Showing papers on "Semantic Web published in 2012"

OWL 2 Web Ontology Language Primer (Second Edition)

Abstract: The OWL 2 Web Ontology Language, informally OWL 2, is an ontology language for the Semantic Web with formally defined meaning. OWL 2 ontologies provide classes, properties, individuals, and data values and are stored as Semantic Web documents. OWL 2 ontologies can be used along with information written in RDF, and OWL 2 ontologies themselves are primarily exchanged as RDF documents. The OWL 2 Document Overview describes the overall state of OWL 2, and should be read before other OWL 2 documents.

Semantics for the Internet of Things: Early Progress and Back to the Future

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.

Semantic web portal and platform

Abstract: The patent describes a single location and application on a network where a user can organize public, group, and private/personal information and have this single, location accessible to the public. A new, ontology-driven portal that organizes all three categories of data according to various “facets” using underlying ontologies to define each “facet” and wherein any type of information can be classified and linked to other types of information is disclosed. An application that enables a user to effectively utilize and manage knowledge and data the user posses and allows other users to effectively and seamlessly benefit from the user's knowledge and data over a computer network is also disclosed. A method of processing content created by a user utilizing a semantic, ontology-driven portal on a computer network is described. The semantic portal application provides the user with a content base, such as a semantic form or meta-form, for creating a semantic posting. The semantic portal utilizes a knowledge data structure, such as a taxonomy or ontology, in preparing a semantic posting based on the information provided by the user via the content base. The semantic portal application prepares a preview of a semantic posting for evaluation by the user. The semantic posting is then either modified by the user or accepted and posted by the user for external parties to view.

Factorizing YAGO: scalable machine learning for linked data

Abstract: Vast amounts of structured information have been published in the Semantic Web's Linked Open Data (LOD) cloud and their size is still growing rapidly. Yet, access to this information via reasoning and querying is sometimes difficult, due to LOD's size, partial data inconsistencies and inherent noisiness. Machine Learning offers an alternative approach to exploiting LOD's data with the advantages that Machine Learning algorithms are typically robust to both noise and data inconsistencies and are able to efficiently utilize non-deterministic dependencies in the data. From a Machine Learning point of view, LOD is challenging due to its relational nature and its scale. Here, we present an efficient approach to relational learning on LOD data, based on the factorization of a sparse tensor that scales to data consisting of millions of entities, hundreds of relations and billions of known facts. Furthermore, we show how ontological knowledge can be incorporated in the factorization to improve learning results and how computation can be distributed across multiple nodes. We demonstrate that our approach is able to factorize the YAGO~2 core ontology and globally predict statements for this large knowledge base using a single dual-core desktop computer. Furthermore, we show experimentally that our approach achieves good results in several relational learning tasks that are relevant to Linked Data. Once a factorization has been computed, our model is able to predict efficiently, and without any additional training, the likelihood of any of the 4.3 ⋅ 1014 possible triples in the YAGO~2 core ontology.

Ontology-based semantic similarity: A new feature-based approach

Abstract: Estimation of the semantic likeness between words is of great importance in many applications dealing with textual data such as natural language processing, knowledge acquisition and information retrieval. Semantic similarity measures exploit knowledge sources as the base to perform the estimations. In recent years, ontologies have grown in interest thanks to global initiatives such as the Semantic Web, offering an structured knowledge representation. Thanks to the possibilities that ontologies enable regarding semantic interpretation of terms many ontology-based similarity measures have been developed. According to the principle in which those measures base the similarity assessment and the way in which ontologies are exploited or complemented with other sources several families of measures can be identified. In this paper, we survey and classify most of the ontology-based approaches developed in order to evaluate their advantages and limitations and compare their expected performance both from theoretical and practical points of view. We also present a new ontology-based measure relying on the exploitation of taxonomical features. The evaluation and comparison of our approach's results against those reported by related works under a common framework suggest that our measure provides a high accuracy without some of the limitations observed in other works.

