Semantic RDF Research Articles

SimE4KG: Distributed and Explainable Multi-Modal Semantic Similarity Estimation for Knowledge Graphs

In recent years, exciting sources of data have been modeled as knowledge graphs (KGs). This modeling represents both structural relationships and the entity-specific multi-modal data in KGs. In various data analytics pipelines and machine learning (ML), the task of semantic similarity estimation plays a significant role. Assigning similarity values to entity pairs is needed in recommendation systems, clustering, classification, entity matching/disambiguation and many others. Efficient and scalable frameworks are needed to handle the quadratic complexity of all-pair semantic similarity on Big Data KGs. Moreover, heterogeneous KGs demand multi-modal semantic similarity estimation to cover the versatile contents like categorical relations between classes or their attribute literals like strings, timestamps or numeric data. In this paper, we propose the SimE4KG framework as a resource providing generic open-source modules that perform semantic similarity estimation in multi-modal KGs. To justify the computational costs of similarity estimation, the SimE4KG generates reproducible, reusable and explainable results. The pipeline results are a native semantic RDF KG, including the experiment results, hyper-parameter setup and explanation of the results, like the most influential features. For fast and scalable execution in memory, we implemented the distributed approach using Apache Spark. The entire development of this framework is integrated into the holistic distributed Semantic ANalytics StAck (SANSA).

An enhanced version of the PHIRI infrastructure: improving the technological solutions

Abstract The proof of concept tested by PHIRI consisted of the development of several research questions in multiple data hubs using a federated approach. It was possible to embed the use cases’ analytical pipelines in a portable standalone (i.e. docker image) and distribute it in different health data hubs and technological environments sources for execution. The tested solution has the advantage of not moving sensitive data out of the silos and thus protecting privacy - the code meets data and not the opposite. Some precious lessons provide guidance on how to further develop the PHIRI infrastructure. 1) A deep knowledge on what data is available in the different data hubs of a federation is key since the basis for the development of a research query is the construction of a data model that is common to all the nodes in the federation. In an eventual enhanced PHIRI infrastructure, a solution will be implementing a semantic information system that allows the exchange of metadata using federated and interoperable metadata catalogues based on Semantic RDF graph databases, compliant with the W3C DCAT metadata standard and exposing the end-points of the SPARQL querying language of the Web of linked-data. 2) Making available training samples mimicking real-world data within the docker image has been of high added-value for the development of the use cases’ analytical pipelines. In an eventual enhanced PHIRI infrastructure, a generalisation could consist of setting up a “knowledge hub” where synthetic data, twinning the population, data would allow any expert users to search and find data through federated queries and prepare and train their analytical pipelines; the “knowledge hub” would provide a computational environment (e.g. Jupyter as a service playground), the necessary tools (i.e. cookbooks and capacity building services) and training samples to answer research questions, with the advantage of using data that is anonymous by nature and open access.

Summarizing semantic graphs: a survey

The explosion in the amount of the available RDF data has lead to the need to explore, query and understand such data sources. Due to the complex structure of RDF graphs and their heterogeneity, the exploration and understanding tasks are significantly harder than in relational databases, where the schema can serve as a first step toward understanding the structure. Summarization has been applied to RDF data to facilitate these tasks. Its purpose is to extract concise and meaningful information from RDF knowledge bases, representing their content as faithfully as possible. There is no single concept of RDF summary, and not a single but many approaches to build such summaries; each is better suited for some uses, and each presents specific challenges with respect to its construction. This survey is the first to provide a comprehensive survey of summarization method for semantic RDF graphs. We propose a taxonomy of existing works in this area, including also some closely related works developed prior to the adoption of RDF in the data management community; we present the concepts at the core of each approach and outline their main technical aspects and implementation. We hope the survey will help readers understand this scientifically rich area and identify the most pertinent summarization method for a variety of usage scenarios.

Enabling intent to configure scientific networks for high performance demands

Globally distributed scientific experiments involve movement of massive data volumes and many collaborators performing distributed data analysis. With complex workloads and heterogeneous resources, each user may desire certain behavior characteristics for their network paths. In this paper, we present the iNDIRA tool, which interacts with SDN north-bound interfaces to enable intent-based networking. It provides reliable, simple, and technology-agnostic communication between users and networks. Focusing particularly on science applications, iNDIRA uses natural language processing to construct semantic RDF graphs to understand, interact, and create the required network services. The technical challenges addressed by iNDIRA are: (1) development of a high-level descriptive language to query network-application requirements, (2) provides keyword identification and condition checking based on user profiles and topology details, (3) allows user negotiation based on the current network state, and (4) integrates network provisioning and service tools used by the application. iNDIRA is implemented on the ESnet network, where it interacts with OpenNSA (aka the NSI client) and Globus data transfer tools, to build complex cross-domain network paths for heterogeneous science applications, and perform secure data transfer. We argue that iNDIRA’s approach presents users with an alternative approach to interact and communicate their network demands, allowing seamless network service integration.

