26 research outputs found
COOPERATIVE QUERY ANSWERING FOR APPROXIMATE ANSWERS WITH NEARNESS MEASURE IN HIERARCHICAL STRUCTURE INFORMATION SYSTEMS
Cooperative query answering for approximate answers has been utilized in various problem domains. Many challenges in manufacturing information retrieval, such as: classifying parts into families in group technology implementation, choosing the closest alternatives or substitutions for an out-of-stock part, or finding similar existing parts for rapid prototyping, could be alleviated using the concept of cooperative query answering. Most cooperative query answering techniques proposed by researchers so far concentrate on simple queries or single table information retrieval. Query relaxations in searching for approximate answers are mostly limited to attribute value substitutions. Many hierarchical structure information systems, such as manufacturing information systems, store their data in multiple tables that are connected to each other using hierarchical relationships - "aggregation", "generalization/specialization", "classification", and "category". Due to the nature of hierarchical structure information systems, information retrieval in such domains usually involves nested or jointed queries. In addition, searching for approximate answers in hierarchical structure databases not only considers attribute value substitutions, but also must take into account attribute or relation substitutions (i.e., WIDTH to DIAMETER, HOLE to GROOVE). For example, shape transformations of parts or features are possible and commonly practiced. A bar could be transformed to a rod. Such characteristics of hierarchical information systems, simple query or single-relation query relaxation techniques used in most cooperative query answering systems are not adequate. In this research, we proposed techniques for neighbor knowledge constructions, and complex query relaxations. We enhanced the original Pattern-based Knowledge Induction (PKI) and Distribution Sensitive Clustering (DISC) so that they can be used in neighbor hierarchy constructions at both tuple and attribute levels. We developed a cooperative query answering model to facilitate the approximate answer searching for complex queries. Our cooperative query answering model is comprised of algorithms for determining the causes of null answer, expanding qualified tuple set, expanding intersected tuple set, and relaxing multiple condition simultaneously. To calculate the semantic nearness between exact-match answers and approximate answers, we also proposed a nearness measuring function, called "Block Nearness", that is appropriate for the query relaxation methods proposed in this research
Knowledge Rich Natural Language Queries over Structured Biological Databases
Increasingly, keyword, natural language and NoSQL queries are being used for
information retrieval from traditional as well as non-traditional databases
such as web, document, image, GIS, legal, and health databases. While their
popularity are undeniable for obvious reasons, their engineering is far from
simple. In most part, semantics and intent preserving mapping of a well
understood natural language query expressed over a structured database schema
to a structured query language is still a difficult task, and research to tame
the complexity is intense. In this paper, we propose a multi-level
knowledge-based middleware to facilitate such mappings that separate the
conceptual level from the physical level. We augment these multi-level
abstractions with a concept reasoner and a query strategy engine to dynamically
link arbitrary natural language querying to well defined structured queries. We
demonstrate the feasibility of our approach by presenting a Datalog based
prototype system, called BioSmart, that can compute responses to arbitrary
natural language queries over arbitrary databases once a syntactic
classification of the natural language query is made
The design and implementation of a meaning driven data query language
We present the design and implementation of a Meaning Driven Data Query Language - MDDQL - which aims at the construction of queries through system made suggestions
of natural language based query terms for both scientific
application domain terms and operator/operation ones. A query construction blackboard is used where query language
terms are suggested to the user in its preferred natural
language and in a name centered way, together with their connotation. This helps in understanding the meaning of the terms and/or operators or operations to be included in the query. Furthermore, the construction of the query turns out to be an incremental refinement of the query under construction through semantic constraints, where only those domain language terms and/or operators/operations are suggested which result into meaningful combinations of query terms as related to the scientific application domain
semantics. Therefore, semantically meaningless queries can be prevented during the query construction. Such a semantics aware mechanism is not available in conventional database query languages such as SQL, where one is allowed to execute a query calculating, for example, the average of numerical data values whereas they represent the codes of categorical values. Moreover, no familiarity with the semantics of complex database schemes or interpretation
of the symbols (names of classes/tables/attributes, value codes) underlying the storage model, as well as familiarity with the syntax of a database specific query language are needed by the end-user. The constructed query can be submitted to the MDDQL query interpretation and transformation engine, where the corresponding SQL-query
is generated and delegated to a DBMS (e.g., Oracle, MSAccess, SQL-Server). Generation of SQL-statements addressing NF2 data models such as those provided by the
object-relational Oracle DBMS is also enabled. The query
result is presented in a table based form where all storage
model symbols are interpreted and can be exported for the
usage with statistical software packages (e.g., SPSS)
A semantics based interactive query formulation technique
We present an interactive query formulation technique
which enables exploitation not only of structural properties
of data but also of semantic constraints as posed by the contents of data. The technique aims at the formulation of a semantically consistent or meaningful query by the end-user without any previous knowledge of syntax formalisms and
data model semantics. This has been achieved by end-user
guidance in that an inference engine suggests semantically
rich query terms for further consideration by the end-user.
