A Visual Summary for Linked Open Data sources

In this paper we propose LODeX, a tool that produces a representative summary of a Linked open Data (LOD) source starting from scratch, thus supporting users in exploring and understanding the contents of a dataset. The tool takes in input the URL of a SPARQL endpoint and launches a set of predefined SPARQL queries, from the results of the queries it generates a visual summary of the source. The summary reports statistical and structural information of the LOD dataset and it can be browsed to focus on particular classes or to explore their properties and their use. LODeX was tested on the 137 public SPARQL endpoints contained in Data Hub (formerly CKAN), one of the main Open Data catalogues. The statistical and structural information of the 107 well performed extractions are collected and available in the online version of LODeX (http://dbgroup.unimo.it/lodex)

LODeX: A tool for Visual Querying Linked Open Data

Formulating a query on a Linked Open Data (LOD) source is not an easy task; a technical knowledge of the query language, and, the awareness of the structure of the dataset are essential to create a query. We present a revised version of LODeX that provides the user an easy way for building queries in a fast and interactive manner. When a user decides to explore a LOD source, he/she can take advantage of the Schema Summary produced by LODeX (i.e. a synthetic view of the dataset’s structure) and he/she can pick graphical elements from it to create a visual query. The tool also supports the user in browsing the results and, eventually, in refining the query. The prototype has been evaluated on hundreds of public SPARQL endpoints (listed in Data Hub) and it is available online at http://dbgroup.unimo.it/lodex2. A survey conducted on 27 users has demonstrated that our tool can effectively support both unskilled and skilled users in exploring and querying LOD datasets

Exploiting Semantics for Filtering and Searching Knowledge in a Software Development Context

Software development is still considered a bottleneck for SMEs (Small and Medium Enterprises) in the advance of the Information Society. Usually, SMEs store and collect a large number of software textual documentation; these documents might be profitably used to facilitate them in using (and re-using) Software Engineering methods for systematically designing their applications, thus reducing software development cost. Specific and semantics textual filtering/search mechanisms, supporting the identification of adequate processes and practices for the enterprise needs, are fundamental in this context. To this aim, we present an automatic document retrieval method based on semantic similarity and Word Sense Disambiguation (WSD) techniques. The proposal leverages on the strengths of both classic information retrieval and knowledge-based techniques, exploiting syntactical and semantic information provided by general and specific domain knowledge sources. For any SME, it is as easily and generally applicable as are the search techniques offered by common enterprise Content Management Systems (CMSs). Our method was developed within the FACIT-SME European FP-7 project, whose aim is to facilitate the diffusion of Software Engineering methods and best practices among SMEs. As shown by a detailed experimental evaluation, the achieved effectiveness goes well beyond typical retrieval solutions

Semantic Integration of heterogeneous data sources in the MOMIS Data Transformation System

In the last twenty years, many data integration systems following a classical wrapper/mediator architecture and providing a Global Virtual Schema (a.k.a. Global Virtual View - GVV) have been proposed by the research community. The main issues faced by these approaches range from system-level heterogeneities, to structural syntax level heterogeneities at the semantic level. Despite the research effort, all the approaches proposed require a lot of user intervention for customizing and managing the data integration and reconciliation tasks. In some cases, the effort and the complexity of the task is huge, since it requires the development of specific programming codes. Unfortunately, due to the specificity to be addressed, application codes and solutions are not frequently reusable in other domains. For this reason, the Lowell Report 2005 has provided the guideline for the definition of a public benchmark for information integration problem. The proposal, called THALIA (Test Harness for the Assessment of Legacy information Integration Approaches), focuses on how the data integration systems manage syntactic and semantic heterogeneities, which definitely are the greatest technical challenges in the field. We developed a Data Transformation System (DTS) that supports data transformation functions and produces query translation in order to push down to the sources the execution. Our DTS is based on MOMIS, a mediator-based data integration system that our research group is developing and supporting since 1999. In this paper, we show how the DTS is able to solve all the twelve queries of the THALIA benchmark by using a simple combination of declarative translation functions already available in the standard SQL language. We think that this is a remarkable result, mainly for two reasons: firstly to the best of our knowledge there is no system that has provided a complete answer to the benchmark, secondly, our queries does not require any overhead of new code

