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A Probabilistic-Logical Framework for Ontology Matching +Ontology matching is the problem of determining correspondences between concepts, properties, and individuals of different heterogeneous ontologies. With this paper we present a novel probabilistic-logical framework for ontology matching based on Markov logic. We define the syntax and semantics and provide a formalization of the ontology matching problem within the framework. The approach has several advantages over existing methods such as ease of experimentation, incoherence mitigation during the alignment process, and the incorporation of apriori confidence values. We show empirically that the approach is efficient and more accurate than existing matchers on an established ontology alignment benchmark dataset.  +
A Semantic Web Middleware for Virtual Data Integration on the Web +In this contribution a system is presented, which provides access to distributed data sources using Semantic Web technology. While it was primarily designed for data sharing and scientific collaboration, it is regarded as a base technology useful for many other Semantic Web applications. The proposed system allows to retrieve data using SPARQL queries, data sources can register and abandon freely, and all RDF Schema or OWL vocabularies can be used to describe their data, as long as they are accessible on the Web. Data heterogeneity is addressed by RDF-wrappers like D2R-Server placed on top of local information systems. A query does not directly refer to actual endpoints, instead it contains graph patterns adhering to a virtual data set. A mediator finally pulls and joins RDF data from different endpoints providing a transparent on-the-fly view to the end-user. The SPARQL protocol has been defined to enable systematic data access to remote endpoints. However, remote SPARQL queries require the explicit notion of endpoint URIs. The presented system allows users to execute queries without the need to specify target endpoints. Additionally, it is possible to execute join and union operations across different remote endpoints. The optimization of such distributed operations is a key factor concerning the performance of the overall system. Therefore, proven concepts from database research can be applied.  +
A Survey of Current Link Discovery Frameworks +Links build the backbone of the Linked Data Cloud. With the steady growth in the size of datasets comes an increased need for end users to know which frameworks to use for deriving links between datasets. In this survey, we comparatively evaluate current Link Discovery tools and frameworks. For this purpose, we outline general requirements and derive a generic architecture of Link Discovery frameworks. Based on this generic architecture, we study and compare the features of state-of the-art linking frameworks. We also analyze reported performance evaluations for the different frameworks. Finally, we derive insights pertaining to possible future developments in the domain of Link Discovery.  +
ANAPSID: An Adaptive Query Processing Engine for SPARQL Endpoints +Following the design rules of Linked Data, the number of available SPARQL endpoints that support remote query processing is quickly growing; however, because of the lack of adaptivity, query executions may frequently be unsuccessful. First, fixed plans identified following the traditional optimize-then execute paradigm, may timeout as a consequence of endpoint availability. Second, because blocking operators are usually implemented, endpoint query engines are not able to incrementally produce results, and may become blocked if data sources stop sending data. We present ANAPSID, an adaptive query engine for SPARQL endpoints that adapts query execution schedulers to data availability and run-time conditions. ANAPSID provides physical SPARQL operators that detect when a source becomes blocked or data traÆc is bursty, and opportunistically, the operators produce results as quickly as data arrives from the sources. Additionally, ANAPSID operators implement main memory replacement policies to move previously computed matches to secondary memory avoiding duplicates. We compared ANAPSID performance with respect to RDF stores and endpoints, and observed that ANAPSID speeds up execution time, in some cases, in more than one order of magnitude.  +
Accessing and Documenting Relational Databases through OWL Ontologies +Relational databases have been designed to store high volumes of data and to provide an efficient query interface. Ontologies are geared towards capturing domain knowledge, annotations, and to offer high-level, machine-processable views of data and metadata. The complementary strengths and weaknesses of these data models motivate the research effort we present in this paper. The goal of this work is to bridge the relational and ontological worlds, in order to leverage the efficiency and scalability of relational technologies and the high-level view of data and metadata proper of ontologies. The system we designed and developed achieves: (i) automatic ontology extraction from relational data sources and (ii) automatic query translation from SPARQL to SQL. Among the others, we focus on two main applications of this novel technology: (i) ontological publishing of relational data, and (ii) automatic relational schema annotation and documentation. The system has been designed and tested against real-life scenarios from Big Science projects, which are used as running examples throughout the paper.  +
Adaptive Integration of Distributed Semantic Web Data +The use of RDF (Resource Description Framework) data is a cornerstone of the Semantic Web. RDF data embedded in Web pages may be indexed using semantic search engines, however, RDF data is often stored in databases, accessible viaWeb Services using the SPARQL query language for RDF, which form part of the Deep Web which is not accessible using search engines. This paper addresses the problem of effectively integrating RDF data stored in separate Web-accessible databases. An approach based on distributed query processing is described, where data from multiple repositories are used to construct partitioned tables that are integrated using an adaptive query processing technique supporting join reordering, which limits any reliance on statistics and metadata about SPARQL endpoints, as such information is often inaccurate or unavailable, but is required by existing systems supporting federated SPARQL queries. The approach presented extends existing approaches in this area by allowing tables to be added to the query plan while it is executing, and shows how an approach currently used within relational query processing can be applied to distributed SPARQL query processing. The approach is evaluated using a prototype implementation and potential applications are discussed.  +