Geographical localization of web domains and organization addresses recognition by employing natural language processing, Pattern Matching and clustering

Nowadays, the World Wide Web is growing at increasing rate and speed, and consequently the online available resources populating Internet represent a large source of knowledge for various business and research interests. For instance, over the past years, increasing attention has been focused on retrieving information related to geographical location of places and entities, which is largely contained in web pages and documents. However, such resources are represented in a wide variety of generally unstructured formats, and this actually does not help final users to find desired information items. The automatic annotation and comprehension of toponyms, location names and addresses (at different resolution and granularity levels) can deliver significant benefits for the whole web community by improving search engines filtering capabilities and intelligent data mining systems. The present paper addresses the problem of gathering geographical information from unstructured text in web pages and documents. In the specific, the proposed method aims at extracting geographical location (at street number resolution) of commercial companies and services, by annotating geo-related information from their web domains. The annotation process is based on Natural Language Processing (NLP) techniques for text comprehension, and relies on Pattern Matching and Hierarchical Cluster Analysis for recognizing and disambiguating geographical entities. Geotagging performances have been assessed by evaluating Precision, Recall and F-Measure of the proposed system output (represented in form of semantic RDF triples) against both a geo-annotated reference database and a semantic Smart City repository.

A Mobile Query Service for Integrated Access to Large Numbers of Online Semantic Web Data Sources

From the Semantic Web’s inception, a number of concurrent initiatives have given rise to multiple segments: large semantic datasets, exposed by query endpoints; online Semantic Web documents, in the form of RDF files; and semantically annotated web content (e.g., using RDFa), semantic sources in their own right. In various mobile application scenarios, online semantic data has proven to be useful. While query endpoints are most commonly exploited, they are mainly useful to expose large semantic datasets. Alternatively, mobile RDF stores are utilized to query local semantic data, but this requires the design-time identification & replication of relevant data. Instead, we present a mobile query service that supports on the-fly and integrated querying of semantic data, originating from a largely unused portion of the Semantic Web, comprising online RDF files and semantics embedded in annotated webpages. To that end, our solution performs dynamic identification, retrieval and caching of query-relevant semantic data. We explore several data identification and caching alternatives, and investigate the utility of source metadata in optimizing these tasks. Further, we introduce a novel cache replacement strategy, fine tuned to the described query dataset, and include explicit support for the Open World Assumption. An extensive experimental validation evaluates the query service and its alternative components.

A mobile query service for integrated access to large numbers of online semantic web data sources

From the Semantic Web’s inception, a number of concurrent initiatives have given rise to multiple segments: large semantic datasets, exposed by query endpoints; online Semantic Web documents, in the form of RDF files; and semantically annotated web content (e.g., using RDFa), semantic sources in their own right. In various mobile application scenarios, online semantic data has proven to be useful. While query endpoints are most commonly exploited, they are mainly useful to expose large semantic datasets. Alternatively, mobile RDF stores are utilized to query local semantic data, but this requires the design-time identification and replication of relevant data. Instead, we present a mobile query service that supports on-the-fly and integrated querying of semantic data, originating from a largely unused portion of the Semantic Web, comprising online RDF files and semantics embedded in annotated webpages. To that end, our solution performs dynamic identification, retrieval and caching of query-relevant semantic data. We explore several data identification and caching alternatives, and investigate the utility of source metadata in optimizing these tasks. Further, we introduce a novel cache replacement strategy, fine-tuned to the described query dataset, and include explicit support for the Open World Assumption. An extensive experimental validation evaluates the query service and its alternative components.

Using Description Logics for RDF Constraint Checking and Closed-World Recognition

RDF and Description Logics work in an open-world setting where absence of information is not information about absence. Nevertheless, Description Logic axioms can be interpreted in a closed-world setting and in this setting they can be used for both constraint checking and closed-world recognition against information sources. When the information sources are expressed in well-behaved RDF or RDFS (i.e., RDF graphs interpreted in the RDF or RDFS semantics) this constraint checking and closed-world recognition is simple to describe. Further this constraint checking can be implemented as SPARQL querying and thus effectively performed.

