Do What I Mean: Online Shopping with a Natural Language Search Agent

Ineffective search engines on e-catalog sites are driving away potential customers. Natural-language querying improves precision and parsing capability, and with advances in the technology, it can also meet these shopping sites\u27 performance demands

Passage de la langue naturelle à une requête SPARQL dans le système SWIP

International audienceNotre objectif est de fournir aux utilisateurs un moyen d'interroger des bases de connaissances en utilisant des requêtes exprimées en langue naturelle. Nous souhaitons masquer la complexité liée à la formulation des requêtes dans un langage de requêtes graphes comme SPARQL. L'originalité principale de notre approche réside dans l'utilisation de patrons de requêtes. Dans cet article, nous justifions le postulat selon lequel les requêtes issues d'utilisateurs de la "vraie vie" sont des variations autour de quelques familles typiques de requêtes. Nous expliquons également comment notre approche est adaptable à différentes langues. Les premières évaluations sur le jeu de données du challenge QALD-2 montrent la pertinence de notre approche

D'un langage de haut niveau à des requêtes graphes permettant d'interroger le web sémantique

Les modèles graphiques sont de bons candidats pour la représentation de connaissances sur le Web, où tout est graphes : du graphe de machines connectées via Internet au "Giant Global Graph" de Tim Berners-Lee, en passant par les triplets RDF et les ontologies. Dans ce contexte, le problème crucial de l'interrogation ontologique est le suivant : est-ce qu'une base de connaissances composée d'une partie terminologique et d'une partie assertionnelle implique la requête, autrement dit, existe-t-il une réponse à la question ? Ces dernières années, des logiques de description ont été proposées dans lesquelles l'expressivité de l'ontologie est réduite de façon à rendre l'interrogation calculable (familles DL-Lite et EL). OWL 2 restreint OWL-DL dans ce sens en se fondant sur ces familles. Nous nous inscrivons dans le contexte d'utilisation de formalismes graphiques pour la représentation (RDF, RDFS et OWL) et l'interrogation (SPARQL) de connaissances. Alors que les langages d'interrogation fondés sur des graphes sont présentés par leurs promoteurs comme étant naturels et intuitifs, les utilisateurs ne pensent pas leurs requêtes en termes de graphes. Les utilisateurs souhaitent des langages simples, proches de la langue naturelle, voire limités à des mots-clés. Nous proposons de définir un moyen générique permettant de transformer une requête exprimée en langue naturelle vers une requête exprimée dans le langage de graphe SPARQL, à l'aide de patrons de requêtes. Le début de ce travail coïncide avec les actions actuelles du W3C visant à préparer une nouvelle version de RDF, ainsi qu'avec le processus de standardisation de SPARQL 1.1 gérant l'implication dans les requêtes.Graph models are suitable candidates for KR on the Web, where everything is a graph, from the graph of machines connected to the Internet, the "Giant Global Graph" as described by Tim Berners-Lee, to RDF graphs and ontologies. In that context, the ontological query answering problem is the following: given a knowledge base composed of a terminological component and an assertional component and a query, does the knowledge base implies the query, i.e. is there an answer to the query in the knowledge base? Recently, new description logic languages have been proposed where the ontological expressivity is restricted so that query answering becomes tractable. The most prominent members are the DL-Lite and the EL families. In the same way, the OWL-DL language has been restricted and this has led to OWL2, based on the DL-Lite and EL families. We work in the framework of using graph formalisms for knowledge representation (RDF, RDF-S and OWL) and interrogation (SPARQL). Even if interrogation languages based on graphs have long been presented as a natural and intuitive way of expressing information needs, end-users do not think their queries in terms of graphs. They need simple languages that are as close as possible to natural language, or at least mainly limited to keywords. We propose to define a generic way of translating a query expressed in a high-level language into the SPARQL query language, by means of query patterns. The beginning of this work coincides with the current activity of the W3C that launches an initiative to prepare a possible new version of RDF and is in the process of standardizing SPARQL 1.1 with entailments

Answering questions about archived, annotated meetings

Retrieving information from archived meetings is a new domain of information retrieval that has received increasing attention in the past few years. Search in spontaneous spoken conversations has been recognized as more difficult than text-based document retrieval because meeting discussions contain two levels of information: the content itself, i.e. what topics are discussed, but also the argumentation process, i.e. what conflicts are resolved and what decisions are made. To capture the richness of information in meetings, current research focuses on recording meetings in Smart-Rooms, transcribing meeting discussion into text and annotating discussion with semantic higher-level structures to allow for efficient access to the data. However, it is not yet clear what type of user interface is best suited for searching and browsing such archived, annotated meetings. Content-based retrieval with keyword search is too naive and does not take into account the semantic annotations on the data. The objective of this thesis is to assess the feasibility and usefulness of a natural language interface to meeting archives that allows users to ask complex questions about meetings and retrieve episodes of meeting discussions based on semantic annotations. The particular issues that we address are: the need of argumentative annotation to answer questions about meetings; the linguistic and domain-specific natural language understanding techniques required to interpret such questions; and the use of visual overviews of meeting annotations to guide users in formulating questions. To meet the outlined objectives, we have annotated meetings with argumentative structure and built a prototype of a natural language understanding engine that interprets questions based on those annotations. Further, we have performed two sets of user experiments to study what questions users ask when faced with a natural language interface to annotated meeting archives. For this, we used a simulation method called Wizard of Oz, to enable users to express questions in their own terms without being influenced by limitations in speech recognition technology. Our experimental results show that technically it is feasible to annotate meetings and implement a deep-linguistic NLU engine for questions about meetings, but in practice users do not consistently take advantage of these features. Instead they often search for keywords in meetings. When visual overviews of the available annotations are provided, users refer to those annotations in their questions, but the complexity of questions remains simple. Users search with a breadth-first approach, asking questions in sequence instead of a single complex question. We conclude that natural language interfaces to meeting archives are useful, but that more experimental work is needed to find ways to incent users to take advantage of the expressive power of natural language when asking questions about meetings

