CBench

2021 ◽  
Vol 14 (8) ◽  
pp. 1325-1337
Author(s):  
Abdelghny Orogat ◽  
Isabelle Liu ◽  
Ahmed El-Roby
Keyword(s):  
Structural Properties ◽  
Information Needs ◽  
Question Answering ◽  
Query Language ◽  
Small Subset ◽  
Question Answering System ◽  
Fine Grained ◽  
Question Answering Systems ◽  
Knowledge Graphs ◽  
Single Number

Recently, there has been an increase in the number of knowledge graphs that can be only queried by experts. However, describing questions using structured queries is not straightforward for non-expert users who need to have sufficient knowledge about both the vocabulary and the structure of the queried knowledge graph, as well as the syntax of the structured query language used to describe the user's information needs. The most popular approach introduced to overcome the aforementioned challenges is to use natural language to query these knowledge graphs. Although several question answering benchmarks can be used to evaluate question-answering systems over a number of popular knowledge graphs, choosing a benchmark to accurately assess the quality of a question answering system is a challenging task. In this paper, we introduce CBench, an extensible, and more informative benchmarking suite for analyzing benchmarks and evaluating question answering systems. CBench can be used to analyze existing benchmarks with respect to several fine-grained linguistic, syntactic, and structural properties of the questions and queries in the benchmark. We show that existing benchmarks vary significantly with respect to these properties deeming choosing a small subset of them unreliable in evaluating QA systems. Until further research improves the quality and comprehensiveness of benchmarks, CBench can be used to facilitate this evaluation using a set of popular benchmarks that can be augmented with other user-provided benchmarks. CBench not only evaluates a question answering system based on popular single-number metrics but also gives a detailed analysis of the linguistic, syntactic, and structural properties of answered and unanswered questions to better help the developers of question answering systems to better understand where their system excels and where it struggles.

scholarly journals MQALD: Evaluating the impact of modifiers in question answering over knowledge graphs

Semantic Web ◽  
2021 ◽  
pp. 1-17
Author(s):  
Lucia Siciliani ◽  
Pierpaolo Basile ◽  
Pasquale Lops ◽  
Giovanni Semeraro
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Query Language ◽  
State Of The Art ◽  
Translation Process ◽  
Specific Data ◽  
Data Query ◽  
Question Answering Systems ◽  
Knowledge Graphs ◽  
The Impact

Question Answering (QA) over Knowledge Graphs (KG) aims to develop a system that is capable of answering users’ questions using the information coming from one or multiple Knowledge Graphs, like DBpedia, Wikidata, and so on. Question Answering systems need to translate the user’s question, written using natural language, into a query formulated through a specific data query language that is compliant with the underlying KG. This translation process is already non-trivial when trying to answer simple questions that involve a single triple pattern. It becomes even more troublesome when trying to cope with questions that require modifiers in the final query, i.e., aggregate functions, query forms, and so on. The attention over this last aspect is growing but has never been thoroughly addressed by the existing literature. Starting from the latest advances in this field, we want to further step in this direction. This work aims to provide a publicly available dataset designed for evaluating the performance of a QA system in translating articulated questions into a specific data query language. This dataset has also been used to evaluate three QA systems available at the state of the art.

BUILD KNOWLEDGE GRAPH FROM HETEROGENEOUS DOCUMENTS

2021 ◽  
Vol 47 (05) ◽  
Author(s):  
NGUYỄN CHÍ HIẾU
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Semantic Analysis ◽  
Knowledge Graph ◽  
Question Answering Systems ◽  
Knowledge Graphs

Knowledge Graphs are applied in many fields such as search engines, semantic analysis, and question answering in recent years. However, there are many obstacles for building knowledge graphs as methodologies, data and tools. This paper introduces a novel methodology to build knowledge graph from heterogeneous documents.  We use the methodologies of Natural Language Processing and deep learning to build this graph. The knowledge graph can use in Question answering systems and Information retrieval especially in Computing domain

Toward Automatic Answers in User-Interactive Question Answering Systems

2013 ◽  
pp. 88-101
Author(s):  
Tianyong Hao ◽  
Feifei Xu ◽  
Jingsheng Lei ◽  
Liu Wenyin ◽  
Qing Li
Keyword(s):  
Pattern Matching ◽  
Similar Pattern ◽  
Question Answering ◽  
Semantic Representation ◽  
Pattern Generation ◽  
Interactive Systems ◽  
Question Answering System ◽  
Semantic Annotations ◽  
Question Answering Systems ◽  
Interactive Question

