An intuitive approach for the expression of Boolean queries

Online information repositories commonly provide keyword search facilities through textual query languages based on Boolean logic. However, there is evidence to suggest that the syntactic demands of such languages can lead to user errors and adversely affect the time that it takes users to form queries. Users also face difficulties because of the conflict in semantics between AND and OR when used in Boolean logic and English language. Analysis of usage logs for the New Zealand Digital Library (NZDL) show that few Boolean queries contain more than three terms, use of the intersection operator dominates and that query refinement is common. We suggest that graphical query languages, in particular Venn-like diagrams, can alleviate the problems that users experience when forming Boolean expressions with textual languages. A study of the utility of Venn diagrams for query specification indicates that with little or no training users can interpret and form Venn-like diagrams in a consistent manner which accurately correspond to Boolean expressions. We describe VQuery, a Venn-diagram based user interface to the New Zealand Digital Library (NZDL). In a study which compared VQuery with a standard textual Boolean interface, users took significantly longer to form queries and produced more erroneous queries when using VQuery. We discuss the implications of these results and suggest directions for future work.

Both phrases and Boolean queries have a long history in information retrieval, particularly in commercial systems. In previous work, Boolean queries have been used as a source of phrases for a statistical retrieval model, This work, like the majority of research on phrases, resulted in little improvement in retrieval effectiveness, In this paper, we describe an approach where phrases identified in natural language queries are used to build structured queries for a probabilistic retrieval model. Our results show that using phrases in this way can improve performance, and that phrases that are automatically extracted from a natural language query perform nearly as well as manually selected phrases.

Icons have become a main component of interface design (Familant & Detweiler, 1993). This study examined functional representation and graphical abstraction components of icons using experienced and novice computer users. Current interface (e.g. word processing) icons were evaluated in a search and select paradigm. Experienced participants were faster and more accurate than novice participants. Functionally representative and graphically concrete icons were recognized faster and more accurately than functionally arbitrary and graphically abstract icons, respectively. Experienced participants were affected more by graphical abstraction than novice participants. Graphically concrete and functionally representative icons were recognized faster than any other type of icon in the study. These data suggest when creating icons, the icons should be made to look as much like the object of reference as possible.

In Information Retrieval it is well-known that the complexity of processing boolean queries depends on the size of the intermediate results, which could be huge (and are typically on disk) even though the size of the final result may be quite small. In the case of inverted files the most time consuming operation is the merging or intersection of the list of occurrences [1]. We propose, the Keyword tree (K-tree) and forest, efficient structures to handle boolean queries in keyword-based information retrieval. Extensive simulations show that K-tree is orders-of-magnitude faster (i.e., far fewer I/O's) for boolean queries than the usual approach of merging the lists of occurrences and incurs only a small overhead for single keyword queries. The K-tree can be efficiently parallelized as well. The construction cost of K-tree is comparable to the cost of building inverted files.

Experimental comparison of navigation in a Galois lattice with conventional information retrieval methods

The performance of Information Retrieval Systems (IRSs) is usually measured using two different criteria, precision and recall. In such a way, the problem of tuning an IRS may be considered as a multi-objective optimization problem. In this contribution, we focus on the automatic learning of Boolean queries in IRSs by means of multi-objective evolutionary techniques. We present a comparative study of four multi-objective evolutionary optimization techniques of general-purpose (NSGA-II, SPEA2 and two MOGLS) to learn Boolean queries.

In this article, our interest is focused on the automatic learning of Boolean queries in information retrieval systems (IRSs) by means of multi‐objective evolutionary algorithms considering the classic performance criteria, precision and recall. We present a comparative study of four well‐known, general‐purpose, multi‐objective evolutionary algorithms to learn Boolean queries in IRSs. These evolutionary algorithms are the Nondominated Sorting Genetic Algorithm (NSGA‐II), the first version of the Strength Pareto Evolutionary Algorithm (SPEA), the second version of SPEA (SPEA2), and the Multi‐Objective Genetic Algorithm (MOGA).

Genetic programming is applied to an information retrieval system in order to improve Boolean query formulation via relevance feedback. This approach brings together the concepts of information retrieval and genetic programming. Documents are viewed as vectors in index term space. A Boolean query, viewed as a parse tree, is an organism in the genetic programming sense. Through the mechanisms of genetic programming, the query is modified in order to improve precision and recall. Relevance feedback is incorporated, in part, via user defined measures over a trial set of documents. The fitness of a candidate query can be expressed directly as a function of the relevance of the retrieved set. Preliminary results based on a testbed are given. The form of the fitness function has a significant effect upon performance and the proper fitness functions take into account relevance based on topicality (and perhaps other factors).< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Many online or local data sources provide powerful querying mechanisms but limited ranking capabilities. For instance, PubMed allows users to submit highly expressive Boolean keyword queries, but ranks the query results by date only. However, a user would typically prefer a ranking by relevance, measured by an information retrieval (IR) ranking function. A naive approach would be to submit a disjunctive query with all query keywords, retrieve all the returned matching documents, and then rerank them. Unfortunately, such an operation would be very expensive due to the large number of results returned by disjunctive queries. In this paper, we present algorithms that return the top results for a query, ranked according to an IR-style ranking function, while operating on top of a source with a Boolean query interface with no ranking capabilities (or a ranking capability of no interest to the end user). The algorithms generate a series of conjunctive queries that return only documents that are candidates for being highly ranked according to a relevance metric. Our approach can also be applied to other settings where the ranking is monotonic on a set of factors (query keywords in IR) and the source query interface is a Boolean expression of these factors. Our comprehensive experimental evaluation on the PubMed database and a TREC data set show that we achieve order of magnitude improvement compared to the current baseline approaches.

