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Simplifying Syntactic and Semantic Parsing of NL Based Queries in Advanced Application Domains E. Kapetanios, D. Baer, P. Groenewoud Dept. of Computer.

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Presentation on theme: "Simplifying Syntactic and Semantic Parsing of NL Based Queries in Advanced Application Domains E. Kapetanios, D. Baer, P. Groenewoud Dept. of Computer."— Presentation transcript:

1 Simplifying Syntactic and Semantic Parsing of NL Based Queries in Advanced Application Domains E. Kapetanios, D. Baer, P. Groenewoud Dept. of Computer Science, ETH Zürich 8 th Inter. Conf. on Applications of Natural LanguageTo Information Systems NLDB 2003

2 Problem statement Need to pose queries with an NL based query interface Advanced application domains are considered, e.g., scientific and technical domains Use more than one (sub)language, e.g., English, German, French, Italian Take data out of a DBMS box

3 Conventional Parsing Techniques Syntactically parse the query to check if compliant with a grammar Semantically parse the query to, mostly, grasp the grammatic role of terms within the query Pragmatically do something with this query, e.g., address data out of a DBMS box

4 ...and their defficiencies A syntactically correct query might be meaningless Semantic parsing might still lead to meaningless statements if not driven by the application domain semantics Preassumption that the user is familiar with the whole range of an advanced application domain vocabulary, since parsing takes place after completion of the query Parsing becomes too complex when different natural languages are concerned

5 Our (MDDQL) approach Application domain semantics (ontology) drive the construction of queries through suggestion of meaningful terms Syntax is determined by semantics No need to consider different syntactic and semantic parsing techniques when changing natural language interfaces Data are taken out of the same DBMS box regardless the preferred NL

6 An example query (Demo)

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10 MDDQL ingredients as a language An Object-Oriented Ontology Description Language (O3DL) providing the Query Language Vocabulary Graph-based Inference Services for both 1. High Level Query Construction operating upon the ontology graph and 2. Transformation to, e.g., SQL, operating upon the corresponding high level query tree

11 A partially defined ontology graph

12 An example high level query tree Class Relationship Property Instance

13 Inferring SQL statements Transformation o3 – PATIENTHIST o50 – PATIENTHIST.HISTRMED o100 – PATIENTHIST.HISTRMED.HASPIR SELECT {} FROM {} WHERE {}

14 Synopsis Syntactic and semantic parsing replaced by having application domain semantics driving the query construction...resulting into a high level query tree which reflects the constructed query semantics

15 Contribution No need to implement complex semantic parsing techniques No need to implement different parsing techniques for each added-on natural (sub)language No need to know spelling and intentional semantics of all possible terms in an advanced application domain Alleviate semantic disambiguation of homonyms (context dependency) during query construction


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