An extended fuzzy linguistic approach to generalize boolean information retrieval

Many commercial information retrieval systems use the Boolean information retrieval model. Some extensions have been designed to provide for the limitations this model presents without proceeding to its complete redesign. Fuzzy set theory has been employed as a suitable tool for this purpose, especially for dealing with imprecision and vagueness in the information retrieval activity. The first extensions of the Boolean query language within this formal framework introduced numeric query weights. Recently, the authors have proposed linguistic extensions which manage user vagueness in formulating queries directly. They have also defined a document representation which more effectively expresses documents’ information content.KeywordsBoolean information retrievallinguistic queriescompound document representation

This paper investigates the expressive power of DATALOG¬ queries under unique T-stable model semantics, i.e., a query on a given database yields an answer if and only if there exists a unique T-stable model. Under this semantics DATALOG¬ queries are shown to express exactly all decision problems with unique solutions. Obviously, unique T-stable model semantics is the ‘natural’ semantics for queries with at most one T-stable model or with exactly one T-stable model for every database. The expressive powers of of these two classes of queries are investigated as well but it turns out that any practical language for such queries cannot get to an expressive power higher than DATALOG with stratified negation.

Most image retrieval systems only allow a fragment of text or an example image as a query. Most users have more complex information needs that are not easily expressed in either of these forms. This paper proposes a model based on the Inference Network framework from information retrieval that employs a powerful query language that allows structured query operators, term weighting, and the combination of text and images within a query. The model uses non-parametric methods to estimate probabilities within the inference network. Image annotation and retrieval results are reported and compared against other published systems and illustrative structured and weighted query results are given to show the power of the query language. The resulting system both performs well and is robust compared to existing approaches.KeywordsFeature VectorImage RetrievalLatent Dirichlet AllocationMean Average PrecisionRetrieval ResultThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Hydro-dams safety represents an important concern since their failure could be critical for the society. A key part of the hydro-dams surveillance programs is their visual inspection. However few computer vision support tools for implementing semi-automatically and objectively the visual surveillance and observation of the hydro-dams components exist. One of the issues addressed during the visual inspection, important in the preservation of a good condition of the concrete, is the examination of surface deterioration in respect to small patterned cracks and roughness on the downstream wall. This is particularly a task where digital image enhancement and analysis can bring significant benefit, not only by presenting the user with a more relevant image of the surface deterioration, but also by providing - through suitable numerical descriptors, correlated with linguistic descriptors- subjective and examiner-independent information on the surface state. The correlation of extracted numerical descriptors used to quantify the surface roughness with linguistic qualifiers of the deterioration state of the hydro-dam wall should be determined using information gathered from observers, since it must be compliant to the human expert interpretation of visual data in assessing the concrete surface deterioration. Such an approach would result in a computer vision decision support tool embedding expert knowledge, as designed, implemented and proposed in this paper. The resulting software system was verified on a set of images acquired from a Romanian hydro-dam. The compliance of the linguistic results with the human observation proves its functionality as a semi-automatic tool for hydro-dams surveillance.

Fuzzy description of ball-bearing wear

In this paper we present a graphical query language for XML. The language, based on a simple form of graph grammars, permits us to extract data and reorganize information in a new structure. As with most of the current query languages for XML, queries consist of two parts: one extracting a subgraph and one constructing the output graph. The semantics of queries is given in terms of graph grammars. The use of graph grammars makes it possible to define, in a simple way, the structural properties of both the subgraph that has to be extracted and the graph that has to be constructed. We provide an example-driven comparison of our language w.r.t. other XML query languages, and show the effectiveness and simplicity of our approach.

Alternatives for facilitating automatic transformation of BIM data using semantic query languages

Time series (TSs) are usually represented numerically; however, there are many situations where a linguistic description is preferable. Granular linguistic model of phenomena (GLMP) is a paradigm used in the generation of linguistic descriptions of static situations without considering the temporal relationship of data present, for example, in TSs. This paper presents a new method to generate linguistic descriptions of TSs with an operation similar to petri nets (PNs) and inspired by GLMP. The presented approach maintains the operation of PNs, adding a mechanism to generate linguistic descriptions based on GLMP. The main components of GLMP are added to places and transitions of PNs. This extension is called linguistic PNs (LPNs) and is a language that can be used to generate linguistic descriptions of systems. GLMP is a method that can be used by experts to design how the linguistic descriptions are synthesized and generated. So, LPNs also allow incorporating expert knowledge and combining descriptions in an appropriate way. The experimental part is focused on showing how LPNs can be used to linguistically describe TSs, including the occurrence of maxima or minima, and trends.