Semantic RDF Based Integration Framework for Heterogeneous XML Data Sources

A significant amount of data on the Web is in the XML format or may easily be converted to XML or to its variations. XML is still the most appropriate language for data interchange and serialization. In this paper, a new framework which can integrate any heterogeneous XML data sources is presented. Each data source is translated into semantically meaningful regular expressions without changing original data source. Proposed framework has two major phases for data preparation. In the first phase, each data source is processed to obtain regular expressions which accommodate with the design choices that made in target by utilizing known global semantic vocabulary as an input. The second phase combines these regular expressions to get a global schema by preserving the original source data. A regular expression generator tool which produces regular expressions by regarding vocabulary and an integrator tool box which integrates and processes regular expressions, are also introduced.

Don't Like RDF Reification? Making Statements about Statements Using Singleton Property.

Statements about RDF statements, or meta triples, provide additional information about individual triples, such as the source, the occurring time or place, or the certainty. Integrating such meta triples into semantic knowledge bases would enable the querying and reasoning mechanisms to be aware of provenance, time, location, or certainty of triples. However, an efficient RDF representation for such meta knowledge of triples remains challenging. The existing standard reification approach allows such meta knowledge of RDF triples to be expressed using RDF by two steps. The first step is representing the triple by a Statement instance which has subject, predicate, and object indicated separately in three different triples. The second step is creating assertions about that instance as if it is a statement. While reification is simple and intuitive, this approach does not have formal semantics and is not commonly used in practice as described in the RDF Primer. In this paper, we propose a novel approach called Singleton Property for representing statements about statements and provide a formal semantics for it. We explain how this singleton property approach fits well with the existing syntax and formal semantics of RDF, and the syntax of SPARQL query language. We also demonstrate the use of singleton property in the representation and querying of meta knowledge in two examples of Semantic Web knowledge bases: YAGO2 and BKR. Our experiments on the BKR show that the singleton property approach gives a decent performance in terms of number of triples, query length and query execution time compared to existing approaches. This approach, which is also simple and intuitive, can be easily adopted for representing and querying statements about statements in other knowledge bases.

Growing triples on trees: an XML-RDF hybrid model for annotated documents

Since the beginning of the Semantic Web initiative, significant efforts have been invested in finding efficient ways to publish, store, and query metadata on the Web. RDF and SPARQL have become the standard data model and query language, respectively, to describe resources on the Web. Large amounts of RDF data are now available either as stand-alone datasets or as metadata over semi-structured (typically XML) documents. The ability to apply RDF annotations over XML data emphasizes the need to represent and query data and metadata simultaneously. We propose XR, a novel hybrid data model capturing the structural aspects of XML data and the semantics of RDF, also enabling us to reason about XML data. Our model is general enough to describe pure XML or RDF datasets, as well as RDF-annotated XML data, where any XML node can act as a resource. This data model comes with the XRQ query language that combines features of both XQuery and SPARQL. To demonstrate the feasibility of this hybrid XML-RDF data management setting, and to validate its interest, we have developed an XR platform on top of well-known data management systems for XML and RDF. In particular, the platform features several XRQ query processing algorithms, whose performance is experimentally compared.

A framework for modular ERDF ontologies

The success of the Semantic Web is impossible without any form of modularity, encapsulation, and access control. In an earlier paper, we extended RDF graphs with weak and strong negation, as well as derivation rules. The ERDF #n-stable model semantics of the extended RDF framework (ERDF) is defined, extending RDF(S) semantics. In this paper, we propose a framework for modular ERDF ontologies, called modular ERDF framework, which enables collaborative reasoning over a set of ERDF ontologies, while support for hidden knowledge is also provided. In particular, the modular ERDF stable model semantics of modular ERDF ontologies is defined, extending the ERDF #n-stable model semantics. Our proposed framework supports local semantics and different points of view, local closed-world and open-world assumptions, and scoped negation-as-failure. Several complexity results are provided.

