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2011 | Buch

The Semantic Web: Research and Applications

8th Extended Semantic Web Conference, ESWC 2011, Heraklion, Crete, Greece, May 29-June 2, 2011, Proceedings, Part I

herausgegeben von: Grigoris Antoniou, Marko Grobelnik, Elena Simperl, Bijan Parsia, Dimitris Plexousakis, Pieter De Leenheer, Jeff Pan

Verlag: Springer Berlin Heidelberg

Buchreihe : Lecture Notes in Computer Science

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

The books (LNCS 6643 and 6644) constitute the refereed proceedings of the 8th European Semantic Web Conference, ESWC 2011, held in Heraklion, Crete, Greece, in May/June 2011. The 57 revised full papers of the research track presented together with 7 PhD symposium papers and 14 demo papers were carefully reviewed and selected from 291 submissions. The papers are organized in topical sections on digital libraries track; inductive and probabilistic approaches track; linked open data track; mobile Web track; natural language processing track; ontologies track; and reasoning track (part I); semantic data management track; semantic Web in use track; sensor Web track; software, services, processes and cloud computing track; social Web and Web science track; demo track, PhD symposium (part II).

Inhaltsverzeichnis

Frontmatter

Digital Libraries Track

Interactive Exploration of Fuzzy RDF Knowledge Bases

Abstract

In several domains we have objects whose descriptions are accompanied by a degree expressing their strength. Such degrees can have various application specific semantics, such as relevance, precision, certainty, trust, etc. In this paper we consider Fuzzy RDF as the representation framework for such “weighted” descriptions and associations, and we propose a novel model for browsing and exploring such sources, which allows formulating complex queries gradually and through plain clicks. Specifically, and in order to exploit the fuzzy degrees, the model proposes interval-based transition markers. The advantage of the model is that it significantly increases the discrimination power of the interaction, without making it complex for the end user.

Nikos Manolis, Yannis Tzitzikas

A Structured Semantic Query Interface for Reasoning-Based Search and Retrieval

Abstract

Information and knowledge retrieval are today some of the main assets of the Semantic Web. However, a notable immaturity still exists, as to what tools, methods and standards may be used to effectively achieve these goals. No matter what approach is actually followed, querying Semantic Web information often requires deep knowledge of the ontological syntax, the querying protocol and the knowledge base structure as well as a careful elaboration of the query itself, in order to extract the desired results. In this paper, we propose a structured semantic query interface that helps to construct and submit entailment-based queries in an intuitive way. It is designed so as to capture the meaning of the intended user query, regardless of the formalism actually being used, and to transparently formulate one in reasoner-compatible format. This interface has been deployed on top of the semantic search prototype of the DSpace digital repository system.

Dimitrios A. Koutsomitropoulos, Ricardo Borillo Domenech, Georgia D. Solomou

Distributed Human Computation Framework for Linked Data Co-reference Resolution

Abstract

Distributed Human Computation (DHC) is used to solve computational problems by incorporating the collaborative effort of a large number of humans. It is also a solution to AI-complete problems such as natural language processing. The Semantic Web with its root in AI has many research problems that are considered as AI-complete. E.g. co-reference resolution, which involves determining whether different URIs refer to the same entity, is a significant hurdle to overcome in the realisation of large-scale Semantic Web applications. In this paper, we propose a framework for building a DHC system on top of the Linked Data Cloud to solve various computational problems. To demonstrate the concept, we are focusing on handling the co-reference resolution when integrating distributed datasets. Traditionally machine-learning algorithms are used as a solution for this but they are often computationally expensive, error-prone and do not scale. We designed a DHC system named iamResearcher, which solves the scientific publication author identity co-reference problem when integrating distributed bibliographic datasets. In our system, we aggregated 6 million bibliographic data from various publication repositories. Users can sign up to the system to audit and align their own publications, thus solving the co-reference problem in a distributed manner. The aggregated results are dereferenceable in the Open Linked Data Cloud.