Query processing in Chiql: optimization and translation.

by Yip Suen-man.Appendixes in Chinese and English.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references.Acknowledgment --- p.1Abstract --- p.2Table of Contents --- p.3List of Tables --- p.5List of Figures --- p.6Chapter Chapter 1 --- Introduction --- p.7Chapter 1.1 --- Objectives --- p.9Chapter 1.2 --- Chapter Summary --- p.10Chapter Chapter 2 --- Related Work --- p.11Chapter 2.1 --- Relational Query Language --- p.11Chapter 2.1.1 --- Relational Algebra Vs Relational Calculus --- p.11Chapter 2.1.2 --- Procedural Vs Nonprocedural --- p.13Chapter 2.1.3 --- Natural Language (NL) Vs Restricted Natural Language (RNL) --- p.13Chapter 2.2 --- Existing Relational Query Language --- p.14Chapter 2.3 --- Chinese Related Work --- p.16Chapter 2.4 --- Chapter Summary --- p.17Chapter Chapter 3 --- Chinese Database Query Language : Chiql --- p.19Chapter 3.1 --- Naturalness --- p.19Chapter 3.2 --- Simplicity --- p.20Chapter 3.3 --- Procedural and Multi-statements Query Style --- p.21Chapter 3.4 --- Functional Completeness --- p.22Chapter 3.5 --- Chapter Summary --- p.25Chapter Chapter 4 --- Query Processing --- p.26Chapter 4.1 --- Query Optimization --- p.27Chapter 4.1.1 --- Query Representation --- p.27Chapter 4.1.2 --- Standardization --- p.28Chapter 4.1.3 --- Simplification --- p.29Chapter 4.1.4 --- Amelioration --- p.29Chapter 4.2 --- Query Translation of SQL --- p.29Chapter 4.3 --- Query Processing in Chiql --- p.33Chapter 4.3.1 --- Overview of the Query Processing --- p.33Chapter 4.3.2 --- Inter-Statement Dependency --- p.34Chapter 4.3.3 --- Translation flow of Chiql-to-SQL --- p.36Chapter 4.3.4 --- An Introductory Example --- p.37Chapter 4.4 --- Chapter Summary --- p.40Chapter Chapter 5 --- Statement Merging Algorithm (SMA) --- p.41Chapter 5.1 --- Problems --- p.41Chapter 5.2 --- Definitions --- p.42Chapter 5.3 --- Linear Merging Algorithm (LMA) --- p.43Chapter 5.4 --- Tree Merging Algorithm (TMA) --- p.47Chapter 5.5 --- Statement Merging Algorithm (SMA) --- p.50Chapter 5.6 --- Improvement --- p.56Chapter 5.7 --- Chapter Summary --- p.57Chapter Chapter 6 --- Pattern Mapping Algorithm (PMA) --- p.58Chapter 6.1 --- Problem --- p.58Chapter 6.2 --- Type of Patterns --- p.61Chapter 6.3 --- Pre-requisite of Pattern Mapping --- p.65Chapter 6.4 --- Pattern Mapping Algorithm (PMA) --- p.65Chapter 6.5 --- An Illustration Example --- p.68Chapter 6.6 --- Chapter Summary --- p.72Chapter Chapter 7 --- Evaluation --- p.73Chapter 7.1 --- Testing the Correctness --- p.73Chapter 7.2 --- Comparison in Translation Power With Other Translator --- p.76Chapter 7.3 --- Chapter Summary --- p.78Chapter Chapter 8 --- Conclusion --- p.79Reference --- p.82Appendix --- p.8

A DBMS query language in natural Chinese language form.