A strategy of automatic answer retrieval for repeated or similar questions in user-interactive systems by employing semantic question patterns is proposed in this paper. The used semantic question pattern is a generalized representation of a group of questions with both similar structure and relevant semantics. Specifically, it consists of semantic annotations (or constraints) for the variable components in the pattern and hence enhances the semantic representation and greatly reduces the ambiguity of a question instance when asked by a user using such pattern. The proposed method consists of four major steps: structure processing, similar pattern matching and filtering, automatic pattern generation, question similarity evaluation and answer retrieval. Preliminary experiments in a real question answering system show a precision of more than 90% of the method.

scholarly journals Leveraging Domain Context for Question Answering Over Knowledge Graph

2019 ◽  
Vol 4 (4) ◽  
pp. 323-335 ◽  
Author(s):  
Peihao Tong ◽  
Qifan Zhang ◽  
Junjie Yao
Keyword(s):  
Information Needs ◽  
Question Answering ◽  
Knowledge Graph ◽  
New Approach ◽  
Rich Domain ◽  
Complex Information ◽  
Question And Answer ◽  
Knowledge Graphs ◽  
The Rich ◽  
Matching Models

Abstract With the growing availability of different knowledge graphs in a variety of domains, question answering over knowledge graph (KG-QA) becomes a prevalent information retrieval approach. Current KG-QA methods usually resort to semantic parsing, search or neural matching models. However, they cannot well tackle increasingly long input questions and complex information needs. In this work, we propose a new KG-QA approach, leveraging the rich domain context in the knowledge graph. We incorporate the new approach with question and answer domain context descriptions. Specifically, for questions, we enrich them with users’ subsequent input questions within a session and expand the input question representation. For the candidate answers, we equip them with surrounding context structures, i.e., meta-paths within the targeting knowledge graph. On top of these, we design a cross-attention mechanism to improve the question and answer matching performance. An experimental study on real datasets verifies these improvements. The new approach is especially beneficial for specific knowledge graphs with complex questions.

scholarly journals BUILDING QUESTION ANSWERING SYSTEM BASED ON COMPUTING DOMAIN ONTOLOGY

2020 ◽  
Vol 38 (02) ◽  
Author(s):  
TẠ DUY CÔNG CHIẾN
Keyword(s):  
Language Processing ◽  
Digital Libraries ◽  
Question Answering ◽  
Domain Ontology ◽  
Text Documents ◽  
Question Answering System ◽  
Domain Specific ◽  
Sql Database ◽  
Question Answering Systems ◽  
Education Business

Question answering systems are applied to many different fields in recent years, such as education, business, and surveys. The purpose of these systems is to answer automatically the questions or queries of users about some problems. This paper introduces a question answering system is built based on a domain specific ontology. This ontology, which contains the data and the vocabularies related to the computing domain are built from text documents of the ACM Digital Libraries. Consequently, the system only answers the problems pertaining to the information technology domains such as database, network, machine learning, etc. We use the methodologies of Natural Language Processing and domain ontology to build this system. In order to increase performance, I use a graph database to store the computing ontology and apply no-SQL database for querying data of computing ontology.

scholarly journals Neural factoid geospatial question answering

2021 ◽  
pp. 65-90
Author(s):  
Haonan Li ◽  
Ehsan Hamzei ◽  
Ivan Majic ◽  
Hua Hua ◽  
Jochen Renz ◽  
...  
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Query Language ◽  
Graph Representation ◽  
Geospatial Information ◽  
Graph Representations ◽  
Semantic Graph ◽  
Complex Syntax ◽  
Question Answering Systems ◽  
Semantic Encoding

Existing question answering systems struggle to answer factoid questions when geospatial information is involved. This is because most systems cannot accurately detect the geospatial semantic elements from the natural language questions, or capture the semantic relationships between those elements. In this paper, we propose a geospatial semantic encoding schema and a semantic graph representation which captures the semantic relations and dependencies in geospatial questions. We demonstrate that our proposed graph representation approach aids in the translation from natural language to a formal, executable expression in a query language. To decrease the need for people to provide explanatory information as part of their question and make the translation fully automatic, we treat the semantic encoding of the question as a sequential tagging task, and the graph generation of the query as a semantic dependency parsing task. We apply neural network approaches to automatically encode the geospatial questions into spatial semantic graph representations. Compared with current template-based approaches, our method generalises to a broader range of questions, including those with complex syntax and semantics. Our proposed approach achieves better results on GeoData201 than existing methods.

