Modular Generation of Relational Query Paraphrases

Lecture Notes in Computer Science, 2012

This research concerns with translating natural language questions into SQL queries by exploiting the MySQL framework for both hypothesis construction and thesis verification in the task of question answering. We use linguistic dependencies and metadata to build sets of possible SELECT and WHERE clauses. Then we exploit again the metadata to build FROM clauses enriched with meaningful joins. Finally, we combine all the clauses to get the set of all possible SQL queries, producing an answer to the question. Our algorithm can be recursively applied to deal with complex questions, requiring nested SELECT instructions. Additionally, it proposes a weighting scheme to order all the generated queries in terms of probability of correctness. Our preliminary results are encouraging as they show that our system generates the right SQL query among the first five in the 88% of the cases. This result can be greatly improved by re-ranking the queries with a machine learning methods.

Proceedings of the 11th international conference on Extending database technology Advances in database technology - EDBT '08, 2008

In the classical database world, information access has been based on a paradigm that involves structured, schema-aware, queries and tabular answers. In the current environment, however, where information prevails in most activities of society, serving people, applications, and devices in dramatically increasing numbers, this paradigm has proved to be very limited. On the query side, much work has been done on moving towards keyword queries over structured data. In our previous work, we have touched the other side as well, and have proposed a paradigm that generates entire databases in response to keyword queries. In this paper, we continue in the same direction and propose synthesizing textual answers in response to queries of any kind over structured data. In particular, we study the transformation of a dynamically-generated logical database subset into a narrative through a customizable, extensible, and templatebased process. In doing so, we exploit the structured nature of database schemas and describe three generic translation modules for different formations in the schema, called unary, split, and join modules. We have implemented the proposed translation procedure into our own database front end and have performed several experiments evaluating the textual answers generated as several features and parameters of the system are varied. We have also conducted a set of experiments measuring the effectiveness of such answers on users. The overall results are very encouraging and indicate the promise that our approach has for several applications.

Lecture Notes in Computer Science, 2005

We present a method for creating natural language interfaces to databases (NLIDB) that allow for translating natural language queries into SQL. The method is domain independent, i.e., it avoids the tedious process of configuring the NLIDB for a given domain. We automatically generate the domain dictionary for query translation using semantic metadata of the database. Our semantic representation of a query is a graph including information from database metadata. The query is translated taking into account the parts of speech of its words (obtained with some linguistic processing). Specifically, unlike most existing NLIDBs, we take seriously auxiliary words (prepositions and conjunctions) as set theory operators, which allows for processing more complex queries. Experimental results (conducted on two Spanish databases from different domains) show that treatment of auxiliary words improves correctness of translation by 12.1%. With the developed NLIDB 82of queries were correctly translated (and thus answered). Reconfiguring the NLIDB from one domain to the other took only ten minutes.

Data & Knowledge Engineering, 2005

Proceedings of the ... AAAI Conference on Artificial Intelligence, 2011

Ontology-based data access is a powerful form of extending database technology, where a classical extensional database (EDB) is enhanced by an ontology that generates new intensional knowledge which may contribute to answer a query. Recently, the Datalog ± family of ontology languages was introduced; in Datalog ± , rules are tuple-generating dependencies (TGDs), i.e., Datalog rules with the possibility of having existentially-quantified variables in the head. In this paper we introduce a novel Datalog ± language, namely sticky sets of TGDs, which allows for a wide class of joins in the body, while enjoying at the same time a low query-answering complexity. We establish complexity results for answering conjunctive queries under sticky sets of TGDs, showing, in particular, that ontological conjunctive queries can be compiled into first-order and thus SQL queries over the given EDB instance. We also show some extensions of sticky sets of TGDs, and how functional dependencies and so-called negative constraints can be added to a sticky set of TGDs without increasing the complexity of query answering. Our language thus properly generalizes both classical database constraints and most widespread tractable description logics.