LinkedGeoData: A core for a web of spatial open data

Abstract: The Semantic Web eases data and information integration tasks by providing an infrastructure based on RDF and ontologies. In this paper, we contribute to the development of a spatial Data Web by elaborating on how the collaboratively collected OpenStreetMap data can be interactively transformed and represented adhering to the RDF data model. This transformation will simplify information integration and aggregation tasks that require comprehensive background knowledge related to spatial features such as ways, structures, and landscapes. We describe how this data is interlinked with other spatial data sets, how it can be made accessible for machines according to the Linked Data paradigm and for humans by means of several applications, including a faceted geo-browser. The spatial data, vocabularies, interlinks and some of the applications are openly available in the LinkedGeoData project.

Enabling the geospatial Semantic Web with Parliament and GeoSPARQL

Abstract: As the amount of Linked Open Data on the web increases, so does the amount of data with an inherent spatial context. Without spatial reasoning, however, the value of this spatial context is limited. Over the past decade there have been several vocabularies and query languages that attempt to exploit this knowledge and enable spatial reasoning. These attempts provide varying levels of support for fundamental geospatial concepts. GeoSPARQL, a forthcoming OGC standard, attempts to unify data access for the geospatial Semantic Web. As authors of the Parliament triple store and contributors to the GeoSPARQL specification, we are particularly interested in the issues of geospatial data access and indexing. In this paper, we look at the overall state of geospatial data in the Semantic Web, with a focus on GeoSPARQL. We first describe the motivation for GeoSPARQL, then the current state of the art in industry and research, followed by an example use case, and finally our implementation of GeoSPARQL in the Parliament triple store.

Linked open data to support content-based recommender systems

Abstract: The World Wide Web is moving from a Web of hyper-linked Documents to a Web of linked Data Thanks to the Semantic Web spread and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets These datasets are connected with each other to form the so called Linked Open Data cloud As of today, there are tons of RDF data available in the Web of Data, but only few applications really exploit their potential power In this paper we show how these data can successfully be used to develop a recommender system (RS) that relies exclusively on the information encoded in the Web of Data We implemented a content-based RS that leverages the data available within Linked Open Data datasets (in particular DBpedia, Freebase and LinkedMDB) in order to recommend movies to the end users We extensively evaluated the approach and validated the effectiveness of the algorithms by experimentally measuring their accuracy with precision and recall metrics

TasteWeights: a visual interactive hybrid recommender system

Abstract: This paper presents an interactive hybrid recommendation system that generates item predictions from multiple social and semantic web resources, such as Wikipedia, Facebook, and Twitter. The system employs hybrid techniques from traditional recommender system literature, in addition to a novel interactive interface which serves to explain the recommendation process and elicit preferences from the end user. We present an evaluation that compares different interactive and non-interactive hybrid strategies for computing recommendations across diverse social and semantic web APIs. Results of the study indicate that explanation and interaction with a visual representation of the hybrid system increase user satisfaction and relevance of predicted content.

Ontology Engineering in a Networked World

Abstract: The Semantic Web is characterized by the existence of a very large number of distributed semantic resources, which together define a network of ontologies. These ontologies in turn are interlinked through a variety of different meta-relationships such as versioning, inclusion, and many more. This scenario is radically different from the relatively narrow contexts in which ontologies have been traditionally developed and applied, and thus calls for new methods and tools to effectively support the development of novel network-oriented semantic applications. This book by Surez-Figueroa et al. provides the necessary methodological and technological support for the development and use of ontology networks, which ontology developers need in this distributed environment. After an introduction, in its second part the authors describe the NeOn Methodology framework. The books third part details the key activities relevant to the ontology engineering life cycle. For each activity, a general introduction, methodological guidelines, and practical examples are provided. The fourth part then presents a detailed overview of the NeOn Toolkit and its plug-ins. Lastly, case studies from the pharmaceutical and the fishery domain round out the work. The book primarily addresses two main audiences: students (and their lecturers) who need a textbook for advanced undergraduate or graduate courses on ontology engineering, and practitioners who need to develop ontologies in particular or Semantic Web-based applications in general. Its educational value is maximized by its structured approach to explaining guidelines and combining them with case studies and numerous examples. The description of the open source NeOn Toolkit provides an additional asset, as it allows readers to easily evaluate and apply the ideas presented.

