Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF

Survey over Existing Query and Transformation Languages

A widely acknowledged obstacle for realizing the vision of the Semantic Web is the inability of many current Semantic Web approaches to cope with data available in such diverging representation formalisms as XML, RDF, or Topic Maps. A common query language is the first step to allow transparent access to data in any of these formats. To further the understanding of the requirements and approaches proposed for query languages in the conventional as well as the Semantic Web, this report surveys a large number of query languages for accessing XML, RDF, or Topic Maps. This is the first systematic survey to consider query languages from all these areas. From the detailed survey of these query languages, a common classification scheme is derived that is useful for understanding and differentiating languages within and among all three areas

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

Data transformation and query management in personal health sensor networks

Sensor technology has been exploited in many application areas ranging from climate monitoring, to traffic management, and healthcare. The role of these sensors is to monitor human beings, the environment or instrumentation and provide continuous streams of information regarding their status or well being. In the case study presented in this work, the network is provided by football teams with sensors generating continuous heart rate values during a number of different sporting activities. In wireless networks such as these, the requirement is for methods of data management and transformation in order to present data in a format suited to high level queries. In effect, what is required is a traditional database-style query interface where domain experts can continue to probe for the answers required in more specialised environments. The challenge arises from the gap that emerges between the low level sensor output and the high level user requirements of the domain experts. This paper describes a process to close this gap by automatically harvesting the raw sensor data and providing semantic enrichment through the addition of context data

A supply chain collaboration solution using XQuery

Enterprises in a collaborative supply chain share information for quick and intelligent decision making. Collaborative Planning, Forecasting and Replenishment (CPFR), a business model developed by an organization called Voluntary Inter-industry Commerce Standards, defines a set of business processes to collaborate on planning, forecasting and replenishment activities to arrive at a common order. This involves sharing sales forecast, order forecast and promotion information among business partners. The Internet has paved the way for using Web Services to share this information as XML data in real time and to make it available across enterprises. Enterprises need to access this information and integrate it with the information available in their databases, Excel sheets and XML applications. This thesis discusses the development of an information architecture using XQuery (a declarative query language by W3C) for aggregating data from disparate data, and evaluates XQuery as a tool to enable information integration for supply chain collaboration. For this purpose, several CPFR scenarios are identified where two retailers share their sales forecast, order forecast and promotion forecast information with the manufacturer. A prototype application is developed which can fetch data from disparate data sources such as Web Service, XML databases and CSV files, aggregate it, and use it to identify exceptions in forecasts and calculate the key performance indicators (KPI). These KPIs are then presented as an executive dashboard using Visual FoxPro. This Dashboard presents information graphically and enables executives to track the performance of the supply chain, and hence enables them to make quick and intelligent decisions

The relational XQuery puzzle: a look-back on the pieces found so far

Given the tremendous versatility of relational database implementations toward awide range of database problems, it seems only natural to consider them as back-ends for XML data processing. Yet, the assumptions behind the language XQuery are considerably different to those in traditional RDBMSs. The underlying data model is a tree, data and results carry an intrinsic order, queries are described using explicit iteration and, after all, problems are everything else but regular. Solving the relational XQuery puzzle, therefore, has challenged anumber of research groups over the past years. The purpose of this article is to summarize and assess some of the results that have been obtained during this period to solve the puzzle. Our main focus is on the Pathfinder XQuery compiler, afull reference implementation of apurely relational XQuery processor. As we dissect its components, we relate them to other work in the field and also point to open problems and limitations in the context of relational XQuery processin

Four Lessons in Versatility or How Query Languages Adapt to the Web

Exposing not only human-centered information, but machine-processable data on the Web is one of the commonalities of recent Web trends. It has enabled a new kind of applications and businesses where the data is used in ways not foreseen by the data providers. Yet this exposition has fractured the Web into islands of data, each in different Web formats: Some providers choose XML, others RDF, again others JSON or OWL, for their data, even in similar domains. This fracturing stifles innovation as application builders have to cope not only with one Web stack (e.g., XML technology) but with several ones, each of considerable complexity. With Xcerpt we have developed a rule- and pattern based query language that aims to give shield application builders from much of this complexity: In a single query language XML and RDF data can be accessed, processed, combined, and re-published. Though the need for combined access to XML and RDF data has been recognized in previous work (including the W3C’s GRDDL), our approach differs in four main aspects: (1) We provide a single language (rather than two separate or embedded languages), thus minimizing the conceptual overhead of dealing with disparate data formats. (2) Both the declarative (logic-based) and the operational semantics are unified in that they apply for querying XML and RDF in the same way. (3) We show that the resulting query language can be implemented reusing traditional database technology, if desirable. Nevertheless, we also give a unified evaluation approach based on interval labelings of graphs that is at least as fast as existing approaches for tree-shaped XML data, yet provides linear time and space querying also for many RDF graphs. We believe that Web query languages are the right tool for declarative data access in Web applications and that Xcerpt is a significant step towards a more convenient, yet highly efficient data access in a “Web of Data”