An automated tool for semantic accessing to formal software models

Many software engineers have found that it is difficult to understand, incorporate and use different formal models consistently in the process of software developments, especially for large and complex software systems. This is mainly due to the complex mathematical nature of the formal methods and the lack of tool support. It is highly desirable to have software models and their related software artefacts systematically connected and used collaboratively, rather than in isolation. The success of the Semantic Web, as the next generation of Web technology, can have profound impact on the environment for formal software development. It allows both the software engineers and machines to understand the content of formal models and supports more effective software design in terms of understanding, sharing and reusing in a distributed manner. To realise the full potential of the Semantic Web in formal software development, effectively creating proper semantic metadata for formal software models and their related software artefacts is crucial. This paper proposed a framework that allows users to interconnect the knowledge about formal software models and other related documents using the semantic technology. We first propose a methodology with tool support is proposed to automatically derive ontological metadata from formal software models and semantically describe them. We then develop a Semantic Web environment for representing and sharing formal Z/OZ models. A method with prototype tool is presented to enhance semantic query to software models and other artefacts. © 2014.

LEAPS:a semantic web and linked data framework for the Algal biomass domain

In this paper we present, LEAPS, a Semantic Web and Linked data framework for searching and visualising datasets from the domain of Algal biomass. LEAPS provides tailored interfaces to explore algal biomass datasets via REST services and a SPARQL endpoint for stakeholders in the domain of algal biomass. The rich suite of datasets include data about potential algal biomass cultivation sites, sources of CO2, the pipelines connecting the cultivation sites to the CO2 sources and a subset of the biological taxonomy of algae derived from the world's largest online information source on algae.

Semantic Search of Internet Information Resources on Base of Ontologies and Multilinguistic Thesauruses

The approaches to the analysis of various information resources pertinent to user requirements at a semantic level are determined by the thesauruses of the appropriate subject domains. The algorithms of formation and normalization of the multilinguistic thesaurus, and also methods of their comparison are given.

INFRAWEBS Semantic Web Service Development on the Base of Knowledge Management Layer

The paper gives an overview about the ongoing FP6-IST INFRAWEBS project and describes the main layers and software components embedded in an application oriented realisation framework. An important part of INFRAWEBS is a Semantic Web Unit (SWU) – a collaboration platform and interoperable middleware for ontology-based handling and maintaining of SWS. The framework provides knowledge about a specific domain and relies on ontologies to structure and exchange this knowledge to semantic service development modules. INFRAWEBS Designer and Composer are sub-modules of SWU responsible for creating Semantic Web Services using Case-Based Reasoning approach. The Service Access Middleware (SAM) is responsible for building up the communication channels between users and various other modules. It serves as a generic middleware for deployment of Semantic Web Services. This software toolset provides a development framework for creating and maintaining the full-life-cycle of Semantic Web Services with specific application support.

Semantic Description of Web Services and Possibilities of BPEL4WS

The using of the upsurge of semantics web technologies gives a possibility for an increasing of the flexibility, extensibility and consistency of the existent industrial standards for modeling of web services. In the paper the types of semantic description of web services and the degree of their realization in BPEL4WS (Business Process Execution Language for Web Services) respectively on the abstract and executable level are treated. The methods for using of BPEL4WS for the purposes of semantic web services in the direction of their semi-automatic integration are suggested.

INFRAWEBS BPEL-Based Editor for Creating the Semantic Web Services Description

INFRAWEBS project [INFRAWEBS] considers usage of semantics for the complete lifecycle of Semantic Web processes, which represent complex interactions between Semantic Web Services. One of the main initiatives in the Semantic Web is WSMO framework, aiming at describing the various aspects related to Semantic Web Services in order to enable the automation of Web Service discovery, composition, interoperation and invocation. In the paper the conceptual architecture for BPEL-based INFRAWEBS editor is proposed that is intended to construct a part of WSMO descriptions of the Semantic Web Services. The semantic description of Web Services has to cover Data, Functional, Execution and QoS semantics. The representation of Functional semantics can be achieved by adding the service functionality to the process description. The architecture relies on a functional (operational) semantics of the Business Process Execution Language for Web Services (BPEL4WS) and uses abstract state machine (ASM) paradigm. This allows describing the dynamic properties of the process descriptions in terms of partially ordered transition rules and transforming them to WSMO framework.

Semantic reasoning for autonomic IT systems

Autonomic systems are required to adapt continually to changing environments and user goals. This process involves the real-Time update of the system's knowledge base, which should therefore be stored in a machine-readable format and automatically checked for consistency. OWL ontologies meet both requirements, as they represent collections of knowl- edge expressed in FIrst order logic, and feature embedded reasoners. To take advantage of these OWL ontology char- acteristics, this PhD project will devise a framework com- prising a theoretical foundation, tools and methods for de- veloping knowledge-centric autonomic systems. Within this framework, the knowledge storage and maintenance roles will be fulfilled by a specialised class of OWL ontologies. ©2014 ACM.

Paronyms for Accelerated Correction of Semantic Errors

* Work done under partial support of Mexican Government (CONACyT, SNI), IPN (CGPI, COFAA) and Korean Government (KIPA Professorship for Visiting Faculty Positions). The second author is currently on Sabbatical leave at Chung-Ang University.

