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Query Understanding for Natural Language Enterprise Search [article]

Francisco Borges, Georgios Balikas, Marc Brette, Guillaume Kempf, Arvind Srikantan, Matthieu Landos, Darya Brazouskaya, Qianqian Shi
2020 arXiv   pre-print
Natural Language Search (NLS) extends the capabilities of search engines that perform keyword search allowing users to issue queries in a more "natural" language.  ...  The engine tries to understand the meaning of the queries and to map the query words to the symbols it supports like Persons, Organizations, Time Expressions etc..  ...  ACKNOWLEDGEMENTS We would like to thank Ahmet Bugdayci, Anmol Bhasin, Christian Posse, Dylan Hingey, Ghislain Brun, Mario Rodriguez, Paulo Gomes, Rohit Kapoor and Sam Edwards for their support, help and  ... 
arXiv:2012.06238v1 fatcat:t5ck2jmaqngexgmgoylpoqt3ua

Querying Enterprise Knowledge Graph With Natural Language

Junyi Chai, Yonggang Deng, Maochen Guan, Yujie He, Bing Li, Rui Yan
2019 International Semantic Web Conference  
The natural language restatement component finally wraps up all of the Yugen's natural language understanding and query results, and returns users a human-readable answer.  ...  It benefits enterprise users by lowering the cost of training employees to learn specified query languages, understanding enterprise domain queries, providing query answers, and explaining the results  ... 
dblp:conf/semweb/ChaiDGHLY19 fatcat:lfrurui2pja4la3iluyymvjb64

Proposal for Using NLP Interchange Format for Question Answering in Organizations

Majid Latifi
2013 International Web Rule Symposium  
The most sophisticated ones provide a simple text box for a query which takes Natural Language (NL) queries as input.  ...  Natural language processing technique is mostly implemented in QA system for asking user"s question and several steps are also followed for conversion of questions to query form for getting an exact answer  ...  Miquel Sànchez-Marrè for his helpful comments and guidance. I acknowledge the financial support of the Generalitat de Catalunya through the AGAUR agency for Consolidated Research Groups.  ... 
dblp:conf/ruleml/Latifi13 fatcat:h447vnbwfvh5ppbb6fykg72shy

LPar – A Distributed Multi Agent platform for building Polyglot, Omni Channel and Industrial grade Natural Language Interfaces [article]

Pranav Sharma
2020 arXiv   pre-print
Last few years, have seen huge progress in Natural Language Processing domain which has led to deployments of conversational agents in many enterprises.  ...  There are also challenges in seamlessly leveraging many tools offered by sub fields of Natural Language Processing and Information Retrieval in a single solution.  ...  Introduction Natural Language Queries from customers of any large Enterprise are very diverse and complex.  ... 
arXiv:2006.14666v1 fatcat:db3jkn62jfadnpzndqxshzi4h4

AI based Chatbot for Human Assistance

Sanchit Singhal, Vatsal Garg, Osho Garg, Prabhat Singh, Harsh Khatter
2020 Zenodo  
Chatbot helps to resolve the queries and respond to the questions of users.  ...  implementation of Artificial Intelligence technology which is used to interact with the human beings and make them feel like they are taking to the real person and the chatbot helps them to solve their queries  ...  The Natural Language Processing is used to understand the human text and speech recognition can be used to understand the speech dialog.  ... 
doi:10.5281/zenodo.4743741 fatcat:bcrm7liaqbcm3mlj7u2ndklxve

A Domain and Language Construct based Mapping to Convert Natural Language Query to SQL

Alka Malik, Rahul Rishi
2015 International Journal of Computer Applications  
The system provide the robustness in terms to handle the broader range of user queries and is be implemented in java environment on Enterprise Employee Database.  ...  Database management systems (DBMS) have been widely used for storing and retrieving data. However, databases are often hard to use since their interface is quite rigid in cooperating with users.  ...  Natural language based query interface accept the query sentence and try to understand it by applying lexicon, syntactic and semantic analysis and then converts into SQL.  ... 
doi:10.5120/20325-2444 fatcat:vdjjmag3ajhjpougqjkguopc7a

A SEMANTIC FRAMEWORK FOR GRAPH-BASED ENTERPRISE SEARCH

Gianfranco E. MODONI, Marco SACCO, Walter TERKAJ
2014 Applied Computer Science  
This paper introduces an envisioned architecture, which should represent the foundations of a new generation of tools for searching information within enterprises.  ...  Various recent studies have shown that in many companies workers can spend near half of their time looking for information. Effective internal search tools could make their job more efficient.  ...  language queries rather than a list of links.  ... 
doaj:4b8f78ce655344e89c7e8f7b51e9e4f1 fatcat:4s3g3z4lkrbpfcdasj7anufl34

Ontology-Based Knowledge Elicitation: An Architecture [chapter]

