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Showing papers in "International Journal on Semantic Web and Information Systems in 2012"


Journal ArticleDOI
TL;DR: The authors review some of the recent developments on applying the semantic technologies based on machine-interpretable representation formalism to the Internet of Things.
Abstract: The Internet of Things IoT has recently received considerable interest from both academia and industry that are working on technologies to develop the future Internet. It is a joint and complex discipline that requires synergetic efforts from several communities such as telecommunication industry, device manufacturers, semantic Web, and informatics and engineering. Much of the IoT initiative is supported by the capabilities of manufacturing low-cost and energy-efficient hardware for devices with communication capacities, the maturity of wireless sensor network technologies, and the interests in integrating the physical and cyber worlds. However, the heterogeneity of the "Things" makes interoperability among them a challenging problem, which prevents generic solutions from being adopted on a global scale. Furthermore, the volume, velocity and volatility of the IoT data impose significant challenges to existing information systems. Semantic technologies based on machine-interpretable representation formalism have shown promise for describing objects, sharing and integrating information, and inferring new knowledge together with other intelligent processing techniques. However, the dynamic and resource-constrained nature of the IoT requires special design considerations to be taken into account to effectively apply the semantic technologies on the real world data. In this article the authors review some of the recent developments on applying the semantic technologies to IoT.

510 citations


Journal ArticleDOI
TL;DR: The authors propose an ontology-based approach for providing data access and query capabilities to streaming data sources, allowing users to express their needs at a conceptual level, independent of implementation and language-specific details.
Abstract: Sensor networks are increasingly being deployed in the environment for many different purposes. The observations that they produce are made available with heterogeneous schemas, vocabularies and data formats, making it difficult to share and reuse this data, for other purposes than those for which they were originally set up. The authors propose an ontology-based approach for providing data access and query capabilities to streaming data sources, allowing users to express their needs at a conceptual level, independent of implementation and language-specific details. In this article, the authors describe the theoretical foundations and technologies that enable exposing semantically enriched sensor metadata, and querying sensor observations through SPARQL extensions, using query rewriting and data translation techniques according to mapping languages, and managing both pull and push delivery modes.

144 citations


Journal ArticleDOI
TL;DR: The authors first describe the conceptual framework and architecture of Elementary to integrate different data resources and KBC techniques in a principled manner, and empirically show that this decomposition-based inference approach achieves higher performance than prior inference approaches.
Abstract: Researchers have approached knowledge-base construction KBC with a wide range of data resources and techniques. The authors present Elementary, a prototype KBC system that is able to combine diverse resources and different KBC techniques via machine learning and statistical inference to construct knowledge bases. Using Elementary, they have implemented a solution to the TAC-KBP challenge with quality comparable to the state of the art, as well as an end-to-end online demonstration that automatically and continuously enriches Wikipedia with structured data by reading millions of webpages on a daily basis. The authors describe several challenges and their solutions in designing, implementing, and deploying Elementary. In particular, the authors first describe the conceptual framework and architecture of Elementary to integrate different data resources and KBC techniques in a principled manner. They then discuss how they address scalability challenges to enable Web-scale deployment. The authors empirically show that this decomposition-based inference approach achieves higher performance than prior inference approaches. To validate the effectiveness of Elementary's approach to KBC, they experimentally show that its ability to incorporate diverse signals has positive impacts on KBC quality.

105 citations


Journal ArticleDOI
TL;DR: This paper proposes a new model to compute Information Content IC of a concept exploiting the taxonomic knowledge modeled in an ontology, and shows that the use of the authors' model produces, in most cases, more accurate similarity estimations than related works.
Abstract: The Information Content IC of a concept quantifies the amount of information it provides when appearing in a context. In the past, IC used to be computed as a function of concept appearance probabilities in corpora, but corpora-dependency and data sparseness hampered results. Recently, some other authors tried to overcome previous approaches, estimating IC from the knowledge modeled in an ontology. In this paper, the authors develop this idea, by proposing a new model to compute the IC of a concept exploiting the taxonomic knowledge modeled in an ontology. In comparison with related works, their proposal aims to better capture semantic evidences found in the ontology. To test the authors' approach, they have applied it to well-known semantic similarity measures, which were evaluated using standard benchmarks. Results show that the use of the authors' model produces, in most cases, more accurate similarity estimations than related works.

52 citations


Journal ArticleDOI
TL;DR: Adimen-SUMO is presented, an operational ontology to be used by first-order theorem provers in intelligent systems that require sophisticated reasoning capabilities e.g. Natural Language Processing, Knowledge Engineering, Semantic Web infrastructure, etc..
Abstract: In this paper, the authors present Adimen-SUMO, an operational ontology to be used by first-order theorem provers in intelligent systems that require sophisticated reasoning capabilities e.g. Natural Language Processing, Knowledge Engineering, Semantic Web infrastructure, etc.. Adimen-SUMO has been obtained by automatically translating around 88% of the original axioms of SUMO Suggested Upper Merged Ontology. Their main interest is to present in a practical way the advantages of using first-order theorem provers during the design and development of first-order ontologies. First-order theorem provers are applied as inference engines for reengineering a large and complex ontology in order to allow for formal reasoning. In particular, the authors' study focuses on providing first-order reasoning support to SUMO. During the process, they detect, explain and repair several important design flaws and problems of the SUMO axiomatization. As a by-product, they also provide general design decisions and good practices for creating operational first-order ontologies of any kind.