Computer-aided System of Semantic Text Analysis of a Technical Specification

The given work is devoted to development of the computer-aided system of semantic text analysis of a technical specification. The purpose of this work is to increase efficiency of software engineering based on automation of semantic text analysis of a technical specification. In work it is offered and investigated the model of the analysis of the text of the technical project is submitted, the attribute grammar of a technical specification, intended for formalization of limited Russian is constructed with the purpose of analysis of offers of text of a technical specification, style features of the technical project as class of documents are considered, recommendations on preparation of text of a technical specification for the automated processing are formulated. The computer-aided system of semantic text analysis of a technical specification is considered. This system consists of the following subsystems: preliminary text processing, the syntactic and semantic analysis and construction of software models, storage of documents and interface.

Computer Support of Semantic Text Analysis of a Technical Specification on Designing Software

The given work is devoted to development of the computer-aided system of semantic text analysis of a technical specification. The purpose of this work is to increase efficiency of software engineering based on automation of semantic text analysis of a technical specification. In work it is offered and investigated a technique of the text analysis of a technical specification is submitted, the expanded fuzzy attribute grammar of a technical specification, intended for formalization of limited Russian language is constructed with the purpose of analysis of offers of text of a technical specification, style features of the technical specification as class of documents are considered, recommendations on preparation of text of a technical specification for the automated processing are formulated. The computer-aided system of semantic text analysis of a technical specification is considered. This system consist of the following subsystems: preliminary text processing, the syntactic and semantic analysis and construction of software models, storage of documents and interface.

The topics they are a-changing - characterising topics with time-stamped semantic graphs

DBpedia has become one of the major sources of structured knowledge extracted from Wikipedia. Such structures gradually re-shape the representation of Topics as new events relevant to such topics emerge. Such changes make evident the continuous evolution of topic representations and introduce new challenges to supervised topic classification tasks, since labelled data can rapidly become outdated. Here we analyse topic changes in DBpedia and propose the use of semantic features as a more stable representation of a topic. Our experiments show promising results in understanding how the relevance of features to a topic changes over time.

Linked knowledge sources for topic classification of microposts:a semantic graph-based approach

Short text messages a.k.a Microposts (e.g. Tweets) have proven to be an effective channel for revealing information about trends and events, ranging from those related to Disaster (e.g. hurricane Sandy) to those related to Violence (e.g. Egyptian revolution). Being informed about such events as they occur could be extremely important to authorities and emergency professionals by allowing such parties to immediately respond. In this work we study the problem of topic classification (TC) of Microposts, which aims to automatically classify short messages based on the subject(s) discussed in them. The accurate TC of Microposts however is a challenging task since the limited number of tokens in a post often implies a lack of sufficient contextual information. In order to provide contextual information to Microposts, we present and evaluate several graph structures surrounding concepts present in linked knowledge sources (KSs). Traditional TC techniques enrich the content of Microposts with features extracted only from the Microposts content. In contrast our approach relies on the generation of different weighted semantic meta-graphs extracted from linked KSs. We introduce a new semantic graph, called category meta-graph. This novel meta-graph provides a more fine grained categorisation of concepts providing a set of novel semantic features. Our findings show that such category meta-graph features effectively improve the performance of a topic classifier of Microposts. Furthermore our goal is also to understand which semantic feature contributes to the performance of a topic classifier. For this reason we propose an approach for automatic estimation of accuracy loss of a topic classifier on new, unseen Microposts. We introduce and evaluate novel topic similarity measures, which capture the similarity between the KS documents and Microposts at a conceptual level, considering the enriched representation of these documents. Extensive evaluation in the context of Emergency Response (ER) and Violence Detection (VD) revealed that our approach outperforms previous approaches using single KS without linked data and Twitter data only up to 31.4% in terms of F1 measure. Our main findings indicate that the new category graph contains useful information for TC and achieves comparable results to previously used semantic graphs. Furthermore our results also indicate that the accuracy of a topic classifier can be accurately predicted using the enhanced text representation, outperforming previous approaches considering content-based similarity measures. © 2014 Elsevier B.V. All rights reserved.

Automatic Discovery of Word Semantic Relations

In this paper, we propose an unsupervised methodology to automatically discover pairs of semantically related words by highlighting their local environment and evaluating their semantic similarity in local and global semantic spaces. This proposal di®ers from previous research as it tries to take the best of two different methodologies i.e. semantic space models and information extraction models. It can be applied to extract close semantic relations, it limits the search space and it is unsupervised.

Cultural Knowledge for Named Entity Disambiguation: A Graph-Based Semantic Relatedness Approach

One of the ultimate aims of Natural Language Processing is to automate the analysis of the meaning of text. A fundamental step in that direction consists in enabling effective ways to automatically link textual references to their referents, that is, real world objects. The work presented in this paper addresses the problem of attributing a sense to proper names in a given text, i.e., automatically associating words representing Named Entities with their referents. The method for Named Entity Disambiguation proposed here is based on the concept of semantic relatedness, which in this work is obtained via a graph-based model over Wikipedia. We show that, without building the traditional bag of words representation of the text, but instead only considering named entities within the text, the proposed method achieves results competitive with the state-of-the-art on two different datasets.

Semantic patterns for sentiment analysis of Twitter

Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets. We evaluate our approach with tweet- and entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19% at the tweet-level and 7.5% at the entity-level in average F-measure.