Addressing ontology-based question answering with collections of user queries

Ferrández, íscar; Izquierdo, Rubén; Ferrández, Sergio; Vicedo, Jose-Luis

doi:10.1016/j.ipm.2008.09.001

Cited by 48 publications

(34 citation statements)

References 9 publications

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“…PowerAqua [19] supports users in querying and exploring the multiple semantic web resources. QACID [8] is an ontology-based Question Answering system implemented in the CInema Domain. The system permits users to retrieve information from prescribed ontologies by expending as input queries articulated in natural language.…”

Section: Related Workmentioning

confidence: 99%

Cognitive Functionality based Question Answering System

Chandiok¹,

Chaturvedi²

2018

IJCA

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Applying cognitive functionality in the artificial system is the prospect of next computing era and is speedily attracting the business and industry. As the volume of information increasing rapidly in the digital world, it also tends to challenge the current search engines to mine more significant and related information in precise and comprehensible manner. A Question Answering (QA) system outdoes the traditional information retrieval search engines in such situations. The paper presents and implements cognitive functionality in question answering systems to mimic human-like performance. The work proposes an architecture which trails human mental and brain like problemsolving procedures to answer questions. An illustration also deliberated and elucidated using a practical implementation and evaluation along with its fundamental cognitive functionality.

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Section: Related Workmentioning

confidence: 99%

Cognitive Functionality based Question Answering System

Chandiok¹,

Chaturvedi²

2018

IJCA

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“…Some were built for a specific domain (e.g. [5]), others are domain independent [9]. The dominant approach is to transform a user's keyword query to a formal semantic query by matching query segments to triples from the knowledge base [3,9,18]).…”

Section: Related Workmentioning

confidence: 99%

“…SemSearch 2011 4 was a research challenge designed for the ad-hoc object retrieval from semantic data, triple collection (namely, Billion Triple Challenge 2009 data set (BTC) 5 ). The list search track of SemSearch 2011 challenge has been especially designed for the task of answering keyword semantic type queries from a collection of triples and as such, was an obvious choice for the evaluation of the SemSets model.…”

Section: Semsearch 2011 Data Setmentioning

confidence: 99%

The SemSets model for ad-hoc semantic list search

Ciglan

Nørvåg

Hluchý

2012

Proceedings of the 21st International Conference on World Wide Web

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The amount of semantic data on the web has been growing rapidly in recent years. One of the key challenges triggered by this growth is the ad-hoc querying, i.e., the ability to retrieve answers from semantic resources using natural language queries. This facilitates interaction with semantic resources for the users so they can benefit from the knowledge covered by semantic data without the complexities of semantic query languages. In this paper, we focus on semantic queries, where the aim is to retrieve objects belonging to a set of semantically related entities. An example of such an adhoc type query is "Apollo astronauts who walked on the Moon". In order to address the task, we propose the SemSets retrieval model that exploits and combines traditional document-based information retrieval, link structure of the semantic data and entity membership in semantic sets, in order to provide the answers. The novelty of the approach lies in the utilization of semantic sets, i.e., groups of semantically related entities. We propose two approaches to identify such semantic sets from the knowledge bases; the first one requires involvement of an expert user knowledgeable of the data set structure, the second one is fully automatic and provides results that are comparable with those delivered by the expert users. As demonstrated in the experimental evaluation, the proposed model has the state-of-the-art performance on the SemSearch2011 data set, which has been designed especially for the semantic list search evaluation.

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“…Ontology (Fernandez et al, 2009) is a technology used to enable the domain knowledge at a high level and improve the query time used in Question Answering system. Related work: PANTO (Wang et al, 2007) model a Portable nAtural laNguage inTerface to Ontologies which accepts input as natural language form and the output is in SPARQL query.…”

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confidence: 99%

“…QACID (Ferrandez et al, 2009) is based on collection of queries from a given domain which are analyzed and grouped as clusters and those are manually annotated using SPARQL queries. Each query is considered as bag of words, mapping between words in NL queries into KB by using string distance metrics.…”

mentioning

confidence: 99%

Comparison of Question Answering Systems Based on Ontology and Semantic Web in Different Environment

S.¹

2012

Journal of Computer Science

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Problem statement: Question Answering (QA) system is taking an important role in current search engine optimization concept. 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. Approach: This paper surveys different types of question answering system based on ontology and semantic web model with different query format. For comparison, the types of input, query processing method, input and output format of each system and the performance metrics with its limitations are analyzed and discussed. Our question answering for automatic learning system architecture is used to overcome the difficulties raised from the different QA models. Results: The semantic search methodology is implemented by using RDF graph in the application of data structure domain and the performance is also analyzed. Answers are retrieved from ontology using Semantic Search approach and question-to-query algorithm is evaluated in our system for analyzing performance evaluation. Conclusion: Performance of question answering system of getting exact result can be improved by using semantic search methodology for retrieving answers from ontology model. Our system successfully implements this technique and the system is also used in intelligent manner for automatic learning method.Key words: Semantic web, ontology, RDF, semantic search, question answering system NTRODUCTIONThe Question Answering system plays a major role in current era. It is needed when the user gets an indepth knowledge in a particular domain. QA system is classified as two types namely closed domain or restricted domain and open domain model. In QA systems two types of search is available namely keywords based search and semantic search (Zhang, 2006). Normal search engines are working under keyword based searching concept. But some time, there is a problem of getting wrong answer for different meaning of same word. So, semantic search is used to solve the above problem.Semantic search is used to improve the accuracy of search by understanding the intent of the user and the meaning of the terms in the searching sentence. Mainly there are two search are available as namely Navigation Search and Research Search.In navigational search, the user is using the search engine as a navigation tool to navigate to a particular intended document. Semantic Search is not applicable to navigational searches. In Research Search, the user provides the search engine with a phrase which is intended to denote an object about which the user is trying to gather/research information. Rather than Google's PageRank algorithm, Semantic Search uses semantics to produce highly relevant searching results.This Semantic Search technique can be used to retrieve the knowledge from the data source like ontology. Ontology (Fernandez et al., 2009) is a technology used to enable the domain knowledge at a high level and improve the query time used in Questi...

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Addressing ontology-based question answering with collections of user queries

Cited by 48 publications

References 9 publications

Cognitive Functionality based Question Answering System

Cognitive Functionality based Question Answering System

The SemSets model for ad-hoc semantic list search

Comparison of Question Answering Systems Based on Ontology and Semantic Web in Different Environment

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