The set of suggested terms at each interaction stage comply
with the already considered query terms with respect to
structure and contents based semantics. Assignment or selection of operational terms are also allowed, if operational semantics comply with the semantics of data. The interactive query formulation component has been implemented in Java and runs on the client side of a client/server based query answering system architecture
Query processing of geometric objects with free form boundarie sin spatial databases
The increasing demand for the use of database systems as an integrating
factor in CAD/CAM applications has necessitated the development of database
systems with appropriate modelling and retrieval capabilities. One essential
problem is the treatment of geometric data which has led to the development of
spatial databases. Unfortunately, most proposals only deal with simple geometric
objects like multidimensional points and rectangles. On the other hand, there has
been a rapid development in the field of representing geometric objects with free
form curves or surfaces, initiated by engineering applications such as mechanical
engineering, aviation or astronautics. Therefore, we propose a concept for the realization
of spatial retrieval operations on geometric objects with free form
boundaries, such as B-spline or Bezier curves, which can easily be integrated in
a database management system. The key concept is the encapsulation of geometric
operations in a so-called query processor. First, this enables the definition of
an interface allowing the integration into the data model and the definition of the
query language of a database system for complex objects. Second, the approach
allows the use of an arbitrary representation of the geometric objects. After a
short description of the query processor, we propose some representations for free
form objects determined by B-spline or Bezier curves. The goal of efficient query
processing in a database environment is achieved using a combination of decomposition
techniques and spatial access methods. Finally, we present some experimental
results indicating that the performance of decomposition techniques is
clearly superior to traditional query processing strategies for geometric objects
with free form boundaries
Object-oriented querying of existing relational databases
In this paper, we present algorithms which allow an object-oriented
querying of existing relational databases. Our goal is to provide an improved query
interface for relational systems with better query facilities than SQL. This
seems to be very important since, in real world applications, relational systems
are most commonly used and their dominance will remain in the near future. To
overcome the drawbacks of relational systems, especially the poor query facilities
of SQL, we propose a schema transformation and a query translation algorithm.
The schema transformation algorithm uses additional semantic information to enhance
the relational schema and transform it into a corresponding object-oriented
schema. If the additional semantic information can be deducted from an underlying
entity-relationship design schema, the schema transformation may be done
fully automatically. To query the created object-oriented schema, we use the
Structured Object Query Language (SOQL) which provides declarative query facilities
on objects. SOQL queries using the created object-oriented schema are
much shorter, easier to write and understand and more intuitive than corresponding
S Q L queries leading to an enhanced usability and an improved querying of
the database. The query translation algorithm automatically translates SOQL queries
into equivalent SQL queries for the original relational schema
Construction de réponses coopératives : du corpus à la modélisation informatique
Les stratégies utilisées pour la recherche d’information dans le cadre du Web diffèrent d’un moteur de recherche à un autre, mais en général, les résultats obtenus ne répondent pas directement et simplement à la question posée. Nous présentons une stratégie qui vise à définir les fondements linguistiques et de communication d’un système d’interrogation du Web qui soit coopératif avec l’usager et qui tente de lui fournir la réponse la plus appropriée possible dans sa forme et dans son contenu. Nous avons constitué et analysé un corpus de questions-réponses coopératives construites à partir des sections Foire Aux Questions (FAQ) de différents services Web aux usagers. Cela constitue à notre sens une bonne expérimentation de ce que pourrait être une communication directe en langue naturelle sur le Web. Cette analyse de corpus a permis d’extraire les caractéristiques majeures du comportement coopératif et de construire l’architecture de notre système informatique webcoop, que nous présentons à la fin de cet article.Algorithms and strategies used on the Web for information retrieval differ from one search engine to another, but, in general, results do not lead to very accurate and informative answers. In this paper, we describe our strategy for designing a cooperative question answering system that aims at producing the most appropriate answers to natural language questions. To characterize these answers, we collected a corpus of cooperative question in our opinion answer pairs extracted from Frequently Asked Questions. The analysis of this corpus constitutes a good experiment on what a cooperative natural language communication on the Web could be. This analysis allows for the elaboration of a general architecture for our cooperative question answering system webcoop, which we present at the end of this paper
No-But-Semantic-Match: Computing Semantically Matched XML Keyword Search Results
Users are rarely familiar with the content of a data source they are
querying, and therefore cannot avoid using keywords that do not exist in the
data source. Traditional systems may respond with an empty result, causing
dissatisfaction, while the data source in effect holds semantically related
content. In this paper we study this no-but-semantic-match problem on XML
keyword search and propose a solution which enables us to present the top-k
semantically related results to the user. Our solution involves two steps: (a)
extracting semantically related candidate queries from the original query and
(b) processing candidate queries and retrieving the top-k semantically related
results. Candidate queries are generated by replacement of non-mapped keywords
with candidate keywords obtained from an ontological knowledge base. Candidate
results are scored using their cohesiveness and their similarity to the
original query. Since the number of queries to process can be large, with each
result having to be analyzed, we propose pruning techniques to retrieve the
top- results efficiently. We develop two query processing algorithms based
on our pruning techniques. Further, we exploit a property of the candidate
queries to propose a technique for processing multiple queries in batch, which
improves the performance substantially. Extensive experiments on two real
datasets verify the effectiveness and efficiency of the proposed approaches.Comment: 24 pages, 21 figures, 6 tables, submitted to The VLDB Journal for
possible publicatio