38 citations


Journal ArticleDOI
TL;DR: A new problem is proposed for ABox abduction and a new method for computing abductive solutions accordingly, which works in finite time for a very expressive DL, which underpins the W3C standard language OWL 2, and guarantees soundness and conditional completeness of computed results.
Abstract: ABox abduction is an important reasoning facility in Description Logics DLs. It finds all minimal sets of ABox axioms, called abductive solutions, which should be added to a background ontology to enforce entailment of an observation which is a specified set of ABox axioms. However, ABox abduction is far from practical by now because there lack feasible methods working in finite time for expressive DLs. To pave a way to practical ABox abduction, this paper proposes a new problem for ABox abduction and a new method for computing abductive solutions accordingly. The proposed problem guarantees finite number of abductive solutions. The proposed method works in finite time for a very expressive DL,, which underpins the W3C standard language OWL 2, and guarantees soundness and conditional completeness of computed results. Experimental results on benchmark ontologies show that the method is feasible and can scale to large ABoxes.

26 citations


Journal ArticleDOI
TL;DR: A novel unsupervised algorithm is presented that provides a more general treatment of the polysemy and synonymy problems and explicitly disambiguates polysemous relation phrases and groups synonymous ones and achieves significant improvement on recall compared to the previous method.
Abstract: The Web brings an open-ended set of semantic relations. Discovering the significant types is very challenging. Unsupervised algorithms have been developed to extract relations from a corpus without knowing the relation types in advance, but most rely on tagging arguments of predefined types. One recently reported system is able to jointly extract relations and their argument semantic classes, taking a set of relation instances extracted by an open IE Information Extraction algorithm as input. However, it cannot handle polysemy of relation phrases and fails to group many similar "synonymous" relation instances because of the sparseness of features. In this paper, the authors present a novel unsupervised algorithm that provides a more general treatment of the polysemy and synonymy problems. The algorithm incorporates various knowledge sources which they will show to be very effective for unsupervised relation extraction. Moreover, it explicitly disambiguates polysemous relation phrases and groups synonymous ones. While maintaining approximately the same precision, the algorithm achieves significant improvement on recall compared to the previous method. It is also very efficient. Experiments on a real-world dataset show that it can handle 14.7 million relation instances and extract a very large set of relations from the Web.

25 citations


Journal ArticleDOI
TL;DR: The authors' observation of intensity of indirect influence, propagated by n parallel spreaders and quantified by retweeting probability in two Twitter social networks, shows that complex contagion is validated globally but is violated locally.
Abstract: Social influence in social networks has been extensively researched. Most studies have focused on direct influence, while another interesting question can be raised as whether indirect influence exists between two users who're not directly connected in the network and what affects such influence. In addition, the theory of complex contagion tells us that more spreaders will enhance the indirect influence between two users. The authors' observation of intensity of indirect influence, propagated by n parallel spreaders and quantified by retweeting probability in two Twitter social networks, shows that complex contagion is validated globally but is violated locally. In other words, the retweeting probability increases non-monotonically with some local drops. A quantum cognition based probabilistic model is proposed to account for these local drops.

25 citations


Journal ArticleDOI
TL;DR: The experimental results show that the authors' method significantly improves the ranking quality in terms of capturing user preferences, compared with the state-of-the-art.
Abstract: This paper presents a novel ranking method for complex semantic relationship semantic association search based on user preferences The authors' method employs a learning-to-rank algorithm to capture each user's preferences Using this, it automatically constructs a personalized ranking function for the user The ranking function is then used to sort the results of each subsequent query by the user Query results that more closely match the user's preferences gain higher ranks Their method is evaluated using a real-world RDF knowledge base created from Freebase linked-open-data The experimental results show that the authors' method significantly improves the ranking quality in terms of capturing user preferences, compared with the state-of-the-art

17 citations


Journal ArticleDOI
TL;DR: This paper presents an integrated ontology driven agent based Sensor Web architecture for managing knowledge and system dynamism and an application case study on wildfire detection is used to illustrate the operation of the architecture.
Abstract: Sensor Web researchers are currently investigating middleware to aid in the dynamic discovery, integration and analysis of vast quantities of both high and low quality, but distributed and heterogeneous earth observation data. Key challenges being investigated include dynamic data integration and analysis, service discovery and semantic interoperability. However, few efforts deal with managing knowledge and system dynamism. Two emerging technologies that have shown promise in dealing with these issues are ontologies and software agents. This paper presents an integrated ontology driven agent based Sensor Web architecture for managing knowledge and system dynamism. An application case study on wildfire detection is used to illustrate the operation of the architecture.