2005

A truly natural language interface to databases also needs to be practical for actual implementation. We developed a new feasible approach to solve the problem and tested it successfully in a laboratory environment. The new result is based on metadata search, where the metadata grow in largely linear manner and the search is linguistics-free (allowing for grammatically incorrect and incomplete input). A new class of reference dictionary integrates four types of enterprise metadata: enterprise information models, database values, user-words, and query cases. The layered and scalable information models allow user-words to stay in original forms as users articulated them, as opposed to relying on permutations of individual words contained in the original query. A graphical representation method turns the dictionary into searchable graphs representing all possible interpretations of the input. A branch-and-bound algorithm then identifies optimal interpretations, which lead to SQL implementation of the original queries. Query cases enhance both the metadata and the search of metadata, as well as providing casebased reasoning to directly answer the queries. This design assures feasible solutions at the termination of the search, even when the search is incomplete (i.e., the results contain the correct answer to the original query). The necessary condition is that the text input contains at least one entry in the reference dictionary. The sufficient condition is that the text input contains a set of entries corresponding to a complete, correct single SQL query. Laboratory testing shows that the system obtained accurate results for most cases that satisfied only the necessary condition.

This research concerns with translating natural language into SQL queries by exploiting the Perl DBI library for both database construction and thesis verification in the task of question answering. We built SQGNL which uses linguistic dependencies and metadata to build sets of possible SELECT and WHERE clauses and is designed to be database and platform independent with multiuser support. It can be used by users with no knowledge of SQL to translate natural language to SQL queries. The program has the ability to learn new grammar. SQGNL application is written in Perl language with a simple user interface implemented using Tk module. It uses Perl recursive descent module to build the underlying parser. Our algorithm can be recursively applied to deal with complex questions, requiring nested SELECT instructions. Our preliminary results are encouraging as they show that our system generates the right SQL query among the first five in the 80% of the cases. This result can be greatly improved by re-ranking the queries with a machine learning algorithms.

Artificial Intelligence, 2012

Ontology reasoning finds a relevant application in the so-called ontology-based data access, where a classical extensional database (EDB) is enhanced by an ontology, in the form of logical assertions, that generates new intensional knowledge which contributes to answering queries. In this setting, queries are therefore answered against a logical theory constituted by the EDB and the ontology; more specifically, query answering amounts to computing the answers to the query that are entailed by the EDB and the ontology. In this paper, we study novel relevant classes of ontological theories for which query answering is both decidable and of tractable data complexity, that is, the complexity with respect to the size of the data only. In particular, our new classes belong to the recently introduced family of Datalog-based languages, called Datalog ±. The basic Datalog ± rules are (functionfree) Horn rules extended with existential quantification in the head, known as tuplegenerating dependencies (TGDs). We propose the language of sticky sets of TGDs (or sticky Datalog ±), which are sets of TGDs with a restriction on multiple occurrences of variables in the rule-bodies. We establish complexity results for answering conjunctive queries under sticky sets of TGDs, showing, in particular, that queries can be compiled into domain independent first-order (and thus translatable into SQL) queries over the given EDB. We also present several extensions of sticky sets of TGDs, and investigate the complexity of query answering under such classes. In summary, we obtain highly expressive and effective ontology languages that unify and generalize both classical database constraints, and important features of the most widespread tractable description logics; in particular, the DL-Lite family of description logics.

International Journal on Natural Language Computing, 2018

This project aims to develop a system which converts a natural language statement into MySQL query to retrieve information from respective database. The system mainly focuses on creation of complex queries which includes nested queries with more than two-level depth, queries with aggregate functions, having clause, group by clause and co-related queries which are formed due to constraint on aggregate function. The natural language input statement taken from the user is passed through various OpenNLP natural language processing techniques like Tokenization, Parts of Speech Tagging, Stemming and Lemmatization to get the statement in the desired form. The statement is further processed to extract the type of query, the basic clause, which specifies the required entities from the database and the condition clause, which specifies constraints on the basic clause. The final query is generated by converting the basic and condition clauses to their query form and then concatenating the condition query to the basic query. Currently, the system works only with MySQL database.

1993

Description Logic (DL) Systems can be useful as front-ends for databases, particularly in applications that involve browsing and exploring, such as data mining. Such use of DL systems raises some pragmatic and theoretical issues. In this paper, we adopt a general architecture for \loose coupling" DL systems with databases; in the context of this architecture, we have built a system called qindl that can generate, from a speci cation, a translator from a description logic to a database query language. qindl was used to generate translators from classic, a typical DL, to 3 di erent database query languages. We also present one view of safety of classic when it is used to query databases, and present a semantical basis for this view.