scholarly journals GeoIRIS: Geospatial Information Retrieval and Indexing System—Content Mining, Semantics Modeling, and Complex Queries

2007 ◽  
Vol 45 (4) ◽  
pp. 839-852 ◽  
Author(s):  
Chi-Ren Shyu ◽  
Matt Klaric ◽  
Grant J. Scott ◽  
Adrian S. Barb ◽  
Curt H. Davis ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Geospatial Information ◽  
Complex Queries ◽  
Indexing System ◽  
Content Mining

scholarly journals GeoFairy: Towards a one-stop and location based Service for Geospatial Information Retrieval

2017 ◽  
Vol 62 ◽  
pp. 156-167 ◽  
Author(s):  
Ziheng Sun ◽  
Liping Di ◽  
Gil Heo ◽  
Chen Zhang ◽  
Hui Fang ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Location Based Service ◽  
Geospatial Information ◽  
One Stop

Developing Ontologies for Engineering Information Retrieval

2007 ◽  
Author(s):  
Zhanjun Li ◽  
Victor Raskin ◽  
Karthik Ramani
Keyword(s):  
Information Retrieval ◽  
Statistical Methods ◽  
Preliminary Test ◽  
The Other ◽  
Product Lifecycle ◽  
Computational Framework ◽  
Complex Queries ◽  
Engineering Documents ◽  
Engineering Information ◽  
Standard Information

When engineering content is created and applied during the product lifecycle, it is often stored and forgotten. Since search remains text-based, engineers do not have the means to harness and reuse past designs and experiences. On the other hand, current information retrieval approaches based on statistical methods and keyword matching are not directly applicable to the engineering domain. We propose a new computational framework that includes an ontological basis and algorithms to retrieve unstructured engineering documents while handling complex queries. The results from the preliminary test demonstrate that our method outperforms the traditional keyword-based search with respect to the standard information retrieval measurement.

scholarly journals Hybrid Ontology for Semantic Information Retrieval Model Using Keyword Matching Indexing System

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
K. R. Uthayan ◽  
G. S. Anandha Mala
Keyword(s):  
Information Retrieval ◽  
Keyword Search ◽  
Predicate Logic ◽  
Relevant Information ◽  
Semantic Matching ◽  
Matching Process ◽  
Executive Process ◽  
Indexing System ◽  
Primary Order ◽  
Information Domain

Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology.

The testing of relational indexing procedures by diagnostic computer programs

1980 ◽  
Vol 2 (6) ◽  
pp. 285-297 ◽  
Author(s):  
J. Farradane ◽  
D. Thompson
Keyword(s):  
Information Retrieval ◽  
Computer Programs ◽  
On Demand ◽  
Indexing System ◽  
Initial Results ◽  
Diagnostic Feedback

Information retrieval in a relational indexing system has been tested by means of a suite of computer programs which will carry out searches in a variety of ways, with, on demand, detailed diagnostic feedback at any stage. The performance of the system can thus be evaluated as a 'system' separately from user judgments of the output. Details of the programming are described. Some initial results are discussed with respect to strengths and possible weaknesses of the system.

Developing Engineering Ontology for Information Retrieval

2008 ◽  
Vol 8 (1) ◽  
Author(s):  
Zhanjun Li ◽  
Victor Raskin ◽  
Karthik Ramani
Keyword(s):  
Information Retrieval ◽  
Life Cycle ◽  
Statistical Methods ◽  
Product Life Cycle ◽  
Effective Means ◽  
Preliminary Test ◽  
Computational Framework ◽  
Complex Queries ◽  
Engineering Documents ◽  
Standard Information

When engineering content is created and applied during the product life cycle, it is often stored and forgotten. Since search remains word based, engineers do not have the effective means to harness and reuse past designs and experiences. Current information retrieval approaches based on statistical methods and keyword matching do not satisfy users’ needs in the engineering domain. Therefore, we propose a new computational framework that includes an ontological basis and algorithms to retrieve unstructured engineering documents while handling complex queries. The results from the preliminary test demonstrate that our method outperforms the traditional keyword-based search with respect to the standard information retrieval measurement.

scholarly journals COVID-19 information retrieval with deep-learning based semantic search, question answering, and abstractive summarization

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Andre Esteva ◽  
Anuprit Kale ◽  
Romain Paulus ◽  
Kazuma Hashimoto ◽  
Wenpeng Yin ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Question Answering ◽  
Health Workers ◽  
Complex Queries ◽  
Global Pandemic ◽  
Multi Stage ◽  
Scientific Disciplines ◽  
Abstractive Summarization ◽  
Deep Learning Model

AbstractThe COVID-19 global pandemic has resulted in international efforts to understand, track, and mitigate the disease, yielding a significant corpus of COVID-19 and SARS-CoV-2-related publications across scientific disciplines. Throughout 2020, over 400,000 coronavirus-related publications have been collected through the COVID-19 Open Research Dataset. Here, we present CO-Search, a semantic, multi-stage, search engine designed to handle complex queries over the COVID-19 literature, potentially aiding overburdened health workers in finding scientific answers and avoiding misinformation during a time of crisis. CO-Search is built from two sequential parts: a hybrid semantic-keyword retriever, which takes an input query and returns a sorted list of the 1000 most relevant documents, and a re-ranker, which further orders them by relevance. The retriever is composed of a deep learning model (Siamese-BERT) that encodes query-level meaning, along with two keyword-based models (BM25, TF-IDF) that emphasize the most important words of a query. The re-ranker assigns a relevance score to each document, computed from the outputs of (1) a question–answering module which gauges how much each document answers the query, and (2) an abstractive summarization module which determines how well a query matches a generated summary of the document. To account for the relatively limited dataset, we develop a text augmentation technique which splits the documents into pairs of paragraphs and the citations contained in them, creating millions of (citation title, paragraph) tuples for training the retriever. We evaluate our system (http://einstein.ai/covid) on the data of the TREC-COVID information retrieval challenge, obtaining strong performance across multiple key information retrieval metrics.

Intelligent Geospatial Information Retrieval for Agricultural Experts—A Knowledge-Based Method

2010 ◽  
Vol 8 (1) ◽  
pp. 178-183 ◽  
Author(s):  
Jian Wang ◽  
Bing-Bo Gao ◽  
Zhi-Qiang Wang ◽  
Fang-Qu Niu ◽  
Meng Yu
Keyword(s):  
Information Retrieval ◽  
Geospatial Information ◽  
Knowledge Based
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