AgreementMaker: Efficient Matching for Large Real-World Schemas and Ontologies +We present the AgreementMaker system for matching real-world schemas and ontologies, which may consist of hundreds or even thousands of concepts. The end users of the system are sophisticated domain experts whose needs have driven the design and implementation of the system: they require a responsive, powerful, and extensible framework to perform, evaluate, and compare matching methods. The system comprises a wide range of matching methods addressing different levels of granularity of the components being matched (conceptual vs. structural), the amount of user intervention that they require (manual vs. automatic), their usage (stand-alone vs. composed), and the types of components to consider (schema only or schema and instances). Performance measurements (recall, precision, and runtime) are supported by the system, along with the weighted combination of the results provided by those methods. The AgreementMaker has been used and tested in practical applications and in the Ontology Alignment Evaluation Initiative (OAEI) competition. We report here on some of its most advanced features, including its extensible architecture that facilitates the integration and performance tuning of a variety of matching methods, its capability to evaluate, compare, and combine matching results, and its user interfaces with a control panel that drives all the matching methods and evaluation strategies.  +
Analysing Scholarly Communication Metadata of Computer Science Events +Over the past 30 years, we have observed the impact of the ubiquitous availability of the Internet, email, and web-based services on scholarly communication. The preparation of manuscripts as well as the organization of conferences, from submission to peer review to publication, have become considerably easier and efficient. A key question now is what were the measurable effects on scholarly communication in computer science? Of particular interest are the following questions: Did the number of submissions to conferences increase? How did the selection processes change? Is there a proliferation of publications? We shed light on some of these questions by analyzing comprehensive scholarly communication metadata from a large number of computer science conferences of the last 30 years. Our transferable analysis methodology is based on descriptive statistics analysis as well as exploratory data analysis and uses crowd-sourced, semantically represented scholarly communication metadata from OpenResearch.org.  +
Avalanche: Putting the Spirit of the Web back into Semantic Web Querying +Traditionally Semantic Web applications either included a web crawler or relied on external services to gain access to the Web of Data. Recent efforts have enabled applications to query the entire Semantic Web for up-to-date results. Such approaches are based on either centralized indexing of semantically annotated metadata or link traversal and URI dereferencing as in the case of Linked Open Data. By making limiting assumptions about the information space, they violate the openness principle of the Web – a key factor for its ongoing success. In this article we propose a technique called Avalanche, designed to allow a data surfer to query the Semantic Web transparently without making any prior assumptions about the distribution of the data – thus adhering to the openness criteria. Specifically, Avalanche can perform “live” (SPARQL) queries over the Web of Data. First, it gets on-line statistical information about the data distribution, as well as bandwidth availability. Then, it plans and executes the query in a distributed manner trying to quickly provide first answers. The main contribution of this paper is the presentation of this open and distributed SPARQL querying approach. Furthermore, we propose to extend the query planning algorithm with qualitative statistical information. We empirically evaluate Avalanche using a realistic dataset, show its strengths but also point out the challenges that still exist.  +
Bringing Relational Databases into the Semantic Web: A Survey +Relational databases are considered one of the most popular storage solutions for various kinds of data and they have been recognized as a key factor in generating huge amounts of data for Semantic Web applications. Ontologies, on the other hand, are one of the key concepts and main vehicle of knowledge in the Semantic Web research area. The problem of bridging the gap between relational databases and ontologies has attracted the interest of the Semantic Web community, even from the early years of its existence and is commonly referred to as the database-to-ontology mapping problem. However, this term has been used interchangeably for referring to two distinct problems: namely, the creation of an ontology from an existing database instance and the discovery of mappings between an existing database instance and an existing ontology. In this paper, we clearly define these two problems and present the motivation, benefits, challenges and solutions for each one of them. We attempt to gather the most notable approaches proposed so far in the literature, present them concisely in tabular format and group them under a classification scheme. We finally explore the perspectives and future research steps for a seamless and meaningful integration of databases into the Semantic Web.  +
