AquaLog : an ontology-driven question answering system to interface the semantic web

The semantic web (SW) vision is one in which rich, ontology-based semantic markup will become widely available. The availability of semantic markup on the web opens the way to novel, sophisticated forms of question answering. AquaLog is a portable question-answering system which takes queries expressed in natural language (NL) and an ontology as input, and returns answers drawn from one or more knowledge bases (KB). AquaLog presents an elegant solution in which different strategies are combined together in a novel way. AquaLog novel ontology-based relation similarity service makes sense of user queries.

Disturbing visual working memory:electrophysiological evidence for a role of the prefrontal cortex in recovery from interference

Single cell recordings in monkeys support the notion that the lateral prefrontal cortex (PFC) controls reactivation of visual working memory representations when rehearsal is disrupted. In contrast, recent fMRI findings yielded a double dissociation for PFC and the medial temporal lobe (MTL) in a letter working memory task. PFC was engaged in interference protection during reactivation while MTL was prominently involved in the retrieval of the letter representations. We present event-related potential data (ERP) that support PFC involvement in the top-down control of reactivation during a visual working memory task with endogenously triggered recovery after visual interference. A differentiating view is proposed for the role of PFC in working memory with respect to endogenous/exogenous control and to stimulus type. General implications for binding and retention mechanisms are discussed.

Semi-supervised learning of statistical models for natural language understanding

Natural language understanding is to specify a computational model that maps sentences to their semantic mean representation. In this paper, we propose a novel framework to train the statistical models without using expensive fully annotated data. In particular, the input of our framework is a set of sentences labeled with abstract semantic annotations. These annotations encode the underlying embedded semantic structural relations without explicit word/semantic tag alignment. The proposed framework can automatically induce derivation rules that map sentences to their semantic meaning representations. The learning framework is applied on two statistical models, the conditional random fields (CRFs) and the hidden Markov support vector machines (HM-SVMs). Our experimental results on the DARPA communicator data show that both CRFs and HM-SVMs outperform the baseline approach, previously proposed hidden vector state (HVS) model which is also trained on abstract semantic annotations. In addition, the proposed framework shows superior performance than two other baseline approaches, a hybrid framework combining HVS and HM-SVMs and discriminative training of HVS, with a relative error reduction rate of about 25% and 15% being achieved in F-measure.

Morphological-compound dysgraphia in an aphasic patient:"A wild write through the lexicon"

We describe the case of a dysgraphic aphasic individual-S.G.W.-who, in writing to dictation, produced high rates of formally related errors consisting of both lexical substitutions and what we call morphological-compound errors involving legal or illegal combinations of morphemes. These errors were produced in the context of a minimal number of semantic errors. We could exclude problems with phonological discrimination and phonological short-term memory. We also excluded rapid decay of lexical information and/or weak activation of word forms and letter representations since S.G.W.'s spelling showed no effect of delay and no consistent length effects, but, instead, paradoxical complexity effects with segmental, lexical, and morphological errors that were more complex than the target. The case of S.G.W. strongly resembles that of another dysgraphic individual reported in the literature-D.W.-suggesting that this pattern of errors can be replicated across patients. In particular, both patients show unusual errors resulting in the production of neologistic compounds (e.g., "bed button" in response to "bed"). These patterns can be explained if we accept two claims: (a) Brain damage can produce both a reduction and an increase in lexical activation; and (b) there are direct connections between phonological and orthographic lexical representations (a third spelling route). We suggest that both patients are suffering from a difficulty of lexical selection resulting from excessive activation of formally related lexical representations. This hypothesis is strongly supported by S.G.W.'s worse performance in spelling to dictation than in written naming, which shows that a phonological input, activating a cohort of formally related lexical representations, increases selection difficulties. © 2014 Taylor & Francis.