Yang Yang, Priyanka Singh, Jiadi Yao, Ching-man Au Yeung, Amir Zareian, Xiaowei Wang, Zhonglun Cai, Manuel Salvadores, Nicholas Gibbins, Wendy Hall, Nigel Shadbolt

Inductive and Probabilistic Approaches Track

Relational Kernel Machines for Learning from Graph-Structured RDF Data

Abstract

Despite the increased awareness that exploiting the large amount of semantic data requires statistics-based inference capabilities, only little work can be found on this direction in the Semantic Web research. On semantic data, supervised approaches, particularly kernel-based Support Vector Machines (SVM), are promising. However, obtaining the right features to be used in kernels is an open problem because the amount of features that can be extracted from the complex structure of semantic data might be very large. Further, combining several kernels can help to deal with efficiency and data sparsity but creates the additional challenge of identifying and joining different subsets of features or kernels, respectively. In this work, we solve these two problems by employing the strategy of dynamic feature construction to compute a hypothesis, representing the relevant features for a set of examples. Then, a composite kernel is obtained from a set of clause kernels derived from components of the hypothesis. The learning of the hypothesis and kernel(s) is performed in an interleaving fashion. Based on experiments on real-world datasets, we show that the resulting relational kernel machine improves the SVM baseline.

Veli Bicer, Thanh Tran, Anna Gossen

AutoSPARQL: Let Users Query Your Knowledge Base

Abstract

An advantage of Semantic Web standards like RDF and OWL is their flexibility in modifying the structure of a knowledge base. To turn this flexibility into a practical advantage, it is of high importance to have tools and methods, which offer similar flexibility in exploring information in a knowledge base. This is closely related to the ability to easily formulate queries over those knowledge bases. We explain benefits and drawbacks of existing techniques in achieving this goal and then present the QTL algorithm, which fills a gap in research and practice. It uses supervised machine learning and allows users to ask queries without knowing the schema of the underlying knowledge base beforehand and without expertise in the SPARQL query language. We then present the AutoSPARQL user interface, which implements an active learning approach on top of QTL. Finally, we evaluate the approach based on a benchmark data set for question answering over Linked Data.

Jens Lehmann, Lorenz Bühmann

Contextual Ontology Alignment of LOD with an Upper Ontology: A Case Study with Proton

Abstract

The Linked Open Data (LOD) is a major milestone towards realizing the Semantic Web vision, and can enable applications such as robust Question Answering (QA) systems that can answer queries requiring multiple, disparate information sources. However, realizing these applications requires relationships at both the schema and instance level, but currently the LOD only provides relationships for the latter. To address this limitation, we present a solution for automatically finding schema-level links between two LOD ontologies – in the sense of ontology alignment. Our solution, called BLOOMS+, extends our previous solution (i.e. BLOOMS) in two significant ways. BLOOMS+ 1) uses a more sophisticated metric to determine which classes between two ontologies to align, and 2) considers contextual information to further support (or reject) an alignment. We present a comprehensive evaluation of our solution using schema-level mappings from LOD ontologies to Proton (an upper level ontology) – created manually by human experts for a real world application called FactForge. We show that our solution performed well on this task. We also show that our solution significantly outperformed existing ontology alignment solutions (including our previously published work on BLOOMS) on this same task.

Prateek Jain, Peter Z. Yeh, Kunal Verma, Reymonrod G. Vasquez, Mariana Damova, Pascal Hitzler, Amit P. Sheth

Linked Open Data Track

Hide the Stack: Toward Usable Linked Data

Abstract

The explosion in growth of the Web of Linked Data has provided, for the first time, a plethora of information in disparate locations, yet bound together by machine-readable, semantically typed relations. Utilisation of the Web of Data has been, until now, restricted to the members of the community, eating their own dogfood, so to speak. To the regular web user browsing Facebook and watching YouTube, this utility is yet to be realised. The primary factor inhibiting uptake is the usability of the Web of Data, where users are required to have prior knowledge of elements from the Semantic Web technology stack. Our solution to this problem is to hide the stack, allowing end users to browse the Web of Data, explore the information it contains, discover knowledge, and use Linked Data. We propose a template-based visualisation approach where information attributed to a given resource is rendered according to the rdf:type of the instance.

Aba-Sah Dadzie, Matthew Rowe, Daniela Petrelli

Linked Data Metrics for Flexible Expert Search on the Open Web

Abstract

As more and more user traces become available as Linked Data Web, using those traces for expert finding becomes an interesting challenge, especially for the open innovation platforms. The existing expert search approaches are mostly limited to one corpus and one particular type of trace – sometimes even to a particular domain. We argue that different expert communities use different communication channels as their primary mean for communicating and disseminating knowledge, and thus different types of traces would be relevant for finding experts on different topics. We propose an approach for adapting the expert search process (choosing the right type of trace and the right expertise hypothesis) to the given topic of expertise, by relying on Linked Data metrics. In a gold standard-based experiment, we have shown that there is a significant positive correlation between the values of our metrics and the precision and recall of expert search. We also present hy.SemEx, a system that uses our Linked Data metrics to recommend the expert search approach to serve for finding experts in an open innovation scenario at hypios. The evaluation of the users’ satisfaction with the system’s recommendations is presented as well.