Social knowledge-based recommender system. Application to the movies domain

Abstract: With the advent of the Social Web and the growing popularity of Web 2.0 applications, recommender systems are gaining momentum. The recommendations generated by these systems aim to provide end users with suggestions about information items, social elements, products or services that are likely to be of their interest. The traditional syntactic-based recommender systems suffer from a number of shortcomings that hamper their effectiveness. As semantic technologies mature, they provide a consistent and reliable basis for dealing with data at the knowledge level. Adding semantically empowered techniques to recommender systems can significantly improve the overall quality of recommendations. In this work, a hybrid recommender system based on knowledge and social networks is presented. Its evaluation in the cinematographic domain yields very promising results compared to state-of-the-art solutions.

The meaningful use of big data: four perspectives -- four challenges

Abstract: Twenty-five Semantic Web and Database researchers met at the 2011 STI Semantic Summit in Riga, Latvia July 6-8, 2011[1] to discuss the opportunities and challenges posed by Big Data for the Semantic Web, Semantic Technologies, and Database communities. The unanimous conclusion was that the greatest shared challenge was not only engineering Big Data, but also doing so meaningfully. The following are four expressions of that challenge from different perspectives.

SenticNet 2: A Semantic and Affective Resource for Opinion Mining and Sentiment Analysis

Abstract: Web 2.0 has changed the ways people communicate, collaborate, and express their opinions and sentiments. But despite social data on the Web being perfectly suitable for human consumption, they remain hardly accessible to machines. To bridge the cognitive and affective gap between word-level natural language data and the concept-level sentiments conveyed by them, we developed SenticNet 2, a publicly available semantic and affective resource for opinion mining and sentiment analysis. SenticNet 2 is built by means of sentic computing, a new paradigm that exploits both AI and Semantic Web techniques to better recognize, interpret, and process natural language opinions. By providing the semantics and sentics (that is, the cognitive and affective information) associated with over 14,000 concepts, SenticNet 2 represents one of the most comprehensive semantic resources for the development of affect-sensitive applications in fields such as social data mining, multimodal affective HCI, and social media marketing.

Interchanging lexical resources on the Semantic Web

Abstract: Lexica and terminology databases play a vital role in many NLP applications, but currently most such resources are published in application-specific formats, or with custom access interfaces, leading to the problem that much of this data is in "data silos" and hence difficult to access. The Semantic Web and in particular the Linked Data initiative provide effective solutions to this problem, as well as possibilities for data reuse by inter-lexicon linking, and incorporation of data categories by dereferencable URIs. The Semantic Web focuses on the use of ontologies to describe semantics on the Web, but currently there is no standard for providing complex lexical information for such ontologies and for describing the relationship between the lexicon and the ontology. We present our model, lemon, which aims to address these gaps while building on existing work, in particular the Lexical Markup Framework, the ISOcat Data Category Registry, SKOS (Simple Knowledge Organization System) and the LexInfo and LIR ontology-lexicon models.

Semi-automatically mapping structured sources into the semantic web

Abstract: Linked data continues to grow at a rapid rate, but a limitation of a lot of the data that is being published is the lack of a semantic description. There are tools, such as D2R, that allow a user to quickly convert a database into RDF, but these tools do not provide a way to easily map the data into an existing ontology. This paper presents a semi-automatic approach to map structured sources to ontologies in order to build semantic descriptions (source models). Since the precise mapping is sometimes ambiguous, we also provide a graphical user interface that allows a user to interactively refine the models. The resulting source models can then be used to convert data into RDF with respect to a given ontology or to define a SPARQL end point that can be queried with respect to an ontology. We evaluated the overall approach on a variety of sources and show that it can be used to quickly build source models with minimal user interaction.