17 citations


Journal ArticleDOI
TL;DR: A core ontological model for Semantic Sensor Web infrastructures that covers all the characteristics of distributed, heterogeneous, and web-accessible sensor data and has been built with a focus on reusability is described.
Abstract: Semantic Sensor Web infrastructures use ontology-based models to represent the data that they manage; however, up to now, these ontological models do not allow representing all the characteristics of distributed, heterogeneous, and web-accessible sensor data. This paper describes a core ontological model for Semantic Sensor Web infrastructures that covers these characteristics and that has been built with a focus on reusability. This ontological model is composed of different modules that deal, on the one hand, with infrastructure data and, on the other hand, with data from a specific domain, that is, the coastal flood emergency planning domain. The paper also presents a set of guidelines, followed during the ontological model development, to satisfy a common set of requirements related to modelling domain-specific features of interest and properties. In addition, the paper includes the results obtained after an exhaustive evaluation of the developed ontologies along different aspects i.e., vocabulary, syntax, structure, semantics, representation, and context.

Journal ArticleDOI
TL;DR: This paper presents an approach for the semantic annotation of RESTful services in the geospatial domain, by using a combination of resources and services: a cross-domain knowledge base like DBpedia, two domain ontologies like GeoNames and the WGS84 vocabulary, and suggestion and synonym services.
Abstract: In this paper the authors present an approach for the semantic annotation of RESTful services in the geospatial domain. Their approach automates some stages of the annotation process, by using a combination of resources and services: a cross-domain knowledge base like DBpedia, two domain ontologies like GeoNames and the WGS84 vocabulary, and suggestion and synonym services. The authors' approach has been successfully evaluated with a set of geospatial RESTful services obtained from ProgrammableWeb.com, where geospatial services account for a third of the total amount of services available in this registry.

Journal ArticleDOI
TL;DR: A novel pattern-based framework to diversify search results, where each pattern is a set of semantically related terms covering the same subtopic, using a maximal frequent pattern mining algorithm to extract the patterns from retrieval results of the query.
Abstract: Traditional information retrieval models do not necessarily provide users with optimal search experience because the top ranked documents may contain excessively redundant information. Therefore, satisfying search results should be not only relevant to the query but also diversified to cover different subtopics of the query. In this paper, the authors propose a novel pattern-based framework to diversify search results, where each pattern is a set of semantically related terms covering the same subtopic. They first apply a maximal frequent pattern mining algorithm to extract the patterns from retrieval results of the query. The authors then propose to model a subtopic with either a single pattern or a group of similar patterns. A profile-based clustering method is adapted to group similar patterns based on their context information. The search results are then diversified using the extracted subtopics. Experimental results show that the proposed pattern-based methods are effective to diversify the search results.

Journal ArticleDOI
TL;DR: The authors aim to turn folksonomies into knowledge structures where tag meanings are identified, and relations between them are asserted, and use DBpedia as a general knowledge base from which they leverage its multilingual capabilities.
Abstract: Folksonomies emerge as the result of the free tagging activity of a large number of users over a variety of resources. They can be considered as valuable sources from which it is possible to obtain emerging vocabularies that can be leveraged in knowledge extraction tasks. However, when it comes to understanding the meaning of tags in folksonomies, several problems mainly related to the appearance of synonymous and ambiguous tags arise, specifically in the context of multilinguality. The authors aim to turn folksonomies into knowledge structures where tag meanings are identified, and relations between them are asserted. For such purpose, they use DBpedia as a general knowledge base from which they leverage its multilingual capabilities.

Journal ArticleDOI
Alfio Gliozzo1, Aditya Kalyanpur1
TL;DR: This paper addresses a specific subtask of Deep QA, consisting of predicting the Lexical Answer Type LAT of a question, with a completely unsupervised approach based on PRISMATIC, a large-scale lexical knowledge base automatically extracted from a Web corpus.
Abstract: Automatic open-domain Question Answering has been a long standing research challenge in the AI community. IBM Research undertook this challenge with the design of the DeepQA architecture and the implementation of Watson. This paper addresses a specific subtask of Deep QA, consisting of predicting the Lexical Answer Type LAT of a question. Our approach is completely unsupervised and is based on PRISMATIC, a large-scale lexical knowledge base automatically extracted from a Web corpus. Experiments on the Jeopardy! data shows that it is possible to correctly predict the LAT in a substantial number of questions. This approach can be used for general purpose knowledge acquisition tasks such as frame induction from text.

Journal ArticleDOI
TL;DR: The authors study SPARQL-DLNOT, an extension of one of these query languages, SPARql-DL, and present novel evaluation and optimization techniques for efficient SParQL- DLNOT execution and a novel graph-based visualization that simplifies query construction and maintenance.
Abstract: Web Ontology Language ontologies become more and more popular in complex domain modeling for their high expressiveness, flexibility and well defined semantics. Although query languages adequate in expressiveness to OWL reasoning capabilities were introduced before, their implementations are rather limited. In this paper, the authors study SPARQL-DLNOT, an extension of one of these query languages, SPARQL-DL, and present novel evaluation and optimization techniques for efficient SPARQL-DLNOT execution. As queries become complex easily, they also present a novel graph-based visualization that simplifies query construction and maintenance. Presented techniques and algorithms were implemented in the Pellet reasoner and in their novel Protege plug-in OWL2Query.