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

Free composition instead of language dictatorship

Historically, programming languages have been—benevolent—dictators: reducing all possible semantics to specific ones offered by a few built-in language constructs. Over the years, some programming languages have freed the programmers from the restrictions to use only built-in libraries, built-in data types, and builtin type-checking rules. Even though—arguably—such freedom could lead to anarchy, or people shooting themselves in the foot, the contrary tends to be the case: a language that does not allow for extensibility is depriving software engineers of the ability to construct proper abstractions and to structure software in the most optimal way. Therefore the software becomes less structured and maintainable than would be possible if the software engineer could express the behavior of the program with the most appropriate abstractions. The idea proposed by this paper is to move composition from built-in language constructs to programmable, first-class abstractions in a language. We discuss several prototypes of the Co-op language, which show that it is possible, with a relatively simple model, to express a wide range of compositions as first-class concepts

Pantry: A Macro Library for Python

Python lacks a simple way to create custom syntax and constructs that goes outside of its own syntax rules. A paradigm that allows for these possibilities to exist within languages is macros. Macros allow for a shorter set of syntax to expand into a longer set of instructions at compile-time. This gives the capability to evolve the language to fit personal needs. Pantry, implements a hygienic text-substitution macro system for Python. Pantry achieves this through the introduction of an additional preparsing step that utilizes parsing and lexing of the source code. Pantry proposes a way to simply declare a pattern to be recognized, articulate instructions that replace the pattern, and replace the pattern in the source code. This form of meta-programming allows its users to be able to more concisely write their Python code and present the language in a more natural and intuitive manner. We validate Pantry’s utility through use cases inspired by Python Enhancement Proposals (PEPs) and go through five of them. These are requests from the Python community for features to be implemented into Python. Pantry fulfills these desires through the composition of macros that that performs the new feature

AIOCJ: A Choreographic Framework for Safe Adaptive Distributed Applications

We present AIOCJ, a framework for programming distributed adaptive applications. Applications are programmed using AIOC, a choreographic language suited for expressing patterns of interaction from a global point of view. AIOC allows the programmer to specify which parts of the application can be adapted. Adaptation takes place at runtime by means of rules, which can change during the execution to tackle possibly unforeseen adaptation needs. AIOCJ relies on a solid theory that ensures applications to be deadlock-free by construction also after adaptation. We describe the architecture of AIOCJ, the design of the AIOC language, and an empirical validation of the framework.Comment: Technical Repor

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”

Review of software applications for deriving collocations

The field of collocation extraction has enjoyed considerable growth and vitality from the 1990s onwards. Our research uncovered a multitude of software programs that can derive collocations from textual data, but also introduced the following question: Which one is the most fitting for the task of extracting collocations from a corpus? This paper attempts to answer the previous question. We start by stating the criteria on which we based our judgement of the software applications included in our review. We then move on to give a brief description of each package, in terms of its functions, merits and demerits. We conclude by stating which of the packages was deemed, according to our opinion, the most appropriate for our purposes and provide a summary table of the results of the software review