Search (20 results, page 1 of 1)

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Abstract

The term "facet" was introduced into the field of library classification systems by Ranganathan in the 1930's [Ranganathan, 1962]. A facet is a viewpoint or aspect. In contrast to traditional classification systems, faceted systems are modular in that a domain is analyzed in terms of baseline facets which are then synthesized. In this paper, the term "facet" is used in a broader meaning. Facets can describe different aspects on the same level of abstraction or the same aspect on different levels of abstraction. The notion of facets is related to database views, multicontexts and conceptual scaling in formal concept analysis [Ganter and Wille, 1999], polymorphism in object-oriented design, aspect-oriented programming, views and contexts in description logic and semantic networks. This paper presents a definition of facets in terms of faceted knowledge representation that incorporates the traditional narrower notion of facets and potentially facilitates translation between different knowledge representation formalisms. A goal of this approach is a modular, machine-aided knowledge base design mechanism. A possible application is faceted thesaurus construction for information retrieval and data mining. Reasoning complexity depends on the size of the modules (facets). A more general analysis of complexity will be left for future research.

Date

22. 1.2016 17:30:31

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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/]

Content

DOI 10.1109/ACCESS.2017.2734681.

Source

IEEE Access. 10.1109/ACCESS.2017.2734681, 5, (21046-21056) [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7999187]

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Abstract

The success of the use of ontology-based systems depends on efficient and user-friendly methods of formulating queries against the ontology. We propose a method to query a class of ontologies, called facet ontologies ( fac-ontologies ), using a faceted human-oriented approach. A fac-ontology has two important features: (a) a hierarchical view of it can be defined as a nested facet over this ontology and the view can be used as a faceted interface to create queries and to explore the ontology; (b) the ontology can be converted into an ontological database , the ABox of which is stored in a database, and the faceted queries are evaluated against this database. We show that the proposed faceted interface makes it possible to formulate queries that are semantically equivalent to $${\mathcal {SROIQ}}^{Fac}$$ SROIQ Fac , a limited version of the $${\mathcal {SROIQ}}$$ SROIQ description logic. The TBox of a fac-ontology is divided into a set of rules defining intensional predicates and a set of constraint rules to be satisfied by the database. We identify a class of so-called reflexive weak cycles in a set of constraint rules and propose a method to deal with them in the chase procedure. The considerations are illustrated with solutions implemented in the DAFO system ( data access based on faceted queries over ontologies ).

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Abstract

Terminology has evolved from static and prescriptive theories to dynamic and cognitive approaches. Thanks to these approaches, there have been significant advances in the design and elaboration of terminological resources. This has resulted in the creation of tools such as terminological knowledge bases, which are able to show how concepts are interrelated through different semantic or conceptual relations. Of these relations, hyponymy is the most relevant to terminology work because it deals with concept categorization and term hierarchies. This doctoral thesis presents an enhancement of the semantic structure of EcoLexicon, a terminological knowledge base on environmental science. The aim of this research was to improve the description, categorization, and representation of hyponymy in environmental terminology. Therefore, we created HypoLexicon, a new stand-alone module for EcoLexicon in the form of a hyponymy-based terminological resource. This resource contains twelve terminological entries from four specialized domains (Biology, Chemistry, Civil Engineering, and Geology), which consist of 309 concepts and 465 terms associated with those concepts. This research was mainly based on the theoretical premises of Frame-based Terminology. This theory was combined with Cognitive Linguistics, for conceptual description and representation; Corpus Linguistics, for the extraction and processing of linguistic and terminological information; and Ontology, related to hyponymy and relevant for concept categorization. HypoLexicon was constructed from the following materials: (i) the EcoLexicon English Corpus; (ii) other specialized terminological resources, including EcoLexicon; (iii) Sketch Engine; and (iv) Lexonomy. This thesis explains the methodologies applied for corpus extraction and compilation, corpus analysis, the creation of conceptual hierarchies, and the design of the terminological template. The results of the creation of HypoLexicon are discussed by highlighting the information in the hyponymy-based terminological entries: (i) parent concept (hypernym); (ii) child concepts (hyponyms, with various hyponymy levels); (iii) terminological definitions; (iv) conceptual categories; (v) hyponymy subtypes; and (vi) hyponymic contexts. Furthermore, the features and the navigation within HypoLexicon are described from the user interface and the admin interface. In conclusion, this doctoral thesis lays the groundwork for developing a terminological resource that includes definitional, relational, ontological and contextual information about specialized hypernyms and hyponyms. All of this information on specialized knowledge is simple to follow thanks to the hierarchical structure of the terminological template used in HypoLexicon. Therefore, not only does it enhance knowledge representation, but it also facilitates its acquisition.

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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

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

This paper presents a framework for combining semantic relations extracted from text to reveal even more semantics that otherwise would be missed. A set of 26 relations is introduced, with their arguments defined on an ontology of sorts. A semantic parser is used to extract these relations from noun phrases and verb argument structures. The method was successfully used in two applications: rapid customization of semantic relations to arbitrary domains and recognizing entailments.

