Semantic based Enterprise Search Solution in Networking Domain
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The document discusses an enterprise search solution focused on enhancing information retrieval through semantic search and entity detection. It highlights the importance of ontology, query expansion, and latent semantic indexing to address challenges such as ambiguity and synonymy in search queries. Additionally, it outlines various use cases for improving search experience and extracting meaningful data from diverse sources.
Semantic based Enterprise Search Solution in Networking Domain
- 1. ENTERPRISE SEARCH SOLUTION Ranjan Baisak(RB) rb@ezmap.in blog.ezmap.in
- 2. 2 Copyright © 2013 Juniper Networks, Inc. www.juniper.net SCENARIOS I want to see facts from different sources describing EX4200 I want to more about EX4200 about its chassis specifications I want to know power supply details of EX4200 switches I want to know more about Juniper’s Security solutions I want to know Juniper’s Data center offerings I want to see customer’s feedback about Junos
- 3. 3 Copyright © 2013 Juniper Networks, Inc. www.juniper.net 1. Find Information faster Provide search assistants 2. Reveal hidden information Enrich the search index with background knowledge 3. Find more specific information Query the semantic web 4. Find linked information Integrate data sources
- 4. 4 Copyright © 2013 Juniper Networks, Inc. www.juniper.net FIND INFORMATION FASTER To Provide a powerful auto-complete based on past searches To have a enterprise vocabulary to assist in auto-complete I can’t remember how to spell the search term
- 5. 5 Copyright © 2013 Juniper Networks, Inc. www.juniper.net FIND INFORMATION FASTER High Performance Networking Search I can’t remember exactly what I was looking for
- 6. 6 Copyright © 2013 Juniper Networks, Inc. www.juniper.net FIND INFORMATION FASTER WITH RELATED SEARCH TERM EX4200 Search
- 7. 7 Copyright © 2013 Juniper Networks, Inc. www.juniper.net REVEAL HIDDEN INFORMATION WITH QUERY EXPANSION VLAN SearchOR ”VLAN Configuration" Ontology VLAN VLAN Configuration alternative Label preferred Label
- 8. 8 Copyright © 2013 Juniper Networks, Inc. www.juniper.net FIND LINKED INFORMATION IX Content KB TechNotes J-Net Technical Bulletin EX4200
- 9. 9 Copyright © 2013 Juniper Networks, Inc. www.juniper.net WHY ENTITIES IMPORTANT Entities are new driver. Entities are generated relational mapping that uncovers the association between different data points. Entities becomes trusted points around which other data revolves.
- 10. 10 Copyright © 2013 Juniper Networks, Inc. www.juniper.net THE ROLE OF ONTOLOGY IN SEMANTIC SEARCH Junos 12.2 R1 Junos 12.2 JunosSoftware Sep 05 2012 ACX 1000 ACX 2000 M7i M10i MX5 MX10 T320 T640 EX2200 EX4200 ACX M Series MX T Series EX Series Routing Switching Hardware Released on Belongs to Supported on Belongs To Belongs To Belongs To
- 11. 11 Copyright © 2013 Juniper Networks, Inc. www.juniper.net HOW TO EXTRACT ENTITIES Top level view, how entities are defined. We have data sources from iX contents, KB, Technets, J-Net forums, Technical Bulletins etc… Through entity detection and raw relation detection raw text extracted from web pages in unstructured data format at one end becomes a responsive, refined entity that can provide an intelligent answer in semantic search.
- 12. 12 Copyright © 2013 Juniper Networks, Inc. www.juniper.net ONTOLOGY A CENTRAL POINT TO CONTROL Labels and Query Expansion Faceted Search Refine Search Mechanism Metadata Integration Search Services Search Application Collector Semantic Indexer Document Index Ontology Manager Extractor HTML Ontology DB
- 13. 13 Copyright © 2013 Juniper Networks, Inc. www.juniper.net PROBLEM WITH CURRENT SEARCH Ambiguity and association in natural language Search is based on text Polysemy: Words often have a multitude of meanings and different types of usage (more urgent for very heterogeneous collections). Synonymy: Different terms may have an identical or a similar meaning (weaker: words indicating the same topic).
- 14. 14 Copyright © 2013 Juniper Networks, Inc. www.juniper.net LATENT SEMANTIC INDEXING Latent Semantic Indexing (LSI) means analyzing documents to find the underlying/latent meaning/semantics or concepts of those documents. The fundamental difficulty in finding relevant documents from search words is that what we really want is to compare the meanings or concepts behind the words. LSA attempts to solve this problem by mapping both words and documents into a "concept" space and doing the comparison in this space. LSI overcomes two of the most problematic constraints of Boolean keyword queries: multiple words that have similar meanings (synonymy) words that have more than one meaning (polysemy). Text does not need to be in sentence form for LSI to be effective. It can work with lists, free-form notes, email, web content, etc. LSI is also used to perform automated document categorization and clustering. In fact, several experiments have demonstrated that there are a number of correlations between the way LSI and humans process and categorize text.