Survey of Semantic Search technologies for Information Retrieval. Eric Abecassis Houston Technology Center Manager

Transcription

1 Survey of Semantic Search technologies for Information Retrieval Eric Abecassis Houston Technology Center Manager

2 2009 Schlumberger. All rights reserved. An asterisk is used throughout this presentation to denote a mark of Schlumberger. Other company, product, and service names are the properties of their respective owners.

3 Agenda What is the problem Imagine a world where Semantic Search to the rescue!

4 Library of Babel

5 From Information to Action Information Technical Business Gathering Analysis Analysis Action

6 Imagine a World Where You want to search for information about an environmental impact in a coalbed methane reservoir found in the last 10 years in British Columbia. You remember a colleague from the reservoir characterization team in GOM who worked on this type of reservoir and produced some interesting paper but you don t remember his name and he is not working for the company anymore.???

7 Classifying the data Author Time Meta Data By adding meta-data tag on documents and objects, we greatly improve the query by allowing more powerful search based on the dimension of the different tags (by author, by time, by depth, by location ). If meta-data tags are used to bridge between documents and structured data, it allows even more powerful queries. Depth Spatial Classifying the Data Recent progress in standards and tools allows to seriously consider using Ontology to automatically classify documents and add tags. On spatial, MetaCarta is an excellent illustration of automatic classification based on location. Ontology Base classification Social Tagging Social network is a powerful tool to leverage the expertise of your people and harvest their knowledge in a reusable way. Success of website such as del.icio.us is a perfect illustration.

8 Clustering the data Geographic clustering The combination of map tools and search engine gives a unique capability of filtering your query based on geography. Clustering the data Once the concept tags are available, the engine provides concepts clustering to find documents with common themes Concept clustering Analogue clustering An extension of the concept clustering is the ability to regroup objects (well, reservoir, fracture ) based on similar characteristics and bring documents related to these similar objects. *PETREL is a mark of Schlumberger

9 Query Augmentation Ontology driven Using the ontology, all concepts linked to his search are presented and through selection/navigation, i he can narrow down his result set to get only what is the most relevant. Using the metadata tag, the user can narrow/expand his result set by selecting only certain value or range of values for certain tag (e.g. only document from 1995 to 1999 from this author). Dimension of relevance Query Augmentation Context Awareness By collecting information about the user, his location, his role, his step in the workflow the search can be restricted or sorted by what the system will think the most relevant. Through learning system, this knowledge can be gathered on the fly when the users are doing their searches. Wordnet 2010 The Trustees of Princeton University

10 Classifying The Data: Meta Data Meta Data is data about data Time Author Depth Coordinate Lithology

11 Classifying The Data: Social Tagging

12 Classifying The Data: Automatic Classification 1- Build your Ontology 2- Semantic based classification 3- probabilistic classifier - Naives Bayes, Hidden Markov Models, Custom Random Field

13 Resources Clustering Geographical Clustering Ontology Clustering Analogue Clustering *PETREL is a mark of Schlumberger

14 Query Augmentation: Ontology based Analyze the query: Wordnet Narrowing: remove ambiguity & refine query Expanding: spread activation visit neighbor concepts 2010 The Trustees of Princeton University

15 Query Augmentation: Context Aware T Transparently l adding ddi context iinformation f i to make k the h searchh results l more relevant l

16 Query Augmentation: Refinement

17 Wolfram Alpha: a new look at your information 2010 Wolfram Alpha LLC A Wolfram Research Company

18 Conclusion The Technology exists to enable the new generation of Information Management Increases Performance Reduces Risk Improves Confidence