Milan Stankovic, Jelena Jovanovic, Philippe Laublet

Statistical Schema Induction

Abstract

While the realization of the Semantic Web as once envisioned by Tim Berners-Lee remains in a distant future, the Web of Data has already become a reality. Billions of RDF statements on the Internet, facts about a variety of different domains, are ready to be used by semantic applications. Some of these applications, however, crucially hinge on the availability of expressive schemas suitable for logical inference that yields non-trivial conclusions. In this paper, we present a statistical approach to the induction of expressive schemas from large RDF repositories. We describe in detail the implementation of this approach and report on an evaluation that we conducted using several data sets including DBpedia.

Johanna Völker, Mathias Niepert

SIHJoin: Querying Remote and Local Linked Data

Abstract

The amount of Linked Data is increasing steadily. Optimized top-down Linked Data query processing based on complete knowledge about all sources, bottom-up processing based on run-time discovery of sources as well as a mixed strategy that combines them have been proposed. A particular problem with Linked Data processing is that the heterogeneity of the sources and access options lead to varying input latency, rendering the application of blocking join operators infeasible. Previous work partially address this by proposing a non-blocking iterator-based operator and another one based on symmetric-hash join. Here, we propose detailed cost models for these two operators to systematically compare them, and to allow for query optimization. Further, we propose a novel operator called the Symmetric Index Hash Join to address one open problem of Linked Data query processing: to query not only remote, but also local Linked Data. We perform experiments on real-world datasets to compare our approach against the iterator-based baseline, and create a synthetic dataset to more systematically analyze the impacts of the individual components captured by the proposed cost models.

Günter Ladwig, Thanh Tran

Zero-Knowledge Query Planning for an Iterator Implementation of Link Traversal Based Query Execution

Abstract

Link traversal based query execution is a new query execution paradigm for the Web of Data. This approach allows the execution engine to discover potentially relevant data during the query execution and, thus, enables users to tap the full potential of the Web. In earlier work we propose to implement the idea of link traversal based query execution using a synchronous pipeline of iterators. While this idea allows for an easy and efficient implementation, it introduces restrictions that cause less comprehensive result sets. In this paper we address this limitation. We analyze the restrictions and discuss how the evaluation order of a query may affect result set size and query execution costs. To identify a suitable order, we propose a heuristic for our scenario where no a-priory information about relevant data sources is present. We evaluate this heuristic by executing real-world queries over the Web of Data.

Olaf Hartig

Integrating Linked Data and Services with Linked Data Services

Abstract

A sizable amount of data on the Web is currently available via Web APIs that expose data in formats such as JSON or XML. Combining data from different APIs and data sources requires glue code which is typically not shared and hence not reused. We propose Linked Data Services (LIDS), a general, formalised approach for integrating data-providing services with Linked Data, a popular mechanism for data publishing which facilitates data integration and allows for decentralised publishing. We present conventions for service access interfaces that conform to Linked Data principles, and an abstract lightweight service description formalism. We develop algorithms that use LIDS descriptions to automatically create links between services and existing data sets. To evaluate our approach, we realise LIDS wrappers and LIDS descriptions for existing services and measure performance and effectiveness of an automatic interlinking algorithm over multiple billions of triples.

Sebastian Speiser, Andreas Harth

Mobile Web Track

OntoWiki Mobile – Knowledge Management in Your Pocket

Abstract

As comparatively powerful mobile computing devices are becoming more common, mobile web applications have started gaining in popularity. In this paper we present an approach for a mobile semantic collaboration platform based on the OntoWiki framework. It allows users to collect instance data, refine the structure of knowledge bases and browse data using hierarchical or faceted navigation on-the-go even without a present data connection. A crucial part of OntoWiki Mobile is the advanced replication and conflict resolution for RDF content. The approach for conflict resolution is based on a combination of distributed revision control strategies and the EvoPat method for data evolution and ontology refactoring. OntoWiki mobile is available as an HTML5 Web application and can be used in scenarios where semantically rich information has to be collected in field-conditions such as during bio-diversity expeditions to remote areas.