Semantic turkey: a browser-integrated environment for knowledge acquisition and management

Abstract: Born four years ago as a Semantic Web extension for the web browser Firefox, Semantic Turkey pushed forward the traditional concept of links&folders-based bookmarking to a new dimension, allowing users to keep track of relevant information from visited web sites and to organize the collected content according to standard or personally defined ontologies. Today, the tool has broken the boundaries of its original intents and can be considered, under every aspect, an extensible platform for knowledge management and acquisition. The semantic bookmarking and annotation facilities of Semantic Turkey are now supporting just a part of a whole methodology where different actors, from domain experts to knowledge engineers, can cooperate in developing, building and populating ontologies while navigating the Web.

The Semantic Web – ISWC 2012

Abstract: Research Track- MORe: Modular Combination of OWL Reasoners for Ontology Classification- A Formal Semantics for Weighted Ontology- Personalised Graph-Based Selection of Web APIs- Instance-Based Matching of Large Ontologies Using Locality-Sensitive Hashing- Automatic Typing of DBpedia Entities- Performance Heterogeneity and Approximate Reasoning in Description Logic Ontologies- Concept-Based Semantic Difference in Expressive Description Logics- SPLODGE: Systematic Generation of SPARQL Benchmark Queries for Linked Open Data- RDFS Reasoning on Massively Parallel Hardware- An Efficient Bit Vector Approach to Semantics-Based Machine Perception in Resource-Constrained Devices- Semantic Enrichment by Non-experts: Usability of Manual Annotation Tools- Ontology-Based Access to Probabilistic Data with OWL QL- Predicting Reasoning Performance Using Ontology Metrics- Formal Verification of Data Provenance Records- Cost Based Query Ordering over OWL Ontologies- Robust Runtime Optimization and Skew-Resistant Execution of Analytical SPARQL Queries on Pig- Large-Scale Learning of Relation-Extraction Rules with Distant Supervision from the Web- The Not-So-Easy Task of Computing Class Subsumptions in OWL RL- Strabon: A Semantic Geospatial DBMS- DeFacto - Deep Fact Validation- Feature LDA: A Supervised Topic Model for Automatic Detection of Web API Documentations from the Web- Efficient Execution of Top-K SPARQL Queries- Collaborative Filtering by Analyzing Dynamic User Interests Modeled by Taxonomy- Link Discovery with Guaranteed Reduction Ratio in Affine Spaces with Minkowski Measures- Hitting the Sweetspot: Economic Rewriting of Knowledge Bases- Mining Semantic Relations between Research Areas- Discovering Concept Coverings in Ontologies of Linked Data Sources- Ontology Constraints in Incomplete and Complete Data- A Machine Learning Approach for Instance Matching Based on Similarity Metrics- Who Will Follow Whom? Exploiting Semantics for Link Prediction in Attention-Information Networks- On the Diversity and Availability of Temporal Information in Linked Open Data- Semantic Sentiment Analysis of Twitter- CrowdMap: Crowdsourcing Ontology Alignment with Microtasks- Domain-Aware Ontology Matching- Rapidly Integrating Services into