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

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”

Keyword-Based Querying for the Social Semantic Web

Enabling non-experts to publish data on the web is an important achievement of the social web and one of the primary goals of the social semantic web. Making the data easily accessible in turn has received only little attention, which is problematic from the point of view of incentives: users are likely to be less motivated to participate in the creation of content if the use of this content is mostly reserved to experts. Querying in semantic wikis, for example, is typically realized in terms of full text search over the textual content and a web query language such as SPARQL for the annotations. This approach has two shortcomings that limit the extent to which data can be leveraged by users: combined queries over content and annotations are not possible, and users either are restricted to expressing their query intent using simple but vague keyword queries or have to learn a complex web query language. The work presented in this dissertation investigates a more suitable form of querying for semantic wikis that consolidates two seemingly conflicting characteristics of query languages, ease of use and expressiveness. This work was carried out in the context of the semantic wiki KiWi, but the underlying ideas apply more generally to the social semantic and social web. We begin by defining a simple modular conceptual model for the KiWi wiki that enables rich and expressive knowledge representation. A component of this model are structured tags, an annotation formalism that is simple yet flexible and expressive, and aims at bridging the gap between atomic tags and RDF. The viability of the approach is confirmed by a user study, which finds that structured tags are suitable for quickly annotating evolving knowledge and are perceived well by the users. The main contribution of this dissertation is the design and implementation of KWQL, a query language for semantic wikis. KWQL combines keyword search and web querying to enable querying that scales with user experience and information need: basic queries are easy to express; as the search criteria become more complex, more expertise is needed to formulate the corresponding query. A novel aspect of KWQL is that it combines both paradigms in a bottom-up fashion. It treats neither of the two as an extension to the other, but instead integrates both in one framework. The language allows for rich combined queries of full text, metadata, document structure, and informal to formal semantic annotations. KWilt, the KWQL query engine, provides the full expressive power of first-order queries, but at the same time can evaluate basic queries at almost the speed of the underlying search engine. KWQL is accompanied by the visual query language visKWQL, and an editor that displays both the textual and visual form of the current query and reflects changes to either representation in the other. A user study shows that participants quickly learn to construct KWQL and visKWQL queries, even when given only a short introduction. KWQL allows users to sift the wealth of structure and annotations in an information system for relevant data. If relevant data constitutes a substantial fraction of all data, ranking becomes important. To this end, we propose PEST, a novel ranking method that propagates relevance among structurally related or similarly annotated data. Extensive experiments, including a user study on a real life wiki, show that pest improves the quality of the ranking over a range of existing ranking approaches

Mining XML documents with association rule algorithms

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2008Includes bibliographical references (leaves: 59-63)Text in English; Abstract: Turkish and Englishx, 63 leavesFollowing the increasing use of XML technology for data storage and data exchange between applications, the subject of mining XML documents has become more researchable and important topic. In this study, we considered the problem of Mining Association Rules between items in XML document. The principal purpose of this study is applying association rule algorithms directly to the XML documents with using XQuery which is a functional expression language that can be used to query or process XML data. We used three different algorithms; Apriori, AprioriTid and High Efficient AprioriTid. We give comparisons of mining times of these three apriori-like algorithms on XML documents using different support levels, different datasets and different dataset sizes

The XML benchmark project

With standardization efforts of a query language for XML documents drawing to a close, researchers and users increasingly focus their attention on the database technology that has to deliver on the new challenges that the sheer amount of XML documents produced by applications pose to data management: validation, performance evaluation and optimization of XML query processors are the upcoming issues. Following a long tradition in database research, the XML Store Benchmark Project provides a framework to assess an XML database's abilities to cope with a broad spectrum of different queries, typically posed in real-world application scenarios. The benchmark is intended to help both implementors and users to compare XML databases independent of their own, specific application scenario. To this end, the benchmark offers a set queries each of which is intended to challenge a particular primitive of the query processor or storage engine. The overall workload we propose consists of a scalable document database and a concise, yet comprehensive set of queries, which covers the major aspects of query processing. The queries' challenges range from stressing the textual character of the document to data analysis queries, but include also typical ad-hoc queries. We complement our research with results obtained from running the benchmark on our XML database platform. They are intended to give a first baseline, illustrating the state of the art

e-Commerce Ontology Web (e-COW)

The objective of "e-Commerce Ontology Web" (e-COW) is to implement the ontology concept and semantic web in knowledge management to improve business operation in organization. Generally, current problem of e-commerce system is searching information is very difficult and sometimes may miss relevant information especially details for certain products. Furthermore, human browsing and reading required to extract relevant information from information sources. The semantic web deals with important application areas such as knowledge management and electronic commerce (both B2C and B2B). It may help to overcome many of the current bottlenecks in these areas. In feasibility study, it helps author in identifying the framework of semantic web and ontology development process. The important feature ofthe e-COW is retrieving information based on the class and attributes of data intree views, and advance search using ontology agents. Inaddition the system will display products details in meaningful and organized which can help users to understand easily. With development of many knowledge management systems today, it shows that the knowledge management is very important especially to organization. As a result, we know ontology has become important concept in knowledge management especially for managing and organizing information and data

