Finite-discrete element modelling of masonry infill walls subjected to out-of-plane loads

In this paper, the out-of-plane response of infill walls is investigated by means of non-linear monotonic (push-over) analyses through a combined finite and discrete modelling approach. The model accounts for material deformability, crack formation, sliding, separa-tion and formation of new contacts. Masonry units are modelled as finite elements, and differ-ent material models are assumed for the masonry. Contact between masonry units, and between masonry and frame elements is modelled by means of interfaces, which permit tan-gential motion with frictional sliding. Frame elements are modelled by means of a linear-elastic material. The results of the numerical analyses are compared with those of experimen-tal tests available in the literature. The advantages and disadvantages of the adopted model-ling strategy are investigated

Automatic annotation for mapping discovery in data integration systems (Extended abstract)

In this article we present CWSD (Combined Word Sense Disambiguation) a method and a software tool for enabling automatic lexical annotation of local (structured and semi-structured) data sources in a data integration system. CWSD is based on the exploitation of WordNet Domains and the lexical and structural knowledge of the data sources. The method extends the semi-automatic lexical annotation module of the MOMIS data integration system. The distinguishing feature of the method is its independence or low dependence of a human intervention. CWSD is a valid method to satisfy two important tasks: (1) the source lexical annotation process, i.e. the operation of associating an element of a lexical reference database (WordNet) to all source elements, (2) the discover of mappings among concepts of distributed data sources/ontologies

Results on Alternating-Time Temporal Logics with Linear Past

We investigate the succinctness gap between two known equally-expressive and different linear-past extensions of standard CTL^* (resp., ATL^*). We establish by formal non-trivial arguments that the "memoryful" linear-past extension (the history leading to the current state is taken into account) can be exponentially more succinct than the standard "local" linear-past extension (the history leading to the current state is forgotten). As a second contribution, we consider the ATL-like fragment, denoted ATL_{lp}, of the known "memoryful" linear-past extension of ATL^{*}. We show that ATL_{lp} is strictly more expressive than ATL, and interestingly, it can be exponentially more succinct than the more expressive logic ATL^{*}. Moreover, we prove that both satisfiability and model-checking for the logic ATL_{lp} are Exptime-complete

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

Rising Waters: Local Implications and Actions

According to Mark Lowery, Climate Policy Analyst for the New York State Department of Environmental Conservation, climate change influences warmer water, causing the oceans to take up more space and causing a higher percentage of water vapor in the atmosphere, which creates more powerful storms. That being said, it is evident that climate change has contributed to rising sea levels and more intense storms so it is important to understand the dangers associated with this issue and what possible options exist to minimize its impacts.Our research investigates the threat of rising waters in the lower New York coastal region and what plans have been proposed to protect the coast. By reaching out to municipalities along the Westchester and New York City coastlines, we have examined how these cities and towns are planning to mitigate the threat of rising waters. The goal of this research is to summarize some of the preventive actions being considered by New York City and Westchester County. We hope that our findings will encourage communities without action plans to develop protective strategies and others to adopt established designs that may benefit them