the Linked Data Cloud- An Evidence-Based Verification Approach to Extract Entities and Relations for Knowledge Base Population- Blank Node Matching and RDF/S Comparison Functions- Hybrid SPARQL Queries: Fresh vs Fast Results- Provenance for SPARQL Queries- SRBench: A Streaming RDF/SPARQL Benchmark- Scalable Geo-thematic Query Answering- In-Use Track- Managing the Life-Cycle of Linked Data with the LOD2 Stack- Achieving Interoperability through Semantics-Based Technologies: The Instant Messaging Case- Linking Smart Cities Datasets with Human Computation - The Case of UrbanMatch- ourSpaces - Design and Deployment of a Semantic Virtual Research Environment- Embedded EL+ Reasoning on Programmable Logic Controllers- Experiences with Modeling Composite Phenotypes in the SKELETOME Project- Toward an Ecosystem of LOD in the Field: LOD Content Generation and Its Consuming Service- Applying Semantic Web Technologies for Diagnosing Road Traffic Congestions- deqa: Deep Web Extraction for Question Answering- QuerioCity: A Linked Data Platform for Urban Information Management- Semantic Similarity-Driven Decision Support in the Skeletal Dysplasia Domain- Using SPARQL to Query BioPortal Ontologies and Metadata- Trentino Government Linked Open Geo-data: A Case Study- Semantic Reasoning in Context-Aware Assistive Environments to Support Ageing with Dementia- Query Driven Hypothesis Generation for Answering Queries over NLP Graphs- A Comparison of Hard Filters and Soft Evidence for Answer Typing in Watson- Incorporating Semantic Knowledge into Dynamic Data Processing for Smart Power Grids- Evaluations and Experiments Track- Evaluating Semantic Search Query Approaches with Expert and Casual Users- Extracting Justifications from BioPortal Ontologies- Linked Stream Data Processing Engines: Facts and Figures- Benchmarking Federated SPARQL Query Engines: Are ExistingTestbeds Enough?- Tag Recommendation for Large-Scale Ontology-Based Information Systems- Evaluation of Techniques for Inconsistency Handling in OWL 2 QL Ontologies- Evaluating Entity Summarization Using a Game-Based Ground Truth- Evaluation of a Layered Approach to Question Answering over Linked Data- Doctoral Consortium - Long Papers- Cross Lingual Semantic Search by Improving Semantic Similarity and Relatedness Measures- Quality Reasoning in the Semantic Web- Burst the Filter Bubble: Using Semantic Web to Enable Serendipity- Reconstructing Provenance- Very Large Scale OWL Reasoning through Distributed Computation- Replication for Linked Data- Scalable and Domain-Independent Entity Coreference: Establishing High Quality Data Linkages across Heterogeneous Data Sources- Doctoral Consortium - Short Papers- Distributed Reasoning on Semantic Data Streams- Reusing XML Schemas' Information as a Foundation for Designing Domain Ontologies - A Multi-domain Framework for Community Building Based on Data Tagging- Towards a Theoretical Foundation for the Harmonization of Linked Data- Knowledge Pattern Extraction and Their Usage in Exploratory Search- SPARQL Update for Complex Event Processing- Online Unsupervised Coreference Resolution for Semi-structured Heterogeneous Data- Composition of Linked Data-Based RESTful Services

