Natural Language Interfaces to Ontologies: usability and performance (Transfer report) (original) (raw)

A text-based query interface to owl ontologies

2008

Abstract Accessing structured data in the form of ontologies requires training and learning formal query languages (eg, SeRQL or SPARQL) which poses significant difficulties for non-expert users. One of the ways to lower the learning overhead and make ontology queries more straightforward is through a Natural Language Interface (NLI). While there are existing NLIs to structured data with reasonable performance, they tend to require expensive customisation to each new domain or ontology.

FREyA: An interactive way of querying Linked Data using natural language

2012

Natural Language Interfaces are increasingly relevant for information systems fronting rich structured data stores such as RDF and OWL repositories, mainly because of the conception of them being intuitive for human. In the previous work, we developed FREyA, an interactive Natural Language Interface for querying ontologies. It uses syntactic parsing in combination with the ontology-based lookup in order to interpret the question, and involves the user if necessary.

Improving the customization of natural language interface to databases using an ontology

2007

Natural language interfaces to databases are considered one of the best alternatives for final users who wish to make complex, uncommon and frequent queries, which is a very common need in organizations. The use of this type of interfaces has been very limited, due to their limited publicizing and the complexity to adapt them to users' needs, and because their precision varies widely.

Natural Language Interfaces to Conceptual Models

2011

Abstract Accessing structured data in the form of ontologies currently requires the use of formal query languages (eg, SeRQL or SPARQL) which pose significant difficulties for non-expert users. One way to lower the learning overhead and make ontology queries more straightforward is through a Natural Language Interface (NLI). While there are existing NLIs to structured data with reasonable performance, they tend to require expensive customisation to each new domain.

Ontology Based Queries–Investigating a Natural Language Interface

2010

ABSTRACT In this paper we look at what may be learned from a comparative study examining non-technical users with a background in social science browsing and querying metadata. Four query tasks were carried out with a natural language interface and with an interface that uses a web paradigm with hyperlinks.

Ontology Based Natural Language Interface for Relational Databases

Procedia Computer Science, 2016

Developing Natural Language Query Interface to Relational Databases has gained much interest in research community since forty years. This can be termed as structured free query interface as it allows the users to retrieve the data from the database without knowing the underlying schema. Structured free query interface should address majorly two problems. Querying the system with Natural Language Interfaces (NLIs) is comfortable for the naive users but it is difficult for the machine to understand. The other problem is that the users can query the system with different expressions to retrieve the same information. The different words used in the query can have same meaning and also the same word can have multiple meanings. Hence it is the responsibility of the NLI to understand the exact meaning of the word in the particular context. In this paper, a generic NLI Database system has proposed which contains various phases. The exact meaning of the word used in the query in particular context is obtained using ontology constructed for customer database. The proposed system is evaluated using customer database with precision, recall and f1-measure.

The history and recent advances of Natural Language Interfaces for Databases Querying

2021

Databases have been always the most important topic in the study of information systems, and an indispensable tool in all information management systems. However, the extraction of information stored in these databases is generally carried out using queries expressed in a computer language, such as SQL (Structured Query Language). This generally has the effect of limiting the number of potential users, in particular non-expert database users who must know the database structure to write such requests. One solution to this problem is to use Natural Language Interface (NLI), to communicate with the database, which is the easiest way to get information. So, the appearance of Natural Language Interfaces for Databases (NLIDB) is becoming a real need and an ambitious goal to translate the user’s query given in Natural Language (NL) into the corresponding one in Database Query Language (DBQL). This article provides an overview of the state of the art of Natural Language Interfaces as well ...

Ontology-Based Natural Language Query Interfaces for Data Exploration

2018

Enterprises are creating domain-specific knowledge bases by curating and integrating all their business data, structured, unstructured and semi-structured, and using them in enterprise applications to derive better business decisions. One distinct characteristic of these enterprise knowledge bases, compared to the open-domain general purpose knowledge bases like DBpedia [16] and Freebase [6], is their deep domain specialization. This deep domain understanding empowers many applications in various domains, such as health care and finance. Exploring such knowledge bases, and operational data stores requires different querying capabilities. In addition to search, these databases also require very precise structured queries, including aggregations, as well as complex graph queries to understand the various relationships between various entities of the domain. For example, in a financial knowledge base, users may want to find out “which startups raised the most VC funding in the first qu...

Natural Language Interfaces to Databases: An Analysis of the State of the Art

People constantly make decisions based on information, most of which is stored in databases. Accessing this information requires the use of query languages to databases such as SQL. In order to avoid the difficulty of using these languages for users who are not computing experts, Natural Language Interfaces for Databases (NLIDB) have been developed, which permit to query databases through queries formulated in natural language. Although since the 60s many NLIDBs have been developed, their performance has not been satisfactory, there still remain very difficult problems that have not been solved by NLIDB technology, and there does not yet exist a standardized method of evaluation that permits to compare the performance of different NLIDBs. This chapter presents an analysis of NLIDBs, which includes their classification, techniques, advantages, disadvantages, and a proposal for a proper evaluation of them.