Event trigger identification for biomedical events extraction using domain knowledge

Motivation: In molecular biology, molecular events describe observable alterations of biomolecules, such as binding of proteins or RNA production. These events might be responsible for drug reactions or development of certain diseases. As such, biomedical event extraction, the process of automatically detecting description of molecular interactions in research articles, attracted substantial research interest recently. Event trigger identification, detecting the words describing the event types, is a crucial and prerequisite step in the pipeline process of biomedical event extraction. Taking the event types as classes, event trigger identification can be viewed as a classification task. For each word in a sentence, a trained classifier predicts whether the word corresponds to an event type and which event type based on the context features. Therefore, a well-designed feature set with a good level of discrimination and generalization is crucial for the performance of event trigger identification. Results: In this article, we propose a novel framework for event trigger identification. In particular, we learn biomedical domain knowledge from a large text corpus built from Medline and embed it into word features using neural language modeling. The embedded features are then combined with the syntactic and semantic context features using the multiple kernel learning method. The combined feature set is used for training the event trigger classifier. Experimental results on the golden standard corpus show that >2.5% improvement on F-score is achieved by the proposed framework when compared with the state-of-the-art approach, demonstrating the effectiveness of the proposed framework. © 2014 The Author 2014. The source code for the proposed framework is freely available and can be downloaded at http://cse.seu.edu.cn/people/zhoudeyu/ETI_Sourcecode.zip.

A survey of semantic web services formalisms

The field of Semantic Web Services (SWS) has been recognized as one of the most promising areas of emergent research within the Semantic Web initiative, exhibiting an extensive commercial potential and attracting significant attention from both industry and the research community. Currently, there exist several different frameworks and languages for formally describing a Web Service: Web Ontology Language for Services (OWL-S), Web Service Modelling Ontology (WSMO) and Semantic Annotations for the Web Services Description Language (SAWSDL) are the most important approaches. To the inexperienced user, choosing the appropriate platform for a specific SWS application may prove to be challenging, given a lack of clear separation between the ideas promoted by the associated research communities. In this paper, we systematically compare OWL-S, WSMO and SAWSDL from various standpoints, namely, that of the service requester and provider as well as the broker-based view. The comparison is meant to help users to better understand the strengths and limitations of these different approaches to formalizing SWS, and to choose the most suitable solution for a given application. Copyright © 2015 John Wiley & Sons, Ltd.

BOWL:augmenting the semantic web with beliefs

As the Semantic Web is an open, complex and constantly evolving medium, it is the norm, but not exception that information at different sites is incomplete or inconsistent. This poses challenges for the engineering and development of agent systems on the Semantic Web, since autonomous software agents need to understand, process and aggregate this information. Ontology language OWL provides core language constructs to semantically markup resources on the Semantic Web, on which software agents interact and cooperate to accomplish complex tasks. However, as OWL was designed on top of (a subset of) classic predicate logic, it lacks the ability to reason about inconsistent or incomplete information. Belief-augmented Frames (BAF) is a frame-based logic system that associates with each frame a supporting and a refuting belief value. In this paper, we propose a new ontology language Belief-augmented OWL (BOWL) by integrating OWL DL and BAF to incorporate the notion of confidence. BOWL is paraconsistent, hence it can perform useful reasoning services in the presence of inconsistencies and incompleteness. We define the abstract syntax and semantics of BOWL by extending those of OWL. We have proposed reasoning algorithms for various reasoning tasks in the BOWL framework and we have implemented the algorithms using the constraint logic programming framework. One example in the sensor fusion domain is presented to demonstrate the application of BOWL.

A survey of semantic web services formalisms

The field of Semantic Web Services (SWS) has been recognized as one of the most promising areas of emergent research within the Semantic Web (SW) initiative, exhibiting an extensive commercial potential, and attracting significant attention from both industry and the research community. Currently, there exist several different frameworks and languages for formally describing a Web Service: OWL-S (Web Ontology Language for Services), WSMO (Web Service Modeling Ontology) and SAWSDL (Semantic Annotations for the Web Services Description Language) are the most important approaches. To the inexperienced user, choosing the appropriate paradigm for a specific SWS application may prove to be challenging, given a lack of clear separation between the ideas promoted by the associated research communities. In this paper, we systematically compare OWL-S, WSMO and SAWSDL from various standpoints, namely that of the service requester and provider as well as the broker based view. The comparison is meant to help users to better understand the strengths and limitations of these different approaches to formalising SWS, and to choose the most suitable solution for a given use case. © 2013 IEEE.

