Search (46 results, page 2 of 3)

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Content

Inhalt YAMATO: Yet Another More Advanced Top-level Ontology (invited talk) - Riichiro Mizoguchi A Visual Analytics Approach to Augmenting Formal Concepts with Relational Background Knowledge in a Biological Domain - Elma Akand, Michael Bain, Mark Temple Combining Ontologies And Natural Language - Wolf Fischer, Bernhard Bauer Comparison of Thesauri and Ontologies from a Semiotic Perspective - Daniel Kless, Simon Milton Fast Classification in Protégé: Snorocket as an OWL2 EL Reasoner - Michael Lawley, Cyril Bousquet Ontological Support for Consistency Checking of Engineering Design Workflows - Franz Maier, Wolfgang Mayer, Markus Stumptner Ontology Inferencing Rules and Operations in Conceptual Structure Theory - Philip H.P. Nguyen, Ken Kaneiwa, Minh-Quang Nguyen An Axiomatisation of Basic Formal Ontology with Projection Functions - Kerry Trentelman, Barry Smith Making Sense of Spreadsheet Data: A Case of Semantic Water Data Translation - Yanfeng Shu, David Ratcliffe, Geoffrey Squire, Michael Compton

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Abstract

Thesauri that are "ontologized" into OWL-DL semantics are highly amenable to modeling errors resulting from falsely interpreting existential restrictions. We investigated the OWL-DL representation of the NCI Thesaurus (NCIT) in order to assess the correctness of existential restrictions. A random sample of 354 axioms using the someValuesFrom operator was taken. According to a rating performed by two domain experts, roughly half of these examples, and in consequence more than 76,000 axioms in the OWL-DL version, make incorrect assertions if interpreted according to description logics semantics. These axioms therefore constitute a huge source for unintended models, rendering most logic-based reasoning unreliable. After identifying typical error patterns we discuss some possible improvements. Our recommendation is to either amend the problematic axioms in the OWL-DL formalization or to consider some less strict representational format.

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Abstract

In this paper, I have traced a series of evolutionary adaptations of FOL motivated entirely by its use by knowledge engineers to represent and share information on the Web culminating in the development of Common Logic. While the primary goal in this paper has been to document this evolution, it is arguable, I think that CL's syntactic and semantic egalitarianism better realizes the goal "topic neutrality" that a logic should ideally exemplify - understood, at least in part, as the idea that logic should as far as possible not itself embody any metaphysical presuppositions. Instead of retaining the traditional metaphysical divisions of FOL that reflect its Fregean origins, CL begins as it were with a single, metaphysically homogeneous domain in which, potentially, anything can play the traditional roles of object, property, relation, and function. Note that the effect of this is not to destroy traditional metaphysical divisions. Rather, it simply to refrain from building those divisions explicitly into one's logic; instead, such divisions are left to the user to introduce and enforce axiomatically in an explicit metaphysical theory.

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Abstract

An increasing number of applications rely on RDF, OWL2, and SPARQL for storing and querying data. SPARQL, however, is not targeted towards end-users, and suitable query interfaces are needed. Faceted search is a prominent approach for end-user data access, and several RDF-based faceted search systems have been developed. There is, however, a lack of rigorous theoretical underpinning for faceted search in the context of RDF and OWL2. In this paper, we provide such solid foundations. We formalise faceted interfaces for this context, identify a fragment of first-order logic capturing the underlying queries, and study the complexity of answering such queries for RDF and OWL2 profiles. We then study interface generation and update, and devise efficiently implementable algorithms. Finally, we have implemented and tested our faceted search algorithms for scalability, with encouraging results.

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Abstract

In this thesis, we present an ontology-based information extraction and retrieval system and its application to soccer domain. In general, we deal with three issues in semantic search, namely, usability, scalability and retrieval performance. We propose a keyword-based semantic retrieval approach. The performance of the system is improved considerably using domain-specific information extraction, inference and rules. Scalability is achieved by adapting a semantic indexing approach. The system is implemented using the state-of-the-art technologies in SemanticWeb and its performance is evaluated against traditional systems as well as the query expansion methods. Furthermore, a detailed evaluation is provided to observe the performance gain due to domain-specific information extraction and inference. Finally, we show how we use semantic indexing to solve simple structural ambiguities.

