1,370 research outputs found
From science to practice: Bringing innovations to agronomy and forestry
The challenge of the work presented here is to make innovative research output in the agronomy and forestry domain accessible to end-users, so that it can be practically applied. We have developed an approach that consists of three key-elements: an ontology with domain knowledge, a set of documents that have been annotated and meta-annotated, and a system (ask-Valerie) that is based on a dialogue to represent the interaction between end user and system.<br/> We show that the dialogue-metaphor is a good way of modelling the interaction between user and system. The system helps the user in formulating his question and in answering it in a useful way. Meta-annotations of key-paragraphs in the document-base turn out to be relevant in assessing in one glance what the content of a document is. <br/> End-users are very enthusiastic about the possibilities that ask-Valerie offers them in translating scientific results to their own situation
Proof of Concept of Ontology-based Query Expansion on Financial Domain
Este trabajo presenta el uso de una ontologÃa en el dominio financiero para la expansión de consultas con el fin de mejorar los resultados de un sistema de recuperación de información (RI) financiera. Este sistema está compuesto por una ontologÃa y un Ãndice de Lucene que permite recuperación de conceptos identificados mediante procesamiento de lenguaje natural. Se ha llevado a cabo una evaluación con un conjunto limitado de consultas y los resultados indican que la ambigüedad sigue siendo un problema al expandir la consulta. En ocasiones, la elección de las entidades adecuadas a la hora de expandir las consultas (filtrando por sector, empresa, etc.) permite resolver esa ambigüedad.This paper explains the application of ontologies in financial domains to a query
expansion process. The final goal is to improve financial information retrieval effectiveness.
The system is composed of an ontology and a Lucene index that stores and retrieves natural
language concepts. An initial evaluation with a limited number of queries has been performed.
Obtained results show that ambiguity remains a problem when expanding a query. The filtering
of entities in the expansion process by selecting only companies or references to markets helps
in the reduction of ambiguity.Este trabajo ha sido parcialmente financiado por el proyecto Trendminer (EU FP7-ICT287863) , el proyecto Monnet (EU FP7-ICT 247176) y MA2VICMR (S2009/TIC-1542).Publicad
Natural language processing
Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems
BIM-GPT: a Prompt-Based Virtual Assistant Framework for BIM Information Retrieval
Efficient information retrieval (IR) from building information models (BIMs)
poses significant challenges due to the necessity for deep BIM knowledge or
extensive engineering efforts for automation. We introduce BIM-GPT, a
prompt-based virtual assistant (VA) framework integrating BIM and generative
pre-trained transformer (GPT) technologies to support NL-based IR. A prompt
manager and dynamic template generate prompts for GPT models, enabling
interpretation of NL queries, summarization of retrieved information, and
answering BIM-related questions. In tests on a BIM IR dataset, our approach
achieved 83.5% and 99.5% accuracy rates for classifying NL queries with no data
and 2% data incorporated in prompts, respectively. Additionally, we validated
the functionality of BIM-GPT through a VA prototype for a hospital building.
This research contributes to the development of effective and versatile VAs for
BIM IR in the construction industry, significantly enhancing BIM accessibility
and reducing engineering efforts and training data requirements for processing
NL queries.Comment: 35 pages, 15 figure
An ontology-aided, natural language-based approach for multi-constraint BIM model querying
Being able to efficiently retrieve the required building information is
critical for construction project stakeholders to carry out their engineering
and management activities. Natural language interface (NLI) systems are
emerging as a time and cost-effective way to query Building Information Models
(BIMs). However, the existing methods cannot logically combine different
constraints to perform fine-grained queries, dampening the usability of natural
language (NL)-based BIM queries. This paper presents a novel ontology-aided
semantic parser to automatically map natural language queries (NLQs) that
contain different attribute and relational constraints into computer-readable
codes for querying complex BIM models. First, a modular ontology was developed
to represent NL expressions of Industry Foundation Classes (IFC) concepts and
relationships, and was then populated with entities from target BIM models to
assimilate project-specific information. Hereafter, the ontology-aided semantic
parser progressively extracts concepts, relationships, and value restrictions
from NLQs to fully identify constraint conditions, resulting in standard SPARQL
queries with reasoning rules to successfully retrieve IFC-based BIM models. The
approach was evaluated based on 225 NLQs collected from BIM users, with a 91%
accuracy rate. Finally, a case study about the design-checking of a real-world
residential building demonstrates the practical value of the proposed approach
in the construction industry
Information retrieval from scientific abstract and citation databases: A query-by-documents approach based on Monte-Carlo sampling
The rapidly increasing amount of information and entries in abstract and citation databases steadily complicates the information retrieval task. In this study, a novel query-by-document approach using Monte-Carlo sampling of relevant keywords is presented. From a set of input documents (seed) keywords are extracted using TF-IDF and subsequently sampled to repeatedly construct queries to the database. The occurrence of returned documents is counted and serves as a proxy relevance metric. Two case studies based on the Scopus® database are used to demonstrate the method and its key advantages. No expert knowledge and human intervention is needed to construct the final search strings which reduces the human bias. The methods practicality is supported by the high re-retrieval of seed documents of 7/8 and 26/31 in high ranks in the two presented case studies.Peer ReviewedPostprint (author's final draft
Geospatial Semantics
Geospatial semantics is a broad field that involves a variety of research
areas. The term semantics refers to the meaning of things, and is in contrast
with the term syntactics. Accordingly, studies on geospatial semantics usually
focus on understanding the meaning of geographic entities as well as their
counterparts in the cognitive and digital world, such as cognitive geographic
concepts and digital gazetteers. Geospatial semantics can also facilitate the
design of geographic information systems (GIS) by enhancing the
interoperability of distributed systems and developing more intelligent
interfaces for user interactions. During the past years, a lot of research has
been conducted, approaching geospatial semantics from different perspectives,
using a variety of methods, and targeting different problems. Meanwhile, the
arrival of big geo data, especially the large amount of unstructured text data
on the Web, and the fast development of natural language processing methods
enable new research directions in geospatial semantics. This chapter,
therefore, provides a systematic review on the existing geospatial semantic
research. Six major research areas are identified and discussed, including
semantic interoperability, digital gazetteers, geographic information
retrieval, geospatial Semantic Web, place semantics, and cognitive geographic
concepts.Comment: Yingjie Hu (2017). Geospatial Semantics. In Bo Huang, Thomas J. Cova,
and Ming-Hsiang Tsou et al. (Eds): Comprehensive Geographic Information
Systems, Elsevier. Oxford, U
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