Cross: an OWL wrapper for teasoning on relational databases +One of the challenges of the Semantic Web is to integrate the huge amount of information already available on the standard Web, usually stored in relational databases. In this paper, we propose a formalization of a logic model of relational databases, and a transformation of that model into an OWL, a Semantic Web language. This transformation is implemented in Cross, as an open-source prototype. We prove a relation between the notion of legal database state and the consistency of the corresponding OWL knowledge base. We then show how that transformation can prove useful to enhance databases, and integrate them in the Semantic Web.  +
D2RQ – Treating Non-RDF Databases as Virtual RDF Graphs +As Semantic Web technologies are getting mature, there is a growing need for RDF applications to access the content of huge, live, non-RDF, legacy databases without having to replicate the whole database into RDF. In this poster we present D2RQ, a declarative language to describe mappings between application-specific relational database schemata and RDF-S/OWL ontologies. D2RQ allows RDF applications to treat non-RDF relational data bases as virtual RDF graphs, which can be queried using RDQL.  +
DataMaster – a Plug-in for Importing Schemas and Data from Relational Databases into Protégé +We present DataMaster, a Protégé plug-in that supports the importing of schema structure and data from relational databases into Protégé. The plug-in supports both OWL and frames-based ontologies and can be used with any relational database with JDBC/ODBC drivers.  +
Discovering and Maintaining Links on the Web of Data +The Web of Data is built upon two simple ideas, Employ the RDF data model to publish structured data on the Web and to create explicit data links between entities within different data sources. This paper presents the Silk -- Linking Framework, a toolkit for discovering and maintaining data links between Web data sources. Silk consists of three components: 1. A link discovery engine, which computes links between data sources based on a declarative specification of the conditions that entities must fulfil in order to be interlinked; 2. A tool for evaluating the generated data links in order to fine-tune the linking specification; 3. A protocol for maintaining data links between continuously changing data sources. The protocol allows data sources to exchange both linksets as well as detailed change information and enables continuous link recomputation. The interplay of all the components is demonstrated within a life science use case.  +
FedX: Optimization Techniques for Federated Query Processing on Linked Data +Motivated by the ongoing success of Linked Data and the growing amount of semantic data sources available on the Web, new challenges to query processing are emerging. Especially in distributed settings that require joining data provided by multiple sources, sophisticated optimization techniques are necessary for efficient query processing. We propose novel join processing and grouping techniques to minimize the number of remote requests, and develop an effective solution for source selection in the absence of preprocessed metadata. We present FedX, a practical framework that enables efficient SPARQL query processing on heterogeneous, virtually integrated Linked Data sources. In experiments, we demonstrate the practicability and efficiency of our framework on a set of real-world queries and data sources from the Linked Open Data cloud. With FedX we achieve a significant improvement in query performance over state-of-the-art federated query engines.  +
From Relational Data to RDFS Models +A vast amount of information resources is stored as relational-like data and inaccessible to RDFS-based systems. We describe FDR2 – an approach to integration of relational-like information resources with RDFS-aware systems. The proposed solution is purely RDFS-based. We use RDF/S as a mechanism to specify and perform linking of relational data to a predefined domain ontology. The approach is transformation-free, this ensures that all the data is accessible and usable in consistence with the original data model.  +
Integration of Scholarly Communication Metadata using Knowledge Graphs +Important questions about the scientific community, e.g., what authors are the experts in a certain field, or are actively engaged in international collaborations, can be answered using publicly available datasets. However, data required to answer such questions is often scattered over multiple isolated datasets. Recently, the Knowledge Graph (KG) concept has been identified as a means for interweaving heterogeneous datasets and enhancing answer completeness and soundness. We present a pipeline for creating high quality knowledge graphs that comprise data collected from multiple isolated structured datasets. As proof of concept, we illustrate the different steps in the construction of a knowledge graph in the domain of scholarly communication metadata (SCM-KG). Particularly, we demonstrate the benefits of exploiting semantic web technology to reconcile data about authors, papers, and conferences. We conducted an experimental study on an SCM-KG that merges scientific research metadata from the DBLP bibliographic source and the Microsoft Academic Graph. The observed results provide evidence that queries are processed more effectively on top of the SCM-KG than over the isolated datasets, while execution time is not negatively affected.  +
KnoFuss: A Comprehensive Architecture for Knowledge Fusion +We propose a knowledge fusion architecture KnoFuss based on the application of problem-solving methods technology, which allows methods for subtasks of the fusion process to be combined and the best methods to be selected, depending on the domain and task at hand.  +