Spatial or graphical query languages (GQL) for spatial databases have been discussed for more than 10 years. Quite a number of authors have suggested their own solutions and several prototypes have been developed -- most of them as an extension of SQL. On the other hand commercial GIS products/GIS databases today still use their proprietary command language or graphical user interface for dealing with spatial data in any way. But recently the SNI company has extended their SICAD system with a GQL solution which is also an extension of SQL. This is quite logical as the SICAD/GDB-X is one of the few geographic databases which is fully integrated in a standard relational database management system. We show how this GQL approach fits in a user environment -- in this case in the field of forestry. It has been analyzed to what extent GQL can be used for various GIS activities, what kind of operators are needed, if there are limitations, and how users accept this approach.

The purpose of the Information Retrieval (IR) is to find a set of documents that are relevant for a specific information need of a user. Traditional Information Retrieval model commonly used in commercial search engine is based on keyword indexing system and Boolean logic queries. One big drawback of traditional information retrieval is that they typically retrieve information without an explicitly defined domain of interest to the users so that a lot of no relevance information returns to users, which burden the user to pick up useful answer from these no relevance results. In order to tackle this issue, many semantic web information retrieval models have been proposed recently. The main advantage of Semantic Web is to enhance search mechanisms with the use of Ontology’s mechanisms. In this paper, we present our approach to personalize web search engine based on ontology. In addition, key techniques are also discussed in our paper. Compared to previous research, our works concentrate on the semantic similarity and the whole process including query submission and information annotation.

The migration of e-health systems to the cloud computing brings huge benefits, as same as some security risks. Searchable Encryption(SE) is a cryptography encryption scheme that can protect the confidentiality of data and utilize the encrypted data at the same time. The SE scheme proposed by Cash et al. in Crypto2013 and its follow-up work in CCS2013 are most practical SE Scheme that support Boolean queries at present. In their scheme, the data user has to generate the search tokens by the counter number one by one and interact with server repeatedly, until he meets the correct one, or goes through plenty of tokens to illustrate that there is no search result. In this paper, we make an improvement to their scheme. We allow server to send back some information and help the user to generate exact search token in the search phase. In our scheme, there are only two round interaction between server and user, and the search token has [Formula: see text] elements, where n is the keywords number in query expression, and [Formula: see text] is the minimum documents number that contains one of keyword in query expression, and the computation cost of server is [Formula: see text] modular exponentiation operation.

For bridging the gap between information retrieval (IR) and databases (DB), this article focuses on the logical view. We claim that IR should adopt three major concepts from DB, namely inference, vague predicates and expressive query languages. By regarding IR as uncertain inference, probabilistic versions of relational algebra and Datalog yield very powerful inference mechanisms for IR as well as allowing for more flexible systems. For dealing with various media and data types, vague predicates form a natural extension of text retrieval methods to attribute values, thus switching from propositional to predicate logic. A more expressive IR query language should support joins, be able to compute aggregated results, and allow for restructuring of the result objects.

VISUAL is a graphical icon-based query language designed for scientific databases where visualization of the relationships are important for the domain scientist to express queries. Graphical objects are not tied to the underlying formalism; instead, they represent the relationships of the application domain. VISUAL supports relational, nested, and object-oriented models naturally and has formal basis. In addition to set and bag constructs for complex objects, sequences are also supported by the data model. Concepts of external and internal queries are developed as modularization tools. A new parallel/distributed query processing paradigm is presented. VISUAL query processing techniques are also discussed.

Not many XML information retrieval (IR) systems exist that allow easy addition of and switching between different IR models. Especially for the scientific environment where building a system takes a lot of time and keeps researchers away from the real work, i.e., investigating what is the most effective IR model, a platform that would provide this functionality would be ideal. For this reason we developed such an XML IR system. It is centered around a logical algebra, named score region algebra (SRA), that enables transparent specification of IR models for XML databases (see [1] for more details). The transparency is achieved by a possibility to instantiate various retrieval models, using abstract score functions within algebra operators, while logical query plan and operator definitions remain unchanged. Our algebra operators model three important aspects of XML IR: element relevance score computation, element score propagation, and element score combination. To implement a new IR model, one only needs to provide definitions for these abstract function classes. To illustrate the usefulness of our algebra our demo system supports several, well known IR scoring models (e.g., Language Models, Okapi, and tf.idf), combined with different score propagation and combination functions. The user can select which model to use at run time. Following good practice in database systems design, our prototype system has a typical three-layered architecture. (1) The conceptual layer takes a NEXI [3] query expression as input, e.g.,