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

prototypical implementations

In this technical report, we present prototypical implementations of innovative tools and methods developed according to the working plan outlined in Technical Report TR-B-09-05 [23]. We present an ontology modularization and integration framework and the SVoNt server, the server-side end of an SVN- based versioning system for ontologies in the Corporate Ontology Engineering pillar. For the Corporate Semantic Collaboration pillar, we present the prototypical implementation of a light-weight ontology editor for non-experts and an ontology based expert finder system. For the Corporate Semantic Search pillar, we present a prototype for algorithmic extraction of relations in folksonomies, a tool for trend detection using a semantic analyzer, a tool for automatic classification of web documents using Hidden Markov models, a personalized semantic recommender for multimedia content, and a semantic search assistant developed in co-operation with the Museumsportal Berlin. The prototypes complete the next milestone on the path to an integral Cor- porate Semantic Web architecture based on the three pillars Corporate Ontol- ogy Engineering, Corporate Semantic Collaboration, and Corporate Semantic Search, as envisioned in [23]

Knowledge extraction from unstructured data

Data availability is becoming more essential, considering the current growth of web-based data. The data available on the web are represented as unstructured, semi-structured, or structured data. In order to make the web-based data available for several Natural Language Processing or Data Mining tasks, the data needs to be presented as machine-readable data in a structured format. Thus, techniques for addressing the problem of capturing knowledge from unstructured data sources are needed. Knowledge extraction methods are used by the research communities to address this problem; methods that are able to capture knowledge in a natural language text and map the extracted knowledge to existing knowledge presented in knowledge graphs (KGs). These knowledge extraction methods include Named-entity recognition, Named-entity Disambiguation, Relation Recognition, and Relation Linking. This thesis addresses the problem of extracting knowledge over unstructured data and discovering patterns in the extracted knowledge. We devise a rule-based approach for entity and relation recognition and linking. The defined approach effectively maps entities and relations within a text to their resources in a target KG. Additionally, it overcomes the challenges of recognizing and linking entities and relations to a specific KG by employing devised catalogs of linguistic and domain-specific rules that state the criteria to recognize entities in a sentence of a particular language, and a deductive database that encodes knowledge in community-maintained KGs. Moreover, we define a Neuro-symbolic approach for the tasks of knowledge extraction in encyclopedic and domain-specific domains; it combines symbolic and sub-symbolic components to overcome the challenges of entity recognition and linking and the limitation of the availability of training data while maintaining the accuracy of recognizing and linking entities. Additionally, we present a context-aware framework for unveiling semantically related posts in a corpus; it is a knowledge-driven framework that retrieves associated posts effectively. We cast the problem of unveiling semantically related posts in a corpus into the Vertex Coloring Problem. We evaluate the performance of our techniques on several benchmarks related to various domains for knowledge extraction tasks. Furthermore, we apply these methods in real-world scenarios from national and international projects. The outcomes show that our techniques are able to effectively extract knowledge encoded in unstructured data and discover patterns over the extracted knowledge presented as machine-readable data. More importantly, the evaluation results provide evidence to the effectiveness of combining the reasoning capacity of the symbolic frameworks with the power of pattern recognition and classification of sub-symbolic models

A distributed architecture for efficient Web service discovery

open3noAlthough the definition of service-oriented architecture (SOA) included the presence of a service registry from the beginning, the first implementations (e.g., UDDI) did not really succeed mainly because of security and governance issues. This article tackles the problem by introducing DREAM (Distributed Registry by ExAMple): a publish/subscribe-based solution to integrate existing, different registries, along with a match-making approach to ease the publication and retrieval of services. DREAM fosters the interoperability among registry technologies and supports UDDI, ebXML Registry, and other registries. The publish/subscribe paradigm allows service providers to decide the services they want to publish, and requestors to be informed of the services that satisfy their interests. As for the match-making, DREAM supports different ways to evaluate the matching between published and required services. Besides presenting the architecture of DREAM and the different match-making opportunities, the article also describes the experiments conducted to evaluate proposed solutions.Baresi, Luciano; Miraz, Matteo; Plebani, PierluigiBaresi, Luciano; Miraz, Matteo; Plebani, Pierluig

Web service searching

With the growing number of Web services, it is no longer adequate to locate a Web service by searching its name or browsing a UDDI directory. An efficient Web services discovery mechanism is necessary for locating and selecting the required Web services. Searching mechanism should be based on Web service description rather than on keywords. In this work, we introduce a Web service searching prototype that can locate Web services by comparing all available information encoded in Web service description, such as operation name, input and output types, the structure of the underlying XML schema, and the semantic of element names. Our approach combines information-retrieval techniques, weighted bipartite graph matching algorithm and tree-matching algorithm. Given a query, represented as set of keywords, Web service description, or operation description, an information retrieval technique is used to rank the candidate Web services based on their text-base similarity to the query. The ranked result can be further refined by computing their structure similarity. (Abstract shortened by UMI.) Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .J34. Source: Masters Abstracts International, Volume: 44-03, page: 1403. Thesis (M.Sc.)--University of Windsor (Canada), 2005