Timofey Ermilov, Norman Heino, Sebastian Tramp, Sören Auer

Weaving a Distributed, Semantic Social Network for Mobile Users

Abstract

Smartphones, which contain a large number of sensors and integrated devices, are becoming increasingly powerful and fully featured computing platforms in our pockets. For many people they already replace the computer as their window to the Internet, to the Web as well as to social networks. Hence, the management and presentation of information about contacts, social relationships and associated information is one of the main requirements and features of today’s smartphones. The problem is currently solved only for centralized proprietary platforms (such as Google mail, contacts & calendar) as well as data-silo-like social networks (e.g. Facebook). Within the Semantic Web initiative standards and best-practices for social, Semantic Web applications such as FOAF emerged. However, there is no comprehensive strategy, how these technologies can be used efficiently in a mobile environment. In this paper we present the architecture as well as the implementation of a mobile Social Semantic Web framework, which weaves a distributed social network based on semantic technologies.

Sebastian Tramp, Philipp Frischmuth, Natanael Arndt, Timofey Ermilov, Sören Auer

Natural Language Processing Track

Automatic Semantic Subject Indexing of Web Documents in Highly Inflected Languages

Abstract

Structured semantic metadata about unstructured web documents can be created using automatic subject indexing methods, avoiding laborious manual indexing. A succesful automatic subject indexing tool for the web should work with texts in multiple languages and be independent of the domain of discourse of the documents and controlled vocabularies. However, analyzing text written in a highly inflected language requires word form normalization that goes beyond rule-based stemming algorithms. We have tested the state-of-the art automatic indexing tool Maui on Finnish texts using three stemming and lemmatization algorithms and tested it with documents and vocabularies of different domains. Both of the lemmatization algorithms we tested performed significantly better than a rule-based stemmer, and the subject indexing quality was found to be comparable to that of human indexers.

Reetta Sinkkilä, Osma Suominen, Eero Hyvönen

FootbOWL: Using a Generic Ontology of Football Competition for Planning Match Summaries

Abstract

We present a two-layer OWL ontology-based Knowledge Base (KB) that allows for flexible content selection and discourse structuring in Natural Language text Generation (NLG) and discuss its use for these two tasks. The first layer of the ontology contains an application-independent base ontology. It models the domain and was not designed with NLG in mind. The second layer, which is added on top of the base ontology, models entities and events that can be inferred from the base ontology, including inferable logico-semantic relations between individuals. The nodes in the KB are weighted according to learnt models of content selection, such that a subset of them can be extracted. The extraction is done using templates that also consider semantic relations between the nodes and a simple user profile. The discourse structuring submodule maps the semantic relations to discourse relations and forms discourse units to then arrange them into a coherent discourse graph. The approach is illustrated and evaluated on a KB that models the First Spanish Football League.

Nadjet Bouayad-Agha, Gerard Casamayor, Leo Wanner, Fernando Díez, Sergio López Hernández

Linking Lexical Resources and Ontologies on the Semantic Web with Lemon

Abstract

There are a large number of ontologies currently available on the Semantic Web. However, in order to exploit them within natural language processing applications, more linguistic information than can be represented in current Semantic Web standards is required. Further, there are a large number of lexical resources available representing a wealth of linguistic information, but this data exists in various formats and is difficult to link to ontologies and other resources. We present a model we call lemon (Lexicon Model for Ontologies) that supports the sharing of terminological and lexicon resources on the Semantic Web as well as their linking to the existing semantic representations provided by ontologies. We demonstrate that lemon can succinctly represent existing lexical resources and in combination with standard NLP tools we can easily generate new lexica for domain ontologies according to the lemon model. We demonstrate that by combining generated and existing lexica we can collaboratively develop rich lexical descriptions of ontology entities. We also show that the adoption of Semantic Web standards can provide added value for lexicon models by supporting a rich axiomatization of linguistic categories that can be used to constrain the usage of the model and to perform consistency checks.

John McCrae, Dennis Spohr, Philipp Cimiano

Ontologies Track

Elimination of Redundancy in Ontologies

Abstract

Ontologies may contain redundancy in terms of axioms that logically follow from other axioms and that could be removed for the sake of consolidation and conciseness without changing the overall meaning. In this paper, we investigate methods for removing such redundancy from ontologies. We define notions around redundancy and discuss typical cases of redundancy and their relation to ontology engineering and evolution. We provide methods to compute irredundant ontologies both indirectly by calculating justifications, and directly by utilising a hitting set tree algorithm and module extraction techniques for optimization. Moreover, we report on experimental results on removing redundancy from existing ontologies available on the Web.

