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

Pazos, A R Rodolfo; González, J B Juan; Aguirre, A L Marco; Martínez, Alicia; Fraire, J H Héctor

doi:10.1007/978-3-642-33021-6_36

Cited by 28 publications

(15 citation statements)

References 20 publications

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“…Androutsopoulos et al [5] give an introduction to the research area. Pazos R. et al [6] review the state of the art. In the following, we briefly present representatives for common approaches.…”

Section: Natural Language Interfaces To Databasesmentioning

confidence: 99%

Automatic Generation of Virtual Assistants from Databases using Active Ontologies

Blersch¹,

Weigelt²,

Tichy³

et al. 2019

International Conferences on Software Engineering and Knowledge Engineering

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Virtual assistants such as Siri or Google Assistant are omnipresent. However, their development remains costly. One must either manually model the problem domain or provide thousands of labeled samples.We propose to automatically create virtual assistants based on Active Ontologies for interacting with databases. Our approach generates Active Ontologies; we use the database structure to derive a concept hierarchy and database values together with synonyms to extract information from user queries. Our approach also learns common phrases from samples, e.g. from existing Dialogflow agents. We extract pre-and postfixes and attach them to concepts, e.g. at to detect a succeeding location. The generated Active Ontologies reply to previously unseen and composed requests. The approach is not limited to virtual assistants but can be applied to any system with a textual or voice-based conversational interface such as chatbots.We evaluate our approach in three domains: tourism, hotel, and web cams. The study shows that automatically generated Active Ontologies extract relevant information from user utterances with a precision of 58%. The precision increases to 79% (recall 46%, F1 58%) when we use sample utterances. Our approach successfully transfers between domains, e.g. we learn phrases from the tourism domain and use them to reply to hotel requests without any adjustments.

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Section: Natural Language Interfaces To Databasesmentioning

confidence: 99%

Automatic Generation of Virtual Assistants from Databases using Active Ontologies

Blersch¹,

Weigelt²,

Tichy³

et al. 2019

International Conferences on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

show abstract

“…Although there exist other ways of classification, Natural Language Interfaces to DataBases systems (NLIDBs) are usually classified according to their architecture (Androutsopoulos et al, 1995;Pazos R. et al, 2013). In this way, we can find four types of systems: pattern matching, syntax-based, semantic grammars, and intermediate representation languages.…”

Section: Technologies and Architecturesmentioning

confidence: 99%

“…Taking the GeoBase 13 database and considering the question "Which river passes through Illinois?" adapted from Pazos R. et al (2013), using a lexicon and some grammar rules, the system could make the syntactic tree shown in Figure 2a. This parse tree would be mapped to the logical query ?…”

Section: Syntax Based Systemsmentioning

confidence: 99%

“…For example, if we consider the same question as before, "Which river passes through Illinois? ", it could be parsed into the semantic tree that appears in Figure 2b (Pazos R. et al, 2013). As we can see, this semantic tree is shallower and easier to understand and process than the corresponding syntactic tree of Figure 2a.…”

Section: Semantic Grammar Systemsmentioning

confidence: 99%

“…Some authors (Pazos R. et al, 2013) state that a good natural language interface to databases (NLIDB) system should: be easy to configure and use; include tools for modifying the knowledge; make its capabilities and limitations evident to users; offer recommendations sufficiently justified; be robust in case of possible failure; answer quickly and with accuracy; answer deductive, temporal and fuzzy queries; be multimodal; be independent from the domain, the database management system, the language, the hardware and the software; handle linguistic phenomena (e.g. anaphora, ellipsis, ambiguity, or incomplete search values).…”

Section: Smartseller Our Proposalmentioning

confidence: 99%

See 2 more Smart Citations

A multi-agent conversational system with heterogeneous data sources access

Eisman

Navarro

Castro

2016

Expert Systems with Applications

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In many of the problems that can be found nowadays, information is scattered across different heterogeneous data sources. Most of the natural language interfaces just focus on a very specific part of the problem (e.g. an interface to a relational database, or an interface to an ontology). However, from the point of view of users, it does not matter where the information is stored, they just want to get the knowledge in an integrated, transparent, efficient, effective, and pleasant way. To solve this problem, this article proposes a generic multi-agent conversational architecture that follows the divide and conquer philosophy and considers two different types of agents. Expert agents are specialized in accessing different knowledge sources, and decision agents coordinate them to provide a coherent final answer to the user. This architecture has been used to design and implement SmartSeller, a specific system which includes a Virtual Assistant to answer general questions and a Bookseller to query a book database. A deep analysis regarding other relevant systems has demonstrated that our proposal provides several improvements at some key features presented along the paper.

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New Intelligent Tools to Adapt NL Interface to Corporate Environments

Чуприна

Postanogov

2019

Lecture Notes in Computer Science

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

Cited by 28 publications

References 20 publications

Automatic Generation of Virtual Assistants from Databases using Active Ontologies

Automatic Generation of Virtual Assistants from Databases using Active Ontologies

A multi-agent conversational system with heterogeneous data sources access

New Intelligent Tools to Adapt NL Interface to Corporate Environments

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