Toward Automatic Answers in User-Interactive Question Answering Systems

2011 ◽  
Vol 3 (4) ◽  
pp. 52-66 ◽  
Author(s):  
Tianyong Hao ◽  
Feifei Xu ◽  
Jingsheng Lei ◽  
Liu Wenyin ◽  
Qing Li
Keyword(s):  
Pattern Matching ◽  
Similar Pattern ◽  
Question Answering ◽  
Semantic Representation ◽  
Pattern Generation ◽  
Interactive Systems ◽  
Question Answering System ◽  
Semantic Annotations ◽  
Question Answering Systems ◽  
Interactive Question

A strategy of automatic answer retrieval for repeated or similar questions in user-interactive systems by employing semantic question patterns is proposed in this paper. The used semantic question pattern is a generalized representation of a group of questions with both similar structure and relevant semantics. Specifically, it consists of semantic annotations (or constraints) for the variable components in the pattern and hence enhances the semantic representation and greatly reduces the ambiguity of a question instance when asked by a user using such pattern. The proposed method consists of four major steps: structure processing, similar pattern matching and filtering, automatic pattern generation, question similarity evaluation and answer retrieval. Preliminary experiments in a real question answering system show a precision of more than 90% of the method.

Effect of stud material and structural properties on the transmission loss of partitions

2021 ◽  
Vol 263 (3) ◽  
pp. 3888-3895
Author(s):  
Wayland Dong ◽  
John LoVerde ◽  
Benjamin Shafer ◽  
Lin Hu
Keyword(s):  
Structural Properties ◽  
Laboratory Testing ◽  
Transmission Loss ◽  
Small Subset ◽  
Wood Composite ◽  
Engineered Wood ◽  
Single Number

A common light frame wall design is gypsum wall board (GWB) cladding on each side of a row of studs. Steel studs are available in a variety of metal thicknesses and designs, and wood studs can be solid lumber or engineered wood composite studs with a variety of structural properties. Most published laboratory testing on these walls uses only a small subset of the available stud types, and the acoustical effect of changes to the stud parameters is not well understood. The authors and colleagues have performed several laboratory testing programs to systematically investigate the acoustical effects of stud properties, some of which were presented at Internoise 2020. This paper analyzes the effects of stud material and structural properties on third-octave transmission loss values and single-number ratings.

Complex answers: a case study using a WWW question answering system

2001 ◽  
Vol 7 (4) ◽  
pp. 301-323 ◽  
Author(s):  
S. BUCHHOLZ ◽  
W. DAELEMANS
Keyword(s):  
Question Answering ◽  
Question Answering System ◽  
Question Answering Systems ◽  
Using Data

We investigate the problem of complex answers in question answering. Complex answers consist of several simple answers. We describe the online question answering system SHAPAQA, and using data from this system we show that the problem of complex answers is quite common. We define nine types of complex questions, and suggest two approaches, based on answer frequencies, that allow question answering systems to tackle the problem.

scholarly journals Instructor-aided asynchronous question answering system for online education and distance learning

2012 ◽  
Vol 13 (5) ◽  
pp. 102 ◽  
Author(s):  
Dunwei Wen ◽  
John Cuzzola ◽  
Lorna Brown ◽  
Dr. Kinshuk
Keyword(s):  
Natural Language Processing ◽  
Distance Learning ◽  
Online Education ◽  
Language Processing ◽  
Question Answering ◽  
Prototype System ◽  
Learning Situation ◽  
Question Answering System ◽  
Question Answering Systems

Question answering systems have frequently been explored for educational use. However, their value was somewhat limited due to the quality of the answers returned to the student. Recent question answering (QA) research has started to incorporate deep natural language processing (NLP) in order to improve these answers. However, current NLP technology involves intensive computing and thus it is hard to meet the real-time demand of traditional search. This paper introduces a question answering (QA) system particularly suited for delayed-answered questions that are typical in certain asynchronous online and distance learning settings. We exploit the communication delay between student and instructor and propose a solution that integrates into an organization’s existing learning management system. We present how our system fits into an online and distance learning situation and how it can better assist supporting students. The prototype system and its running results show the perspective and potential of this research.<br /><br />

Sign in / Sign up

Export Citation Format

Share Document