Reconciling OWL and non-monotonic rules for the semantic web

We propose a description logic extending SROIQ (the description logic underlying OWL 2 DL) and at the same time encompassing some of the most prominent monotonic and nonmonotonic rule languages, in particular Datalog extended with the answer set semantics. Our proposal could be considered a substantial contribution towards fulfilling the quest for a unifying logic for the Semantic Web. As a case in point, two non-monotonic extensions of description logics considered to be of distinct expressiveness until now are covered in our proposal. In contrast to earlier such proposals, our language has the "look and feel" of a description logic and avoids hybrid or first-order syntaxes. © 2012 The Author(s).

Semantic enabled sensor network design

Wireless Sensor Network (WSN) systems have become more and more popular in our modern life. They have been widely used in many areas, such as smart homes/buildings, context-aware devices, military applications, etc. Despite the increasing usage, there is a lack of formal description and automated verification for WSN system design. In this paper, we present an approach to support the rigorous verification of WSN modeling using the Semantic Web technology We use Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL) to define a meta-ontology for the modeling of WSN systems. Furthermore, we apply ontology reasoners to perform automated verification on customized WSN models and their instances. We demonstrate and evaluate our approach through a Light Control System (LCS) as the case study.

Attitudes to language in literary sources:beyond post-colonialism in nigerian literature

All societies display attitudes to (varieties of) languages that tell us about the relative status of the groups that are associated to them. One method to document these is the systematic study of public discourses, including literary production. How (varieties of) languages are used, mentioned and characterised in a literary work tells us about their social status, and any change in this status should therefore be followed by changes in judgements on languages. This is demonstrated by the present paper with reference to the language attitudes in Nigeria, on the basis of two iconic Nigerian novels 2004 Purple Hibiscus and in 1958 classic postcolonial Things Fall Apart, separated by nearly fifty years. Ibo as well as Pidgin, Nigerian and European Englishes are presented in Purple Hibiscus in nuanced complementary configurations. A strong axiological polarisation is by contrast offered in Things Fall Apart between Ibo speakers and Ibo interpreters who are presented as cruel and ridiculous traitors siding with the English colonising power whose language, curiously, is not commented upon. Showing how a replicable method applied to language judgements can document social organisation and change, these results validate the view that the Nigerian society and culture has moved beyond the historically situated postcolonialist movement to embrace a globalised paradigm. © 2010 Taylor & Francis.

The development of a semantic model for the interpretation of mathematics including the use of technology

The semantic model developed in this research was in response to the difficulty a group of mathematics learners had with conventional mathematical language and their interpretation of mathematical constructs. In order to develop the model ideas from linguistics, psycholinguistics, cognitive psychology, formal languages and natural language processing were investigated. This investigation led to the identification of four main processes: the parsing process, syntactic processing, semantic processing and conceptual processing. The model showed the complex interdependency between these four processes and provided a theoretical framework in which the behaviour of the mathematics learner could be analysed. The model was then extended to include the use of technological artefacts into the learning process. To facilitate this aspect of the research, the theory of instrumentation was incorporated into the semantic model. The conclusion of this research was that although the cognitive processes were interdependent, they could develop at different rates until mastery of a topic was achieved. It also found that the introduction of a technological artefact into the learning environment introduced another layer of complexity, both in terms of the learning process and the underlying relationship between the four cognitive processes.

Ontology forecasting in scientific literature:semantic concepts prediction based on innovation-adoption priors

The ontology engineering research community has focused for many years on supporting the creation, development and evolution of ontologies. Ontology forecasting, which aims at predicting semantic changes in an ontology, represents instead a new challenge. In this paper, we want to give a contribution to this novel endeavour by focusing on the task of forecasting semantic concepts in the research domain. Indeed, ontologies representing scientific disciplines contain only research topics that are already popular enough to be selected by human experts or automatic algorithms. They are thus unfit to support tasks which require the ability of describing and exploring the forefront of research, such as trend detection and horizon scanning. We address this issue by introducing the Semantic Innovation Forecast (SIF) model, which predicts new concepts of an ontology at time t + 1, using only data available at time t. Our approach relies on lexical innovation and adoption information extracted from historical data. We evaluated the SIF model on a very large dataset consisting of over one million scientific papers belonging to the Computer Science domain: the outcomes show that the proposed approach offers a competitive boost in mean average precision-at-ten compared to the baselines when forecasting over 5 years.