In 1952 Markowitz [247] published his pioneering work which laid the foundation of modern portfolio analysis. The mean-variance methodology for the portfolio selection problem has played an important role in the development of modern portfolio selection theory. It combines probability with optimization techniques to model the behavior investment under uncertainty. The basic principle of the mean-variance model is to use the expected return of a portfolio as the investment return and to use the variance of the expected returns of the portfolio as the investment risk. Most of existing portfolio selection models are based on probability theory. Though probability theory is one of the main techniques used for analyzing uncertainty in finance, the financial market is also affected by several non-probabilistic factors such as vagueness and ambiguity. In many important cases it might also be easier to estimate the possibility distributions of rates of return on securities rather than the corresponding probability distributions. Decision makers are commonly provided with information which is characterized by linguistic descriptions such as high risk, low profit, high interest rate, etc. [298] With the introduction of fuzzy set theory by Zadeh [351], it was realized that imperfect knowledge of the returns on the assets and the uncertainty involved in the behavior of financial markets may be captured by means of fuzzy quantities and/or fuzzy constraints. Since the early seventies fuzzy set theory has been widely used to solve many problems including financial risk management. By using fuzzy approaches, the experts’ knowledge and the investors’ subjective opinions can be better integrated into a portfolio selection model.

With governments and administrations releasing open linked data, and with the gradual rise of sensor deployments across the world, semantic queries on the combined sensor and linked data has become a need to provide several intelligent smart city services and applications. The data is represented in form of triples (RDF), concepts and relations in form of ontologies (OWL) and the corresponding query language is SPARQL as per standards of Semantic Web. In this paper, a system for sensor exploration is presented, which takes a set of keywords, context, data, learned and background knowledge as input and produces the intentioned result as output. The system tries to keep the underlying semantic web technologies transparent to the end user. The relevant challenges and the scope of future work is also discussed.

The traditional Web stores huge amount of data in the form of Relational Databases (RDB) as it is good at storing objects and relationships between them.Relational Databases are dynamic in nature which allows bringing tables together helping user to search for related material across multiple tables.RDB are scalable to expand as the data grows.The RDB uses a Structured Query Language called SQL to access the databases for several data retrieval purposes.As the world is moving today from the Syntactic form to Semantic form and the Web is also taking its new form of Semantic Web.The Structured Query of the RDB on web can be a Semantic Query on Semantic Web.The SPARQL is the Query Language recommended by W3C for the RDF(Resource Description Framework).RDF is a directed, labeled graph data format for representing information in the Web and is a very important layer of the Semantic Web Architecture.In this paper we consider the Library Management System (LMS) database, taking some tuples of the LMS Relational Schema.We discuss how the RDF code is scripted and validated using RDF Validator and how RDF Triples are generated.Later we give the graphical representation of the RDF triples and see the process of extracting ontology from the RDF Schema and application of the Semantic Query.

How to exploit the incredible variety of JSON data sets currently available on the Internet, for example, on Open Data portals? The traditional approach would require getting them from the portals, then storing them into some JSON document store and integrating them within the document store. However, once data are integrated, the lack of a query language that provides flexible querying capabilities could prevent analysts from successfully completing their analysis. In this paper, we show how the J-CO Framework, a novel framework that we developed at the University of Bergamo (Italy) to manage large collections of JSON documents, is a unique and innovative tool that provides analysts with querying capabilities based on fuzzy sets over JSON data sets. Its query language, called J-CO-QL, is continuously evolving to increase potential applications; the most recent extensions give analysts the capability to retrieve data sets directly from web portals as well as constructs to apply fuzzy set theory to JSON documents and to provide analysts with the capability to perform imprecise queries on documents by means of flexible soft conditions. This paper presents a practical case study in which real data sets are retrieved, integrated and analyzed to effectively show the unique and innovative capabilities of the J-CO Framework.

Deco is a comprehensive system for answering declarative queries posed over stored relational data together with data obtained on-demand from the crowd. In this paper we describe Deco's cost-based query optimizer, building on Deco's data model, query language, and query execution engine presented earlier. Deco's objective in query optimization is to find the best query plan to answer a query, in terms of estimated monetary cost. Deco's query semantics and plan execution strategies require several fundamental changes to traditional query optimization. Novel techniques incorporated into Deco's query optimizer include a cost model distinguishing between "free" existing data versus paid new data, a cardinality estimation algorithm coping with changes to the database state during query execution, and a plan enumeration algorithm maximizing reuse of common subplans in a setting that makes reuse challenging. We experimentally evaluate Deco's query optimizer, focusing on the accuracy of cost estimation and the efficiency of plan enumeration.

We develop query relaxation techniques for regular path queries and combine them with query approximation in order to support flexible querying of RDF data when the user lacks knowledge of its full structure or where the structure is irregular. In such circumstances, it is helpful if the querying system can perform both approximate matching and relaxation of the user's query and can rank the answers according to how closely they match the original query. Our framework incorporates both standard notions of approximation based on edit distance and RDFS-based inference rules. The query language we adopt comprises conjunctions of regular path queries, thus including extensions proposed for SPARQL to allow for querying paths using regular expressions. We provide an incremental query evaluation algorithm which runs in polynomial time and returns answers to the user in ranked order.

The Web Ontology Language OWL has been advocated as a suitable model for semantic data integration. Data integration requires expressive means to map between heterogeneous OWL schemas. This paper introduces SWQL (Semantic Web Query Language), a strictly typed query language for OWL, and shows how it can be used for mapping between heterogeneous schemas. In contrast to existing RDF query languages which focus on selection and navigation, SWQL also supports construction and user-defined functions to allow for instantiating integrated global schemas in OWL.