by Lam Chin-keung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1995.Includes bibliographical references (leaves 129-135 (2nd gp.)).ACKNOWLEDGMENTS --- p.IABSTRACT --- p.IITABLE OF CONTENTS --- p.IIILIST OF FIGURES --- p.VILIST OF TABLES --- p.VIIIChapter CHAPTER 1 --- INTRODUCTION --- p.1Chapter 1.1 --- Motivations --- p.1Chapter 1.2 --- Objectives --- p.3Chapter 1.3 --- More to go --- p.3Chapter 1.4 --- Chapter Summary --- p.4Chapter CHAPTER 2 --- RELATED WORK --- p.6Chapter 2.1 --- Chinese Related Work --- p.6Chapter 2.1.1 --- Chinese Natural Language --- p.6Chapter 2.1.2 --- Chinesized Query Language From English --- p.7Chapter 2.2 --- High Level Database Query Language --- p.8Chapter 2.2.1 --- Relational Algebra vs Relational Calculus --- p.9Chapter 2.2.2 --- Procedural vs Declarative --- p.10Chapter 2.2.3 --- Natural Language (NL) vs Restricted Natural Language (RNL) --- p.11Chapter 2.3 --- Database Query Interface --- p.13Chapter 2.3.1 --- Linear Textual Interface --- p.13Chapter 2.3.2 --- Form-based Interface --- p.14Chapter 2.3.3 --- Graphical Interface --- p.14Chapter 2.4 --- Remarks --- p.14Chapter CHAPTER 3 --- DESIGN PRINCIPLES --- p.16Chapter 3.1 --- Underlying Data Model of the new language --- p.16Chapter 3.2 --- Problems Under Attack --- p.17Chapter 3.2.1 --- Naturalness --- p.17Chapter 3.2.2 --- Procedural vs Declarative --- p.19Chapter 3.2.3 --- Supports of Chinese Characters --- p.21Chapter 3.3 --- Design Principles --- p.22Chapter 3.4 --- Chapter Summary --- p.26Chapter CHAPTER 4 --- LANGUAGE DEFINITION --- p.28Chapter 4.1 --- Language Overvew --- p.28Chapter 4.2 --- The Data Manipulation Language --- p.29Chapter 4.2.1 --- Relational Operators --- p.30Chapter 4.2.2 --- Rail-Track Diagram of Chiql --- p.32Chapter 4.2.3 --- The 11-template --- p.33Chapter 4.2.4 --- Chiql Examples --- p.37Chapter 4.2.5 --- Common Language Constructs --- p.39Chapter 4.2.6 --- ONE issue about GROUP BY and RESTRICTION --- p.41Chapter 4.3 --- Other Language Features --- p.42Chapter 4.3.1 --- Aggregate Functions --- p.43Chapter 4.3.2 --- Attribute Alias --- p.44Chapter 4.3.3 --- Conditions in Chinese --- p.45Chapter 4.3.4 --- Unquantifed Predicates --- p.45Chapter 4.3.5 --- sorting --- p.47Chapter 4.4 --- Treatment of Quantified Predicates --- p.48Chapter 4.5 --- The Data Definition Language --- p.52Chapter 4.5.1 --- Create Table --- p.52Chapter 4.5.2 --- Drop Table --- p.54Chapter 4.5.3 --- Alter Table --- p.54Chapter 4.5.4 --- Insert Row --- p.56Chapter 4.5.5 --- Delete Row --- p.56Chapter 4.5.6 --- Update Row --- p.57Chapter 4.5.7 --- Remarks on DDL --- p.58Chapter 4.6 --- Chapter Summary --- p.59Chapter CHAPTER 5 --- END-USER INTERFACE --- p.61Chapter 5.1 --- EUI Overview --- p.61Chapter 5.2 --- Design Principles --- p.62Chapter 5.2.1 --- Language Independent Aspects --- p.62Chapter 5.2.2 --- Language Dependent Aspects --- p.64Chapter 5.3 --- Complex Condition Handling --- p.68Chapter 5.4 --- Input Sequences of the EUI --- p.71Chapter 5.5 --- Query Formulation： An Example --- p.73Chapter 5.6 --- Chapter Summary --- p.85Chapter CHAPTER 6 --- CHIQL TO SQL TRANSLATIONS --- p.86Chapter 6.1 --- Related Work --- p.87Chapter 6.2 --- Translation Overview --- p.87Chapter 6.2.1 --- "Pass One:Mapping( Input = Chiql, Output = multi-statement SQL)" --- p.89Chapter 6.2.2 --- "Pass Two:Nesting(Input = multi-statement SQL, Output = single statement SQL)" --- p.92Chapter 6.2.3 --- Technical Difficulties in Chiql/SQL Translation --- p.99Chapter 6.3 --- Chapter Summary --- p.106Chapter CHAPTER 7 --- EVALUATION --- p.108Chapter 7.1 --- Expressiveness Test --- p.108Chapter 7.1.1 --- Results --- p.109Chapter 7.1.2 --- Implications --- p.111Chapter 7.2 --- Usability Evaluation --- p.111Chapter 7.2.1 --- Evaluation Methodology --- p.112Chapter 7.2.2 --- Result:Completion Time --- p.113Chapter 7.2.3 --- Result: Additional Help --- p.116Chapter 7.2.4 --- Result： Query Error --- p.116Chapter 7.2.5 --- Result： Overall Score --- p.118Chapter 7.2.6 --- User Comments --- p.120Chapter 7.3 --- Chapter Summary --- p.120Chapter CHAPTER 8 --- CONCLUSIONS --- p.122Chapter 8.1 --- Thesis Conclusions --- p.122Chapter 8.2 --- Future Work --- p.124REFERENCESAPPENDI