LIMES - A Time-Efficient Approach for Large-Scale Link Discovery on the Web of Data +The Linked Data paradigm has evolved into a powerful enabler for the transition from the document-oriented Web into the Semantic Web. While the amount of data published as Linked Data grows steadily and has surpassed 25 billion triples, less than 5% of these triples are links between knowledge bases. Link discovery frameworks provide the functionality necessary to discover missing links between knowledge bases in a semi-automatic fashion. Yet, the task of linking knowledge bases requires a significant amount of time, especially when it is carried out on large data sets. This paper presents and evaluates LIMES - a novel time-efficient approach for link discovery in metric spaces. Our approach utilizes the mathematical characteristics of metric spaces to compute estimates of the similarity between instances. These estimates are then used to filter out a large amount of those instance pairs that do not suffice the mapping conditions. Thus, LIMES can reduce the number of comparisons needed during the mapping process by several orders of magnitude. We present the mathematical foundation and the core algorithms employed in the implementation. We evaluate LIMES with synthetic data to elucidate its behavior on small and large data sets with different configurations and show that our approach can significantly reduce the time complexity of a mapping task. In addition, we compare the runtime of our framework with a state-oft heart link discovery tool. We show that LIMES is more than 60 times faster when mapping large knowledge bases.  +
LogMap: Logic-based and Scalable Ontology Matching +In this paper, we present LogMap, a highly scalable ontology matching system with built-in reasoning and diagnosis capabilities. To the best of our knowledge, LogMap is the only matching system that can deal with semantically rich ontologies containing tens (and even hun-dreds) of thousands of classes. In contrast to most existing tools, LogMap also implements algorithms for on the fly unsatisability detection and repair. Our experiments with the ontologies NCI, FMA and SNOMEDCT confirm that our system can efficiently match even the largest existing bio-medical ontologies. Furthermore, LogMap is able to produce a `clean' set of output mappings in many cases, in the sense that the ontology obtained by integrating LogMap's output mappings with the input ontologies is consistent and does not contain unsatisable classes.  +
Optimizing SPARQL Queries over Disparate RDF Data Sources through Distributed Semi-joins +With the ever-increasing amount of data on the Web available at SPARQL endpoints the need for an integrated and transparent way of accessing the data has arisen. It is highly desirable to have a way of asking SPARQL queries that make use of data residing in disparate data sources served by multiple SPARQL endpoints. We aim at providing such a capability and thus enabling an integrated way of querying the whole Semantic Web at a time.  +
Querying Distributed RDF Data Sources with SPARQL +DARQ provides transparent query access to multiple SPARQL services, i.e., it gives the user the impression to query one single RDF graph despite the real data being distributed on the web. A service description language enables the query engine to decompose a query into sub-queries, each of which can be answered by an individual service. DARQ also uses query rewriting and cost-based query optimization to speed-up query execution.  +
Querying over Federated SPARQL Endpoints : A State of the Art Survey +The increasing amount of Linked Data and its inherent distributed nature have attracted significant attention throughout the research community and amongst practitioners to search data, in the past years. Inspired by research results from traditional distributed databases, different approaches for managing federation over SPARQL Endpoints have been introduced. SPARQL is the standardised query language for RDF, the default data model used in Linked Data deployments and SPARQL Endpoints are a popular access mechanism provided by many Linked Open Data (LOD) repositories. In this paper, we initially give an overview of the federation framework infrastructure and then proceed with a comparison of existing SPARQL federation frameworks. Finally, we highlight shortcomings in existing frameworks, which we hope helps spawning new research directions.  +
Querying the Web of Data with Graph Theory-based Techniques +The increasing amount of Linked Data on the Web enables users to retrieve quality and complex information and to deploy innovative, added-value applications. The volume of available Linked Data and their spread across a large number of repositories make a strong case for ecient distributed SPARQL queries. However, in practice, current distributed SPARQL query processing techniques face issues on performance and scalability. In our previous work we provided initial evidence that graph theory-based techniques can address performance issues better than other approaches such as DARQ. Here we further exploit the potential of graph algorithms and we show how they can address performance and scalability for distributed SPARQL queries even better. To that end, we present an improved engine called GDS and we evaluate it by providing a detailed comparison to existing approaches for distributed queries (i.e. DARQ and FedX). By analyzing the evaluation results, we try to identify promising techniques for distributed SPARQL processing, and to outline the problems that need to be addressed in future research.  +
Querying the Web of Interlinked Datasets using VOID Descriptions +Query processing is an important way of accessing data on the Semantic Web. Today, the Semantic Web is characterized as a web of interlinked datasets, and thus querying the web can be seen as dataset integration on the web. Also, this dataset integration must be transparent from the data consumer as if she is querying the whole web. To decide which datasets should be selected and integrated for a query, one requires a metadata of the web of data. In this paper, to enable this transparency, we introduce a federated query engine called WoDQA (Web of Data Query Analyzer) which discovers datasets relevant with a query in an automated manner using VOID documents as metadata. WoDQA focuses on powerful dataset elimination by analyzing query structure with respect to the metadata of datasets. Dataset and linkset descriptions in VOID documents are analyzed for a SPARQL query and a federated query is constructed. By means of linkset concept of VOID, links between datasets are incorporated into selection of federated data sources. Current version ofWoDQA is available as a SPARQL endpoint.  +
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