Stephan Grimm, Jens Wissmann

Evaluating the Stability and Credibility of Ontology Matching Methods

Abstract

Ontology matching is one of the key research topics in Semantic Web. In the last few years, many matching methods have been proposed to generate matches between different ontologies either automatically or semi-automatically. To select appropriate ones, users need some measures to judge whether a method can achieve the similar compliance even on one dataset without reference matches and whether such a method is reliable w.r.t. its output result along with the confidence. However, widely-used traditional measures like precision and recall fail to provide sufficient hints. In this paper, we design two novel evaluation measures to evaluate stability of matching methods and one measure to evaluate credibility of matching confidence values, which help answer the above two questions. Additionally, we carry out comparisons among several carefully selected methods systematically using our new measures. Besides, we report some interesting findings such as identifying potential defects of our subjects.

Xing Niu, Haofen Wang, Gang Wu, Guilin Qi, Yong Yu

How Matchable Are Four Thousand Ontologies on the Semantic Web

Abstract

A growing number of ontologies have been published on the Semantic Web by various parties, to be shared for describing things. Because of the decentralized nature of the Web, there often exist different but similar ontologies from overlapped domains, or even within the same domain. In this paper, we collect more than four thousand ontologies and perform a large-scale pairwise matching based on an ontology matching tool. We create about three million mappings between the terms (classes and properties) in these ontologies, and construct a complex term mapping graph with terms as nodes and mappings as edges. We analyze the macroscopic properties of the term mapping graph as well as the derived ontology mapping graph, which characterize the global ontology matchability in several aspects, including the degree distribution, connectivity and reachability. We further establish a pay-level-domain mapping graph to understand the common interests between different ontology publishers. Additionally, we publish the generated mappings online based on the R2R mapping framework. These mappings and our observations are believed to be useful for the Linked Data community in ontology creation, integration and maintenance.

Wei Hu, Jianfeng Chen, Hang Zhang, Yuzhong Qu

Understanding an Ontology through Divergent Exploration

Abstract

It is important that the ontology captures the essential conceptual structure of the target world as generally as possible. However, such ontologies are sometimes regarded as weak and shallow by domain experts because they often want to understand the target world from the domain-specific viewpoints in which they are interested. Therefore, it is highly desirable to have not only knowledge structuring from the general perspective but also from the domain-specific and multi-perspective so that concepts are structured for appropriate understanding from the multiple experts. On the basis of this observation, the authors propose a novel approach, called divergent exploration of an ontology, to bridge the gap between ontologies and domain experts. Based on the approach, we developed an ontology exploration tool and evaluated the system through an experimental use by experts in an environmental domain. As a result, we confirmed that the tool supports experts to obtain meaningful knowledge for them through the divergent exploration and it contributes to integrated understanding of the ontology and its target domain.

Kouji Kozaki, Takeru Hirota, Riichiro Mizoguchi

The Use of Foundational Ontologies in Ontology Development: An Empirical Assessment

Abstract

There is an assumption that ontology developers will use a top-down approach by using a foundational ontology, because it purportedly speeds up ontology development and improves quality and interoperability of the domain ontology. Informal assessment of these assumptions reveals ambiguous results that are not only open to different interpretations but also such that foundational ontology usage is not foreseen in most methodologies. Therefore, we investigated these assumptions in a controlled experiment. After a lecture about DOLCE, BFO, and part-whole relations, one-third chose to start domain ontology development with an OWLized foundational ontology. On average, those who commenced with a foundational ontology added more new classes and class axioms, and significantly less object properties than those who started from scratch. No ontology contained errors regarding part-of vs. is-a. The comprehensive results show that the ‘cost’ incurred spending time getting acquainted with a foundational ontology compared to starting from scratch was more than made up for in size, understandability, and interoperability already within the limited time frame of the experiment.

C. Maria Keet

Using Pseudo Feedback to Improve Cross-Lingual Ontology Mapping

Abstract

Translation techniques are often employed by cross-lingual ontology mapping (CLOM) approaches to turn a cross-lingual mapping problem into a monolingual mapping problem which can then be solved by state of the art monolingual ontology matching tools. However in the process of doing so, noisy translations can compromise the quality of the matches generated by the subsequent monolingual matching techniques. In this paper, a novel approach to improve the quality of cross-lingual ontology mapping is presented and evaluated. The proposed approach adopts the pseudo feedback technique that is similar to the well understood relevance feedback mechanism used in the field of information retrieval. It is shown through the evaluation that pseudo feedback can improve the matching quality in a CLOM scenario.