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Abstract

This thesis focuses on conversion of vocabularies for representation and integration of collections on the Semantic Web. A secondary focus is how to represent metadata schemas (RDF Schemas representing metadata element sets) such that they interoperate with vocabularies. The primary domain in which we operate is that of cultural heritage collections. The background worldview in which a solution is sought is that of the Semantic Web research paradigmwith its associated theories, methods, tools and use cases. In other words, we assume the SemanticWeb is in principle able to provide the context to realize interoperable collections. Interoperability is dependent on the interplay between representations and the applications that use them. We mean applications in the widest sense, such as "search" and "annotation". These applications or tasks are often present in software applications, such as the E-Culture application. It is therefore necessary that applications requirements on the vocabulary representation are met. This leads us to formulate the following problem statement: HOW CAN EXISTING VOCABULARIES BE MADE AVAILABLE TO SEMANTIC WEB APPLICATIONS?
We refine the problem statement into three research questions. The first two focus on the problem of conversion of a vocabulary to a Semantic Web representation from its original format. Conversion of a vocabulary to a representation in a Semantic Web language is necessary to make the vocabulary available to SemanticWeb applications. In the last question we focus on integration of collection metadata schemas in a way that allows for vocabulary representations as produced by our methods. Academisch proefschrift ter verkrijging van de graad Doctor aan de Vrije Universiteit Amsterdam, Dutch Research School for Information and Knowledge Systems.

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Abstract

Background: Because of the increasing number of electronic resources, designing efficient tools to retrieve and exploit them is a major challenge. Some improvements have been offered by semantic Web technologies and applications based on domain ontologies. In life science, for instance, the Gene Ontology is widely exploited in genomic applications and the Medical Subject Headings is the basis of biomedical publications indexation and information retrieval process proposed by PubMed. However current search engines suffer from two main drawbacks: there is limited user interaction with the list of retrieved resources and no explanation for their adequacy to the query is provided. Users may thus be confused by the selection and have no idea on how to adapt their queries so that the results match their expectations. Results: This paper describes an information retrieval system that relies on domain ontology to widen the set of relevant documents that is retrieved and that uses a graphical rendering of query results to favor user interactions. Semantic proximities between ontology concepts and aggregating models are used to assess documents adequacy with respect to a query. The selection of documents is displayed in a semantic map to provide graphical indications that make explicit to what extent they match the user's query; this man/machine interface favors a more interactive and iterative exploration of data corpus, by facilitating query concepts weighting and visual explanation. We illustrate the benefit of using this information retrieval system on two case studies one of which aiming at collecting human genes related to transcription factors involved in hemopoiesis pathway. Conclusions: The ontology based information retrieval system described in this paper (OBIRS) is freely available at: http://www.ontotoolkit.mines-ales.fr/ObirsClient/. This environment is a first step towards a user centred application in which the system enlightens relevant information to provide decision help.

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Abstract

Companies, governmental agencies and scientists produce a large amount of quantitative (research) data, consisting of measurements ranging from e.g. the surface temperatures of an ocean to the viscosity of a sample of mayonnaise. Such measurements are stored in tables in e.g. spreadsheet files and research reports. To integrate and reuse such data, it is necessary to have a semantic description of the data. However, the notation used is often ambiguous, making automatic interpretation and conversion to RDF or other suitable format diffiult. For example, the table header cell "f(Hz)" refers to frequency measured in Hertz, but the symbol "f" can also refer to the unit farad or the quantities force or luminous flux. Current annotation tools for this task either work on less ambiguous data or perform a more limited task. We introduce new disambiguation strategies based on an ontology, which allows to improve performance on "sloppy" datasets not yet targeted by existing systems.

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Abstract

The formal structure of the information on the Semantic Web lends itself to faceted browsing, an information retrieval method where users can filter results based on the values of properties ("facets"). Numerous faceted browsers have been created to browse RDF and Linked Data, but these systems use their own ontologies for defining how data is queried to populate their facets. Since the source data is the same format across these systems (specifically, RDF), we can unify the different methods of describing how to quer the underlying data, to enable compatibility across systems, and provide an extensible base ontology for future systems. To this end, we present FacetOntology, an ontology that defines how to query data to form a faceted browser, and a number of transformations and filters that can be applied to data before it is shown to users. FacetOntology overcomes limitations in the expressivity of existing work, by enabling the full expressivity of SPARQL when selecting data for facets. By applying a FacetOntology definition to data, a set of facets are specified, each with queries and filters to source RDF data, which enables faceted browsing systems to be created using that RDF data.

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Abstract

Today's conventional search engines hardly do provide the essential content relevant to the user's search query. This is because the context and semantics of the request made by the user is not analyzed to the full extent. So here the need for a semantic web search arises. SWS is upcoming in the area of web search which combines Natural Language Processing and Artificial Intelligence. The objective of the work done here is to design, develop and implement a semantic search engine- SIEU(Semantic Information Extraction in University Domain) confined to the university domain. SIEU uses ontology as a knowledge base for the information retrieval process. It is not just a mere keyword search. It is one layer above what Google or any other search engines retrieve by analyzing just the keywords. Here the query is analyzed both syntactically and semantically. The developed system retrieves the web results more relevant to the user query through keyword expansion. The results obtained here will be accurate enough to satisfy the request made by the user. The level of accuracy will be enhanced since the query is analyzed semantically. The system will be of great use to the developers and researchers who work on web. The Google results are re-ranked and optimized for providing the relevant links. For ranking an algorithm has been applied which fetches more apt results for the user query.