Marcello Montedoro, Giorgio Orsi, Licia Sbattella, Roberto Tedesco
2012 Lecture Notes in Computer Science  
This chapter overviews the process of collection and automatic analysis of data and documents both inside and outside the Networked Enterprise.  ...  We will address the following research problems: discovery of the useful information sources, in terms of the enterprise documentation, of structured and unstructured data provided by existing information  ...  Notice that, as a further advantage of the concept-based queries with respect to traditional word-based engines, the search is multi-language in nature: the language used to search for documents does not  ... 
doi:10.1007/978-3-642-31739-2_9 fatcat:tl4ekbhrufcsvie2qrzyiatrua

Improving Enterprise Wide Search in Large Engineering Multinationals: A Linguistic Comparison of the Structures of Internet-Search and Enterprise-Search Queries [chapter]

David Edward Jones, Yifan Xie, Chris McMahon, Marting Dotter, Nicolas Chanchevrier, Ben Hicks
2016 IFIP Advances in Information and Communication Technology  
This paper presents part-of-speech (POS) statistical analysis on two sets of 'Top 500' search query lists in order to compare Internet search with enterprise search with the aim of understanding how enterprise  ...  This compares to 89% for Internet users. 60% of enterprise queries are single nouns compared to 38% for Internet search users.  ...  The Authors would like to thank colleagues at Airbus and the University of Bristol for support and contribution.  ... 
doi:10.1007/978-3-319-33111-9_20 fatcat:jpps5tyl45ghzd3kwvuw3qc2qa

From IR to Search, and Beyond

Ramana Rao
2004 Queue  
While Boolean matching is conceptually straightforward with structured tables of relational data, it's a completely different matter for documents expressed in richly-structured natural language.  ...  Usually, the user doesn't fully understand their own information need in advance, or else, he can't express the need in a manner suitable for the system to process.  ...  to overcome the full challenge of natural language understanding by machines.  ... 
doi:10.1145/1005062.1005070 fatcat:2p6hzdjc7ncivkn43ebtw4h4eq

Context Sensitive Entity Linking of Search Queries in Enterprise Knowledge Graphs [chapter]

Sumit Bhatia, Anshu Jain
2016 Lecture Notes in Computer Science  
Fast and correct identification of named entities in queries is crucial for query understanding and to map the query to information in structured knowledge base.  ...  Most of the existing work have focused on utilizing search logs and manually curated knowledge bases for entity linking and often involve complex graph operations and are generally slow.  ...  Entity Disambiguation accuracy Conclusions In this paper, we addressed the problem of mapping entity mentions in natural language search queries to corresponding entities in an automatically constructed  ... 
doi:10.1007/978-3-319-47602-5_11 fatcat:if7g33nmfjcapjlazqoeunmahe

Anu Question Answering System

Balaji Ganesan, Avirup Saha, Jaydeep Sen, Matheen Ahmed Pasha, Sumit Bhatia, Arvind Agarwal
2020 International Semantic Web Conference  
AnuQA is a question answering system built on top of a search index and an enterprise knowledge graph.  ...  interface for business analytics.  ...  Reasoning for Natural Language Interpretation Natural Language Query [4] interfaces allow end-users to ask questions without knowing any specialized query language or data storage and schema details.  ... 
dblp:conf/semweb/GanesanSSPBA20 fatcat:fh6g3y56zjfdhavvwehhqgsqtu

Semantic Web in the Age of Big Data: A Perspective: Will We Drown in a Data Tsunami or Enter a Knowledge Utopia?

Syed Ahmad Chan Bukhari, Ali Kashif Bashir, Khalid Mahmood Malik
2018 IEEE Technology Policy and Ethics  
Facebook introduced a semantic search engine with a natural language querying interface [22] .  ...  Querying language SPARQL 1.1 [35] . Interfaces and accessing protocols recently have started supporting for distributed processing.  ... 
doi:10.1109/ntpe.2018.9778122 fatcat:xdmehziewrbjpdhaskm4ptpznm

Natural disasters warning for enterprises through fuzzy keywords search

Zewei Sun, Hanwen Liu, Chao Yan, Ran An
2021 Tsinghua Science and Technology  
the contents of the natural disasters warning through searching for necessary text documents.  ...  With the ever-increasing number of natural disasters warning documents in document databases, the document database is becoming an economic and efficient way for enterprise staffs to learn and understand  ...  .: Natural Disasters Warning for Enterprises Through Fuzzy Keywords Search 559  ... 
doi:10.26599/tst.2020.9010027 fatcat:mg4hht7vivdqrmytkts227z2dy

Enterprise search in the big data era

Yunyao Li, Ziyang Liu, Huaiyu Zhu
2014 Proceedings of the VLDB Endowment  
This tutorial presents an organized overview of these challenges and opportunities, and reviews the state-of-the-art techniques for building a reliable and high quality enterprise search engine, in the  ...  Enterprise search allows users in an enterprise to retrieve desired information through a simple search interface. It is widely viewed as an important productivity tool within an enterprise.  ...  natural language.  ... 
doi:10.14778/2733004.2733071 fatcat:k6zeenyokbfohfv7zaiza2d2hm
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