Bo Fu, Rob Brennan, Declan O’Sullivan

Automatic Identification of Ontology Versions Using Machine Learning Techniques

Abstract

When different versions of an ontology are published online, the links between them are often lost as the standard mechanisms (such as owl:versionInfo and owl:priorVersion) to expose these links are rarely used. This generates issues in scenarios where people or applications are required to make use of large scale, heterogenous ontology collections, implicitly containing multiple versions of ontologies. In this paper, we propose a method to detect automatically versioning links between ontologies which are available online through a Semantic Web search engine. Our approach is based on two main steps. The first step selects candidate pairs of ontologies by using versioning information expressed in their identifiers. In the second step, these candidate pairs are characterized through a set of features, including similarity measures, and classified by using Machine Learning Techniques, to distinguish the pairs that represent versions from the ones that do not. We discuss the features used, the methodology employed to train the classifiers and the precision obtained when applying this approach on the collection of ontologies of the Watson Semantic Web search engine.

Carlo Allocca

Reasoning Track

A Tableaux-Based Algorithm for $\mathcal{SHIQ}$ with Transitive Closure of Roles in Concept and Role Inclusion Axioms

Abstract

In this paper, we investigate an extension of the description logic $\mathcal{SHIQ}$–a knowledge representation formalism used for the Semantic Web–with transitive closure of roles occurring not only in concept inclusion axioms but also in role inclusion axioms. It was proved that adding transitive closure of roles to $\mathcal{SHIQ}$ without restriction on role hierarchies may lead to undecidability. We have identified a kind of role inclusion axioms that is responsible for this undecidability and we propose a restriction on these axioms to obtain decidability. Next, we present a tableaux-based algorithm that decides satisfiability of concepts in the new logic.

Chan Le Duc, Myriam Lamolle, Olivier Curé

SPARQL Query Answering over OWL Ontologies

Abstract

The SPARQL query language is currently being extended by W3C with so-called entailment regimes, which define how queries are evaluated under more expressive semantics than SPARQL’s standard simple entailment. We describe a sound and complete algorithm for the OWL Direct Semantics entailment regime. The queries of the regime are very expressive since variables can occur within complex class expressions and can also bind to class or property names. We propose several novel optimizations such as strategies for determining a good query execution order, query rewriting techniques, and show how specialized OWL reasoning tasks and the class and property hierarchy can be used to reduce the query execution time. We provide a prototypical implementation and evaluate the efficiency of the proposed optimizations. For standard conjunctive queries our system performs comparably to already deployed systems. For complex queries an improvement of up to three orders of magnitude can be observed.

Ilianna Kollia, Birte Glimm, Ian Horrocks

Epistemic Querying of OWL Knowledge Bases

Abstract

Epistemic querying extends standard ontology inferencing by allowing for deductive introspection. We propose a technique for epistemic querying of OWL 2 ontologies not featuring nominals and universal roles by a reduction to a series of standard OWL 2 reasoning steps thereby enabling the deployment of off-the-shelf OWL 2 reasoning tools for this task. We prove formal correctness of our method, justify the omission of nominals and universal role, and provide an implementation as well as evaluation results.

Anees Mehdi, Sebastian Rudolph, Stephan Grimm

A Practical Approach for Computing Generalization Inferences in $\mathcal{EL}$

Abstract

We present methods that compute generalizations of concepts or individuals described in ontologies written in the Description Logic $\mathcal{EL}$. These generalizations are the basis of methods for ontology design and are the core of concept similarity measures. The reasoning service least common subsumer (lcs) generalizes a set of concepts. Similarly, the most specific concept (msc) generalizes an individual into a concept description. For $\mathcal{EL}$ with general $\mathcal{EL}$-TBoxes, the lcs and the msc may not exist. However, it is possible to find a concept description that is the lcs (msc) up to a certain role-depth.

In this paper we present a practical approach for computing the lcs and msc with a bounded depth, based on the polynomial-time completion algorithm for $\mathcal{EL}$ and describe its implementation.

Rafael Peñaloza, Anni-Yasmin Turhan

Backmatter

Titel: The Semantic Web: Research and Applications
herausgegeben von: Grigoris Antoniou
Marko Grobelnik
Elena Simperl
Bijan Parsia
Dimitris Plexousakis
Pieter De Leenheer
Jeff Pan
Verlag: Springer Berlin Heidelberg
Electronic ISBN: 978-3-642-21034-1
Print ISBN: 978-3-642-21033-4
DOI: https://doi.org/10.1007/978-3-642-21034-1