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Abstract

Documents in rich text corpora usually contain multiple facets of information. For example, an article about a specific disease often consists of different facets such as symptom, treatment, cause, diagnosis, prognosis, and prevention. Thus, documents may have different relations based on different facets. Powerful search tools have been developed to help users locate lists of individual documents that are most related to specific keywords. However, there is a lack of effective analysis tools that reveal the multifaceted relations of documents within or cross the document clusters. In this paper, we present FacetAtlas, a multifaceted visualization technique for visually analyzing rich text corpora. FacetAtlas combines search technology with advanced visual analytical tools to convey both global and local patterns simultaneously. We describe several unique aspects of FacetAtlas, including (1) node cliques and multifaceted edges, (2) an optimized density map, and (3) automated opacity pattern enhancement for highlighting visual patterns, (4) interactive context switch between facets. In addition, we demonstrate the power of FacetAtlas through a case study that targets patient education in the health care domain. Our evaluation shows the benefits of this work, especially in support of complex multifaceted data analysis.

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Abstract

In Linked Data, the use of owl:sameAs is ubiquitous in 'inter-linking' data-sets. However, there is a lurking suspicion within the Linked Data community that this use of owl:sameAs may be somehow incorrect, in particular with regards to its interactions with inference. In fact, owl:sameAs can be considered just one type of 'identity link', a link that declares two items to be identical in some fashion. After reviewing the definitions and history of the problem of identity in philosophy and knowledge representation, we outline four alternative readings of owl:sameAs, showing with examples how it is being (ab)used on the Web of data. Then we present possible solutions to this problem by introducing alternative identity links that rely on named graphs.

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Abstract

Knowledge Representation and Reasoning (KR & R) has become one of the promising fields of Artificial Intelligence. KR is dedicated towards representing information about the domain that can be utilized in path planning. Ontology based knowledge representation and reasoning techniques provide sophisticated knowledge about the environment for processing tasks or methods. Ontology helps in representing the knowledge about environment, events and actions that help in path planning and making robots more autonomous. Knowledge reasoning techniques can infer new conclusion and thus aids planning dynamically in a non-deterministic environment. In the initial sections, the representation of knowledge using ontology and the techniques for reasoning that could contribute in path planning are discussed in detail. In the following section, we also provide comparison of various planning domain modeling languages, ontology editors, planners and robot simulation tools.

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Abstract

We present Broccoli, a fast and easy-to-use search engine forwhat we call semantic full-text search. Semantic full-textsearch combines the capabilities of standard full-text searchand ontology search. The search operates on four kinds ofobjects: ordinary words (e.g., edible), classes (e.g., plants), instances (e.g.,Broccoli), and relations (e.g., occurs-with or native-to). Queries are trees, where nodes are arbitrary bags of these objects, and arcs are relations. The user interface guides the user in incrementally constructing such trees by instant (search-as-you-type) suggestions of words, classes, instances, or relations that lead to good hits. Both standard full-text search and pure ontology search are included as special cases. In this paper, we describe the query language of Broccoli, a new kind of index that enables fast processing of queries from that language as well as fast query suggestion, the natural language processing required, and the user interface. We evaluated query times and result quality on the full version of the English Wikipedia (32 GB XML dump) combined with the YAGO ontology (26 million facts). We have implemented a fully functional prototype based on our ideas, see: http://broccoli.informatik.uni-freiburg.de.

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Abstract

Division is obviously important to Knowledge Organization. Typically, an organizational infrastructure might acknowledge three types of connecting relationships: class hierarchies, where some classes are subclasses of others, partitive hierarchies, where some items are parts of others, and instantiation, where some items are members of some classes (see Z39.19 ANSI/NISO 2005 as an example). The first two of these involve division (the third, instantiation, does not involve division). Logical division would usually be a part of hierarchical classification systems, which, in turn, are central to shelving in libraries, to subject classification schemes, to controlled vocabularies, and to thesauri. Partitive hierarchies, and partitive division, are often essential to controlled vocabularies, thesauri, and subject tagging systems. Partitive hierarchies also relate to the bearers of information; for example, a journal would typically have its component articles as parts and, in turn, they might have sections as their parts, and, of course, components might be arrived at by partitive division (see Tillett 2009 as an illustration). Finally, verbal division, disambiguating homographs, is basic to controlled vocabularies. Thus Division is a broad and relevant topic. This article, though, is going to focus on Logical Division.

