig : 1. Five ‘ C ‘ Model Kn
Page 217 and 218:
4 Knowledge acquisition is the star
Page 219 and 220:
6 group of audience. Similarly wit
Page 221 and 222:
8 Knowledge sharing is enhanced by
Page 223 and 224:
10 sharing, and creating a map of
Page 225 and 226:
12 10. REQUIRED SKILLS AND CAPABILI
Page 227:
14 Wireless Communication, Networki
show all

READIT - 2009 - Indira Gandhi Centre for Atomic Research

Create successful ePaper yourself

Delete template?

Save as template?