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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

Innovative research institutes rely on the availability of complete and accurate information about new research and development, and it is the business of information providers such as Elsevier to provide the required information in a cost-effective way. It is very likely that the semantic web will make an important contribution to this effort, since it facilitates access to an unprecedented quantity of data. However, with the unremitting growth of scientific information, integrating access to all this information remains a significant problem, not least because of the heterogeneity of the information sources involved - sources which may use different syntactic standards (syntactic heterogeneity), organize information in very different ways (structural heterogeneity) and even use different terminologies to refer to the same information (semantic heterogeneity). The ability to address these different kinds of heterogeneity is the key to integrated access. Thesauri have already proven to be a core technology to effective information access as they provide controlled vocabularies for indexing information, and thereby help to overcome some of the problems of free-text search by relating and grouping relevant terms in a specific domain. However, currently there is no open architecture which supports the use of these thesauri for querying other data sources. For example, when we move from the centralized and controlled use of EMTREE within EMBASE.com to a distributed setting, it becomes crucial to improve access to the thesaurus by means of a standardized representation using open data standards that allow for semantic qualifications. In general, mental models and keywords for accessing data diverge between subject areas and communities, and so many different ontologies have been developed. An ideal architecture must therefore support the disclosure of distributed and heterogeneous data sources through different ontologies. The aim of the DOPE project (Drug Ontology Project for Elsevier) is to investigate the possibility of providing access to multiple information sources in the area of life science through a single interface.

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Date

22. 5.2021 12:43:05

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Date

22. 1.2016 17:30:31

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Abstract

While thousands of ontologies exist on the web, a unified sys-tem for handling online ontologies - in particular with respect to discov-ery, versioning, access, quality-control, mappings - has not yet surfacedand users of ontologies struggle with many challenges. In this paper, wepresent an online ontology interface and augmented archive called DB-pedia Archivo, that discovers, crawls, versions and archives ontologies onthe DBpedia Databus. Based on this versioned crawl, different features,quality measures and, if possible, fixes are deployed to handle and sta-bilize the changes in the found ontologies at web-scale. A comparison toexisting approaches and ontology repositories is given.

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Source

Evaluating information retrieval and access tasks. Eds.: Sakai, T., Oard, D., Kando, N. [https://doi.org/10.1007/978-981-15-5554-1_12]

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Date

3.12.2016 18:39:22

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Date

28.11.2016 12:43:22

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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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Date

16.11.2018 14:22:01

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Abstract

Ontologies have proven to be an essential element in many applications. They are used in agent systems, knowledge management systems, and e-commerce platforms. They can also generate natural language, integrate intelligent information, provide semantic-based access to the Internet, and extract information from texts in addition to being used in many other applications to explicitly declare the knowledge embedded in them. However, not only are ontologies useful for applications in which knowledge plays a key role, but they can also trigger a major change in current Web contents. This change is leading to the third generation of the Web-known as the Semantic Web-which has been defined as "the conceptual structuring of the Web in an explicit machine-readable way."1 This definition does not differ too much from the one used for defining an ontology: "An ontology is an explicit, machinereadable specification of a shared conceptualization."2 In fact, new ontology-based applications and knowledge architectures are developing for this new Web. A common claim for all of these approaches is the need for languages to represent the semantic information that this Web requires-solving the heterogeneous data exchange in this heterogeneous environment. Here, we don't decide which language is best of the Semantic Web. Rather, our goal is to help developers find the most suitable language for their representation needs. The authors analyze the most representative ontology languages created for the Web and compare them using a common framework.

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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

Today's semantic web deals with meaning in a very restricted sense and offers static solutions. This is adequate for many scientific, technical purposes and for business transactions requiring machine-to-machine communication, but does not answer the needs of culture. Science, technology and business are concerned primarily with the latest findings, the state of the art, i.e. the paradigm or dominant world-view of the day. In this context, history is considered non-essential because it deals with things that are out of date. By contrast, culture faces a much larger challenge, namely, to re-present changes in ways of knowing; changing meanings in different places at a given time (synchronically) and over time (diachronically). Culture is about both objects and the commentaries on them; about a cumulative body of knowledge; about collective memory and heritage. Here, history plays a central role and older does not mean less important or less relevant. Hence, a Leonardo painting that is 400 years old, or a Greek statue that is 2500 years old, typically have richer commentaries and are often more valuable than their contemporary equivalents. In this context, the science of meaning (semantics) is necessarily much more complex than semantic primitives. A semantic web in the cultural domain must enable us to trace how meaning and knowledge organisation have evolved historically in different cultures. This paper examines five issues to address this challenge: 1) different world-views (i.e. a shift from substance to function and from ontology to multiple ontologies); 2) developments in definitions and meaning; 3) distinctions between words and concepts; 4) new classes of relations; and 5) dynamic models of knowledge organisation. These issues reveal that historical dimensions of cultural diversity in knowledge organisation are also central to classification of biological diversity. New ways are proposed of visualizing knowledge using a time/space horizon to distinguish between universals and particulars. It is suggested that new visualization methods make possible a history of questions as well as of answers, thus enabling dynamic access to cultural and historical dimensions of knowledge. Unlike earlier media, which were limited to recording factual dimensions of collective memory, digital media enable us to explore theories, ways of perceiving, ways of knowing; to enter into other mindsets and world-views and thus to attain novel insights and new levels of tolerance. Some practical consequences are outlined.