Semantic question answering on big data

Tatu, Marta; Werner, Steven; Balakrishna, Mithun; Erekhinskaya, Tatiana; Moldovan, Dan

doi:10.1145/2928294.2928302

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…Keeping all components of the system up to date, particularly the ontology and the mapping, is still the responsibility of the administrators of the system and is performed manually. Tatu et al (2016) presented an approach for converting users natural language questions into SPARQL for querying and retrieving answers from an RDF store. Because the focus of their research is in transforming semantic structures identified in unstructured data sources (documents) to an RDF store that is accessible via natural language questions, the mapping of ontological concepts to (external) data sources is beyond the scope of their proposed framework.…”

Section: Ontology-based Applicationsmentioning

confidence: 99%

A web-based system architecture for ontology-based data integration in the domain of IT benchmarking

Pfaff

Krcmar

2017

Enterprise Information Systems

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In the domain of IT benchmarking (ITBM), a variety of data and information are collected. Although these data serve as the basis for business analyses, no unified semantic representation of such data yet exists. Consequently, data analysis across different distributed data sets and different benchmarks is almost impossible. This paper presents a system architecture and prototypical implementation for an integrated data management of distributed databases based on a domain-specific ontology. To preserve the semantic meaning of the data, the ITBM ontology is linked to data sources and functions as the central concept for database access. Thus, additional databases can be integrated by linking them to this domain-specific ontology and are directly available for further business analyses. Moreover, the web-based system supports the process of mapping ontology concepts to external databases by introducing a semi-automatic mapping recommender and by visualizing possible mapping candidates. The system also provides a natural language interface to easily query linked databases. The expected result of this ontologybased approach of knowledge representation and data access is an increase in knowledge and data sharing in this domain, which will enhance existing business analysis methods. ARTICLE HISTORY

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Section: Ontology-based Applicationsmentioning

confidence: 99%

A web-based system architecture for ontology-based data integration in the domain of IT benchmarking

Pfaff

Krcmar

2017

Enterprise Information Systems

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Response Selection and Automatic Message-Response Expansion in Retrieval-Based QA Systems using Semantic Dependency Pair Model

Huang

et al. 2018

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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This article presents an approach to response selection and message-response (MR) database expansion from the unstructured data on the psychological consultation websites for a retrieval-based question answering (QA) system in a constrained domain for emotional support and comforting. First, we manually construct an initial MR database based on the articles collected from the psychological consultation websites. The Chinese Knowledge and Information Processing probabilistic context-free grammar is adopted to obtain the semantic dependency graphs (SDGs) of all the messages and responses in the initial MR database. For each sentence in the MR database, all the semantic dependencies, each composed of two words and their semantic relation, are extracted from the SDG of the sentence to form a semantic dependency set. Finally, a matrix with the element representing the correlation between the semantic dependencies of the messages and their corresponding responses is constructed as a semantic dependency pair model (SDPM) for response selection. Moreover, as the number of MR pairs in the psychological consultation websites is increasing day by day, the MR database in the QA system should be expanded to meet the needs of the users. For MR database expansion, the unstructured data from the message board are automatically collected. For the collected data, the supervised latent Dirichlet allocation is adopted for event detection and then the event-based delta Bayesian Information Criterion is used for message and response article segmentation. Each extracted message segment is then fed to the constructed retrieval-based QA system to find the best matched response segment and the matching score is also estimated to verify if the new MR pair is suitable to be included in the expanded MR database. Fivefold cross validation was employed to evaluate the performance of the proposed retrieval-based QA system over the expanded MR database based on SDPM. Compared to the vector space model-based method, the Okapi BM25 model, and the deep learning-based sequence-to-sequence with attention model, the proposed approach achieved a more favorable performance according to a statistical significance test. The retrieval accuracy based on MR expansion was also evaluated and a satisfactory result was obtained confirming the effectiveness of the expanded MR database. In addition, the user's satisfaction score of the proposed system was evaluated using the Cronbach's alpha value and the satisfaction score of the proposed SDPM was higher than those of the methods for comparison.

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Answering Comparison in Indonesian Question Answering System with Database

Saelan¹,

Purwarianti²,

Widyantoro³

2018

IJEEI

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In this research, we build a question answering system that can answer comparative questions. Our system contains two components, question analysis and answer processing. In question analysis, we use information extraction method to extract entities, aspects, relations, and constants from the question. We also classify the question into five question types such as entity-mentioned, entity-other, entity-all, aspect, and yes/no. These processes in question analysis component are solved by machine learning technique. In answer processing component, we process the comparison by using word lists and rules. The result of comparison processing is used to find the answer by generating query to get relevant data from the database. The query generation process is performed by using rules based on the question type. After that, the data from database is also processed based on question type to generate the answer. Based on the experiment results, our proposed method for comparison question answering system is promising.

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Semantic question answering on big data

Cited by 4 publications

References 14 publications

A web-based system architecture for ontology-based data integration in the domain of IT benchmarking

A web-based system architecture for ontology-based data integration in the domain of IT benchmarking

Response Selection and Automatic Message-Response Expansion in Retrieval-Based QA Systems using Semantic Dependency Pair Model

Answering Comparison in Indonesian Question Answering System with Database

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