Bringing relational databases into the semantic web: a survey

Abstract: Relational databases are considered one of the most popular storage solutions for various kinds of data and they have been recognized as a key factor in generating huge amounts of data for Semantic Web applications. Ontologies, on the other hand, are one of the key concepts and main vehicle of knowledge in the Semantic Web research area. The problem of bridging the gap between relational databases and ontologies has attracted the interest of the Semantic Web community, even from the early years of its existence and is commonly referred to as the database-to-ontology mapping problem. However, this term has been used interchangeably for referring to two distinct problems: namely, the creation of an ontology from an existing database instance and the discovery of mappings between an existing database instance and an existing ontology. In this paper, we clearly define these two problems and present the motivation, benefits, challenges and solutions for each one of them. We attempt to gather the most notable approaches proposed so far in the literature, present them concisely in tabular format and group them under a classification scheme. We finally explore the perspectives and future research steps for a seamless and meaningful integration of databases into the Semantic Web.

Publishing and Using Cultural Heritage Linked Data on the Semantic Web

Abstract: Cultural Heritage (CH) data is syntactically and semantically heterogeneous, multilingual, semantically rich, and highly interlinked. It is produced in a distributed, open fashion by museums, libraries, archives, and media organizations, as well as individual persons. Managing publication of such richness and variety of content on the Web, and at the same time supporting distributed, interoperable content creation processes, poses challenges where traditional publication approaches need to be re-thought. Application of the principles and technologies of Linked Data and the Semantic Web is a new, promising approach to address these problems. This development is leading to the creation of large national and international CH portals, such as Europeana, to large open data repositories, such as the Linked Open Data Cloud, and massive publications of linked library data in the U.S., Europe, and Asia. Cultural Heritage has become one of the most successful application domains of Linked Data nd Semantic Web technologies. This book gives an overview on why, when, and how Linked (Open) Data and Semantic Web technologies can be employed in practice in publishing CH collections and other content on the Web. The text first motivates and presents a general semantic portal model and publishing framework as a solution approach to distributed semantic content creation, based on an ontology infrastructure. On the Semantic Web, such an infrastructure includes shared metadata models, ontologies, and logical reasoning, and is supported by shared ontology and other Web services alleviating the use of the new technology and linked data in legacy cataloging systems. The goal of all this is to provide layman users and researchers with new, more intelligent and usable Web applications that can be utilized by other Web applications, too, via well-defined Application Programming Interfaces (API). At the same time, it is possible to provide publishing organizations with more cost-efficient so utions for content creation and publication. This book is targeted to computer scientists, museum curators, librarians, archivists, and other CH professionals interested in Linked Data and CH applications on the Semantic Web. The text is focused on practice and applications, making it suitable to students, researchers, and practitioners developing Web services and applications of CH, as well as to CH managers willing to understand the technical issues and challenges involved in linked data publication. Table of Contents: Cultural Heritage on the Semantic Web / Portal Model for Collaborative CH Publishing / Requirements for Publishing Linked Data / Metadata Schemas / Domain Vocabularies and Ontologies / Logic Rules for Cultural Heritage / Cultural Content Creation / Semantic Services for Human and Machine Users / Conclusions

OWL 2 Web Ontology Language Manchester Syntax (Second Edition)

Abstract: The OWL 2 Web Ontology Language, informally OWL 2, is an ontology language for the Semantic Web with formally defined meaning. OWL 2 ontologies provide classes, properties, individuals, and data values and are stored as Semantic Web documents. OWL 2 ontologies can be used along with information written in RDF, and OWL 2 ontologies themselves are primarily exchanged as RDF documents. The OWL 2 Document Overview describes the overall state of OWL 2, and should be read before other OWL 2 documents.

Stream reasoning and complex event processing in ETALIS

Abstract: Addressing dynamics and notifications in the Semantic Web realm has recently become an important area of research. Run time data is continuously generated by multiple social networks, sensor networks, various on-line services and so forth. How to get advantage of this continuously arriving data events remains a challenge --that is, how to integrate heterogeneous event streams, combine them with background knowledge e.g., an ontology, and perform event processing and stream reasoning. In this paper we describe ETALIS --a system which enables specification and monitoring of changes in near real time. Changes can be specified as complex event patterns, and ETALIS can detect them in real time. Moreover the system can perform reasoning over streaming events with respect to background knowledge. ETALIS implements two languages for specification of event patterns: ETALIS Language for Events, and Event Processing SPARQL. ETALIS has various applicabilities in capturing changes in semantic networks, broadcasting notifications to interested parties, and creating further changes based on processing of the temporal, static, or slowly evolving knowledge.

Geospatial semantics and linked spatiotemporal data --Past, present, and future

Abstract: The Geosciences and Geography are not just yet another application area for semantic technologies. The vast heterogeneity of the involved disciplines ranging from the natural sciences to the social sciences introduces new challenges in terms of interoperability. Moreover, the inherent spatial and temporal information components also require distinct semantic approaches. For these reasons, geospatial semantics, geo-ontologies, and semantic interoperability have been active research areas over the last 20 years. The geospatial semantics community has been among the early adopters of the Semantic Web, contributing methods, ontologies, use cases, and datasets. Today, geographic information is a crucial part of many central hubs on the Linked Data Web. In this editorial, we outline the research field of geospatial semantics, highlight major research directions and trends, and glance at future challenges. We hope that this text will be valuable for geoscientists interested in semantics research as well as knowledge engineers interested in spatiotemporal data.

Enabling Query Technologies for the Semantic Sensor Web

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.