Making Archival and Special Collections More Accessible

Making Archival and Special Collections More Accessible represents the efforts of OCLC Research over the last seven years to support change in the end-to-end process that results in archival and special collections materials being delivered to interested users.Revealing hidden assets stewarded by research institutions so they can be made available for research and learning locally and globally is a prime opportunity for libraries to create and deliver new value. Making Archival and Special Collections More Accessible collects important work OCLC Research has done to help achieve the economies and efficiencies that permit these materials to be effectively described, properly disclosed, successfully discovered and appropriately delivered. Achieving control over these collections in an economic fashion will mean that current resources can have a broader impact or be invested elsewhere in other activities

Structural Summaries as a Core Technology for Efficient XML Retrieval

The Extensible Markup Language (XML) is extremely popular as a generic markup language for text documents with an explicit hierarchical structure. The different types of XML data found in today’s document repositories, digital libraries, intranets and on the web range from flat text with little meaningful structure to be queried, over truly semistructured data with a rich and often irregular structure, to rather rigidly structured documents with little text that would also fit a relational database system (RDBS). Not surprisingly, various ways of storing and retrieving XML data have been investigated, including native XML systems, relational engines based on RDBSs, and hybrid combinations thereof. Over the years a number of native XML indexing techniques have emerged, the most important ones being structure indices and labelling schemes. Structure indices represent the document schema (i.e., the hierarchy of nested tags that occur in the documents) in a compact central data structure so that structural query constraints (e.g., path or tree patterns) can be efficiently matched without accessing the documents. Labelling schemes specify ways to assign unique identifiers, or labels, to the document nodes so that specific relations (e.g., parent/child) between individual nodes can be inferred from their labels alone in a decentralized manner, again without accessing the documents themselves. Since both structure indices and labelling schemes provide compact approximate views on the document structure, we collectively refer to them as structural summaries. This work presents new structural summaries that enable highly efficient and scalable XML retrieval in native, relational and hybrid systems. The key contribution of our approach is threefold. (1) We introduce BIRD, a very efficient and expressive labelling scheme for XML, and the CADG, a combined text and structure index, and combine them as two complementary building blocks of the same XML retrieval system. (2) We propose a purely relational variant of BIRD and the CADG, called RCADG, that is extremely fast and scales up to large document collections. (3) We present the RCADG Cache, a hybrid system that enhances the RCADG with incremental query evaluation based on cached results of earlier queries. The RCADG Cache exploits schema information in the RCADG to detect cached query results that can supply some or all matches to a new query with little or no computational and I/O effort. A main-memory cache index ensures that reusable query results are quickly retrieved even in a huge cache. Our work shows that structural summaries significantly improve the efficiency and scalability of XML retrieval systems in several ways. Former relational approaches have largely ignored structural summaries. The RCADG shows that these native indexing techniques are equally effective for XML retrieval in RDBSs. BIRD, unlike some other labelling schemes, achieves high retrieval performance with a fairly modest storage overhead. To the best of our knowledge, the RCADG Cache is the only approach to take advantage of structural summaries for effectively detecting query containment or overlap. Moreover, no other XML cache we know of exploits intermediate results that are produced as a by-product during the evaluation from scratch. These are valuable cache contents that increase the effectiveness of the cache at no extra computational cost. Extensive experiments quantify the practical benefit of all of the proposed techniques, which amounts to a performance gain of several orders of magnitude compared to various other approaches

Personal Knowledge Models with Semantic Technologies

Conceptual Data Structures (CDS) is a unified meta-model for representing knowledge cues in varying degrees of granularity, structuredness, and formality. CDS consists of: (1) A simple, expressive data-model; (2) A relation ontology which unifies the relations found in cognitive models of personal knowledge management tools, e. g., documents, mind-maps, hypertext, or semantic wikis. (3) An interchange format for structured text. Implemented prototypes have been evaluated

A Pattern-based Foundation for Language-Driven Software Engineering

This work brings together two fundamental ideas for modelling, programming and analysing software systems. The first idea is of a methodological nature: engineering software by systematically creating and relating languages. The second idea is of a technical nature: using patterns as a practical foundation for computing. The goal is to show that the systematic creation and layering of languages can be reduced to the elementary operations of pattern matching and instantiation and that this pattern-based approach provides a formal and practical foundation for language-driven modelling, programming and analysis. The underpinning of the work is a novel formalism for recognising, deconstructing, creating, searching, transforming and generally manipulating data structures. The formalism is based on typed sequences, a generic structure for representing trees. It defines basic pattern expressions for matching and instantiating atomic values and variables. Horizontal, vertical, diagonal and hierarchical operators are different ways of combining patterns. Transformations combine matching and instantiating patterns and they are patterns themselves. A quasiquotation mechanism allows arbitrary levels of meta-pattern functionality and forms the basis of pattern abstraction. Path polymorphic operators are used to specify fine-grained search of structures. A range of core concepts such as layering, parsing and pattern-based computing can naturally be defined through pattern expressions. Three language-driven tools that utilise the pattern formalism showcase the applicability of the pattern-approach. Concat is a self-sustaining (meta-)programming system in which all computations are expressed by matching and instantiation. This includes parsing, executing and optimising programs. By applying its language engineering tools to its own meta-language, Concat can extend itself from within. XMF (XML Modeling Framework) is a browser-based modelling- and meta-modelling framework that provides flexible means to create and relate modelling languages and to query and validate models. The pattern functionality that makes this possible is partly exposed as a schema language and partly as a JavaScript library. CFR (Channel Filter Rule Language) implements a language-driven approach for layered analysis of communication in complex networked systems. The communication on each layer is visible in the language of an “abstract protocol” that is defined by communication patterns