Online Index Extraction from Linked Open Data Sources

The production of machine-readable data in the form of RDF datasets belonging to the Linked Open Data (LOD) Cloud is growing very fast. However, selecting relevant knowledge sources from the Cloud, assessing the quality and extracting synthetical information from a LOD source are all tasks that require a strong human effort. This paper proposes an approach for the automatic extraction of the more representative information from a LOD source and the creation of a set of indexes that enhance the description of the dataset. These indexes collect statistical information regarding the size and the complexity of the dataset (e.g. the number of instances), but also depict all the instantiated classes and the properties among them, supplying user with a synthetical view of the LOD source. The technique is fully implemented in LODeX, a tool able to deal with the performance issues of systems that expose SPARQL endpoints and to cope with the heterogeneity on the knowledge representation of RDF data. An evaluation on LODeX on a large number of endpoints (244) belonging to the LOD Cloud has been performed and the effectiveness of the index extraction process has been presented

BLAST: a Loosely Schema-aware Meta-blocking Approach for Entity Resolution

Identifying records that refer to the same entity is a fundamental step for data integration. Since it is prohibitively expensive to compare every pair of records, blocking techniques are typically employed to reduce the complexity of this task. These techniques partition records into blocks and limit the comparison to records co-occurring in a block. Generally, to deal with highly heterogeneous and noisy data (e.g. semi-structured data of the Web), these techniques rely on redundancy to reduce the chance of missing matches. Meta-blocking is the task of restructuring blocks generated by redundancy-based blocking techniques, removing superfluous comparisons. Existing meta-blocking approaches rely exclusively on schema-agnostic features. In this paper, we demonstrate how “loose” schema information (i.e., statistics collected directly from the data) can be exploited to enhance the quality of the blocks in a holistic loosely schema-aware (meta-)blocking approach that can be used to speed up your favorite Entity Resolution algorithm. We call it Blast (Blocking with Loosely-Aware Schema Techniques). We show how Blast can automatically extract this loose information by adopting a LSH-based step for e ciently scaling to large datasets. We experimentally demonstrate, on real-world datasets, how Blast outperforms the state-of-the-art unsupervised meta-blocking approaches, and, in many cases, also the supervised one

Lexical Knowledge Extraction: an Effective Approach to Schema and Ontology Matching

This paper’s aim is to examine what role Lexical Knowledge Extraction plays in data integration as well as ontology engineering.Data integration is the problem of combining data residing at distributed heterogeneous sources, and providing the user with a unified view of these data; a common and important scenario in data integration are structured or semi-structure data sources described by a schema.Ontology engineering is a subfield of knowledge engineering that studies the methodologies for building and maintaining ontologies. Ontology engineering offers a direction towards solving the interoperability problems brought about by semantic obstacles, such as the obstacles related to the definitions of business terms and software classes. In these contexts where users are confronted with heterogeneous information it is crucial the support of matching techniques. Matching techniques aim at finding correspondences between semantically related entities of different schemata/ontologies.Several matching techniques have been proposed in the literature based on different approaches, often derived from other fields, such as text similarity, graph comparison and machine learning.This paper proposes a matching technique based on Lexical Knowledge Extraction: first, an Automatic Lexical Annotation of schemata/ontologies is performed, then lexical relationships are extracted based on such annotations.Lexical Annotation is a piece of information added in a document (book, online record, video, or other data), that refers to a semantic resource such as WordNet. Each annotation has the property to own one or more lexical descriptions. Lexical annotation is performed by the Probabilistic Word Sense Disambiguation (PWSD) method that combines several disambiguation algorithms.Our hypothesis is that performing lexical annotation of elements (e.g. classes and properties/attributes) of schemata/ontologies makes the system able to automatically extract the lexical knowledge that is implicit in a schema/ontology and then to derive lexical relationships between the elements of a schema/ontology or among elements of different schemata/ontologies.The effectiveness of the method presented in this paper has been proven within the data integration system MOMIS

Automatic Normalization and Annotation for Discovering Semantic Mappings

Schema matching is the problem of finding relationships among concepts across heterogeneous data sources (heterogeneous in format and in structure). Starting from the “hidden meaning” associated to schema labels (i.e. class/attribute names) it is possible to discover relationships among the elements of different schemata. Lexical annotation (i.e. annotation w.r.t. a thesaurus/lexical resource) helps in associating a “meaning” to schema labels. However, accuracy of semi-automatic lexical annotation methods on real-world schemata suffers from the abundance of non-dictionary words such as compound nouns and word abbreviations. In this work, we address this problem by proposing a method to perform schema labels normalization which increases the number of comparable labels. Unlike other solutions, the method semi-automatically expands abbreviations and annotates compound terms, without a minimal manual effort. We empirically prove that our normalization method helps in the identification of similarities among schema elements of different data sources, thus improving schema matching accuracy