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Abstract

Current retrieval and recommendation approaches rely on hard-wired data models. This hinders personalized cus-tomizations to meet information needs of users in a more flexible manner. Therefore, the paper investigates how similarity-basedretrieval strategies can be combined with graph queries to enable users or system providers to explore repositories in the LinkedOpen Data (LOD) cloud more thoroughly. For this purpose, we developed novel content-based recommendation approaches.They rely on concept annotations of Simple Knowledge Organization System (SKOS) vocabularies and a SPARQL-based querylanguage that facilitates advanced and personalized requests for openly available knowledge graphs. We have comprehensivelyevaluated the novel search strategies in several test cases and example application domains (i.e., travel search and multimediaretrieval). The results of the web-based online experiments showed that our approaches increase the recall and diversity of rec-ommendations or at least provide a competitive alternative strategy of resource access when conventional methods do not providehelpful suggestions. The findings may be of use for Linked Data-enabled recommender systems (LDRS) as well as for semanticsearch engines that can consume LOD resources. (PDF) Similarity-based knowledge graph queries for recommendation retrieval. Available from: https://www.researchgate.net/publication/333358714_Similarity-based_knowledge_graph_queries_for_recommendation_retrieval [accessed May 21 2020].

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Abstract

Internet of Things (IoT) brings the third development wave of the global information industry which makes users, network and perception devices cooperate more closely. However, if IoT has security problems, it may cause a variety of damage and even threaten human lives and properties. To improve the abilities of monitoring, providing emergency response and predicting the development trend of IoT security, a new paradigm called network security situation awareness (NSSA) is proposed. However, it is limited by its ability to mine and evaluate security situation elements from multi-source heterogeneous network security information. To solve this problem, this paper proposes an IoT network security situation awareness model using situation reasoning method based on semantic ontology and user-defined rules. Ontology technology can provide a unified and formalized description to solve the problem of semantic heterogeneity in the IoT security domain. In this paper, four key sub-domains are proposed to reflect an IoT security situation: context, attack, vulnerability and network flow. Further, user-defined rules can compensate for the limited description ability of ontology, and hence can enhance the reasoning ability of our proposed ontology model. The examples in real IoT scenarios show that the ability of the network security situation awareness that adopts our situation reasoning method is more comprehensive and more powerful reasoning abilities than the traditional NSSA methods. [http://ieeexplore.ieee.org/abstract/document/7999187/]

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Abstract

Ontologies are often viewed as the answer to the need for inter-operable semantics in modern information systems. The explosion of textual information on the "Read/Write" Web coupled with the increasing demand for ontologies to power the Semantic Web have made (semi-)automatic ontology learning from text a very promising research area. This together with the advanced state in related areas such as natural language processing have fuelled research into ontology learning over the past decade. This survey looks at how far we have come since the turn of the millennium, and discusses the remaining challenges that will define the research directions in this area in the near future.

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Abstract

Mathematics is a ubiquitous foundation of science, technology, and engineering. Specific areas, such as numeric and symbolic computation or logics, enjoy considerable software support. Working mathematicians have recently started to adopt Web 2.0 environment, such as blogs and wikis, but these systems lack machine support for knowledge organization and reuse, and they are disconnected from tools such as computer algebra systems or interactive proof assistants.We argue that such scenarios will benefit from Semantic Web technology. Conversely, mathematics is still underrepresented on the Web of [Linked] Data. There are mathematics-related Linked Data, for example statistical government data or scientific publication databases, but their mathematical semantics has not yet been modeled. We argue that the services for the Web of Data will benefit from a deeper representation of mathematical knowledge. Mathematical knowledge comprises logical and functional structures - formulæ, statements, and theories -, a mixture of rigorous natural language and symbolic notation in documents, application-specific metadata, and discussions about conceptualizations, formalizations, proofs, and (counter-)examples. Our review of approaches to representing these structures covers ontologies for mathematical problems, proofs, interlinked scientific publications, scientific discourse, as well as mathematical metadata vocabularies and domain knowledge from pure and applied mathematics. Many fields of mathematics have not yet been implemented as proper Semantic Web ontologies; however, we show that MathML and OpenMath, the standard XML-based exchange languages for mathematical knowledge, can be fully integrated with RDF representations in order to contribute existing mathematical knowledge to theWeb of Data. We conclude with a roadmap for getting the mathematical Web of Data started: what datasets to publish, how to interlink them, and how to take advantage of these new connections.

Type

a