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

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

Schema Label Normalization for Improving Schema Matching

Schema matching is the problem of finding relationships among concepts across heterogeneous data sources that are heterogeneous in format and in structure. Starting from the \u201chidden meaning\u201d associated with 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 \u201cmeaning\u201d to schema labels.However, the performance of semi-automatic lexical annotation methods on real-world schemata suffers from the abundance of non-dictionary words such as compound nouns, abbreviations, and acronyms. We address this problem by proposing a method to perform schema label normalization which increases the number of comparable labels. The method semi-automatically expands abbreviations/acronyms and annotates compound nouns, with 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 results

Dealing with Uncertainty in Lexical Annotation

We present ALA, a tool for the automatic lexical annotation (i.e.annotation w.r.t. a thesaurus/lexical resource) of structured and semi-structured data sources and the discovery of probabilistic lexical relationships in a data integration environment. ALA performs automatic lexical annotation through the use of probabilistic annotations, i.e. an annotation is associated to a probability value. By performing probabilistic lexical annotation, we discover probabilistic inter-sources lexical relationships among schema elements. ALA extends the lexical annotation module of the MOMIS data integration system. However, it may be applied in general in the context of schema mapping discovery, ontology merging and data integration system and it is particularly suitable for performing “on-the-fly” data integration or probabilistic ontology matching

An Ontology-Based Data Integration System for Data and Multimedia Sources

Data integration is the problem of combining data residing at distributed heterogeneous sources, including multimedia sources, and providing the user with a unified view of these data. Ontology based Data Integration involves the use of ontology(s) to effectively combine data and information from multiple heterogeneous sources [16]. Ontologies, with respect to the integration of data sources, can be used for the identification and association of semantically correspond- ing information concepts, i.e. for the definition of semantic mappings among concepts of the information sources. MOMIS is a Data Integration System which performs in-formation extraction and integration from both structured and semi- structured data sources [6]. In [5] MOMIS was extended to manage “traditional” and “multimedia” data sources at the same time. STASIS is a comprehensive application suite which allows enterprises to simplify the mapping process between data schemas based on semantics [1]. Moreover, in STASIS, a general framework to perform Ontology-driven Semantic Mapping has been pro-posed [7]. This paper describes the early effort to combine the MOMIS and the STASIS frameworks in order to obtain an effective approach for Ontology-Based Data Integration for data and multimedia sources

Automatic annotation in data integration systems

We propose a CWSD (Combined Word Sense Disambiguation) algorithm for the automatic annotation of structured and semi-structured data sources. Rather than being targeted to textual data sources like most of the traditional WSD algorithms found in the literature, our algorithm can exploit information coming from the structure of the sources together with the lexical knowledge associated with the terms (elements of the schemata)

Loss-of-rescue of Ryr1I4895T-related pathology by the genetic inhibition of the ER stress response mediator CHOP

RYR1 is the gene encoding the ryanodine receptor 1, a calcium release channel of the endo/sarcoplasmic reticulum. I4898T in RYR1 is one of the most common mutations that give rise to central core disease (CCD), with a variable phenotype ranging from mild to severe myopathy to lethal early-onset core-rod myopathy. Mice with the corresponding I4895T mutation in Ryr1 present mild myopathy when the mutation is heterozygous while I4895T homozygous is perinatal-lethal. Here we show that skeletal muscles of I4895T homozygous mice at birth present signs of stress of the endoplasmic reticulum (ER stress) and of the related unfolded protein response (UPR) with increased levels of the maladaptive mediators CHOP and ERO1. To gain information on the role of CHOP in the pathogenesis of RYR1I4895T-related myopathy, we generated compound Ryr1I4895T, Chop knock-out (-/-) mice. However, the genetic deletion of Chop, although it attenuates ER stress in the skeletal muscle of the newborns, does not rescue any phenotypic or functional features of Ryr1I4895T in mice: neither the perinatal-lethal phenotype nor the inability of Ryr1I4895T to respond to its agonist caffeine, but protects from ER stress-induced apoptosis. These findings suggest that genetic deletion of the ER stress response mediator CHOP is not sufficient to counteract the pathological Ryr1I4895T phenotype

Innovative Caciocavallo cheeses made from a mixture of cow milk with ewe or goat milk.

This study assessed and compared the physicochemical, microbiological, and sensorial characteristics of Caciocavallo cheeses, made from cow milk and a mixture of cow with ewe or goat milk, during ripening. Different cheese-making trials were carried out on an industrial scale following the standard procedure of pasta filata cheeses, with some modifications. The percentage of the different added milk to cow milk influenced compositional and nutritional characteristics of the innovative products, leading to a satisfactory microbiological and sensorial quality