Blank Nodes in RDF

Semantic Web plays an important role in the Web of future. The RDF data is the key component which establishes the basis of the Semantic Web. In this paper, we conlude the usages and the possible problems of blank nodes of RDF with detailed analyses of the applications and semantics of the blank nodes in RDF graphs. Give special attentions to the inconsistency between RDF semantics and SPARQL semantics of blank nodes. Employ the concept of “lean graph” in the pre-process of the RDF data operation, propose a method of using the entailment relations between RDF graphs and the transformation from blank nodes to the URI references to eliminate the blank nodes in RDF graphs, and give the theoretic background to support the method. Lastly, some referenced methods of transforming the blank nodes to URI references are provided.

View selection in Semantic Web databases

We consider the setting of a Semantic Web database, containing both explicit data encoded in RDF triples, and implicit data, implied by the RDF semantics. Based on a query workload, we address the problem of selecting a set of views to be materialized in the database, minimizing a combination of query processing, view storage, and view maintenance costs. Starting from an existing relational view selection method, we devise new algorithms for recommending view sets, and show that they scale significantly beyond the existing relational ones when adapted to the RDF context. To account for implicit triples in query answers, we propose a novel RDF query reformulation algorithm and an innovative way of incorporating it into view selection in order to avoid a combinatorial explosion in the complexity of the selection process. The interest of our techniques is demonstrated through a set of experiments.

Connecting Archaeological Data and Grey Literature via Semantic Cross Search

Differing terminology and database structure hinders meaningful cross search of excavation datasets. Matching free text grey literature reports with datasets poses yet more challenges. Conventional search techniques are unable to cross search between archaeological datasets and Web-based grey literature. Results are reported from two AHRC funded research projects that investigated the use of semantic techniques to link digital archive databases, vocabularies and associated grey literature. STAR (Semantic Technologies for Archaeological Resources) was a collaboration between the University of Glamorgan, Hypermedia Research Unit and English Heritage (EH). The main outcome is a research Demonstrator (available online), which cross searches over excavation datasets from different database schemas, including Raunds Roman, Raunds Prehistoric, Museum of London, Silchester Roman and Stanwick sampling. The system additionally cross searches over an extract of excavation reports from the OASIS index of grey literature, operated by the Archaeology Data Service (ADS). A conceptual framework provided by the CIDOC Conceptual Reference Model(CRM) integrates the different database structures and the metadata automatically generated from the OASIS reports by natural language processing techniques. The methods employed for extracting semantic RDF representations from the datasets and the information extraction from grey literature are described. The STELLAR project provides freely available tools to reduce the costs of mapping and extracting data to semantic search systems such as the Demonstrator and to linked data representation generally. Detailed use scenarios (and a screen capture video) provide a basis for a discussion of key issues, including cost-benefits, ontology modelling, mapping, terminology control, semantic implementation and information extraction issues. The scenarios show that semantic interoperability can be achieved by mapping and extracting different datasets and key concepts from OASIS reports to a central RDF based triple store. It is not necessary to expose the full detail of the ontological model; the Demonstrator shows that user interfaces for retrieval (or mapping) systems can be expressed using familiar archaeological concepts. Working with the CRM-EH archaeological extension of the CIDOC CRM ontology allows specific archaeological queries, while permitting interoperability at the more general CRM level, potentially extending to other areas of cultural heritage. The ability to connect published datasets with the hitherto under-utilised grey literature holds potential for meta studies, where aggregate patterns can be compared and hypotheses for future detailed investigation uncovered. Connecting the interpretation with the underlying context data via the semantic model facilitates the revisiting of previous interpretations by third parties, the possibility of juxtaposing parallel interpretations, or exposing the data to new research questions.

Layered RDF: An object-oriented approach to web information representation

Based on XML, RDF(S) has been recommended as the basic layer of the Semantic Web, providing a foundation for the higher layers of ontology, logic and trust. Although RDF(S) enables specification and identification of data and relationships between data, it has some limitations as a foundational language of the Semantic Web. Firstly, the unconstrained framework of the RDF Schema makes it hard to share information from different sources. Furthermore, RDF(S) has limited expres-siveness as a vocabulary description language, which is partly due to insufficient object-oriented features. Finally, the unclear semantics of RDF(S), caused by language constructs with dual roles, impede any approach to provide reasoning support for the language. To overcome some of the limitations, this paper presents Layered RDF, a web information representation language based on RDF(S). Layered RDF distinguishes schema information from instance information by having separated layers. Furthermore, Layered RDF provides more powerful schema mechanisms than does RDF Schema, by incorporating object-oriented features such as inheritance and polymorphism.

ActiveRDF: Embedding Semantic Web data into object-oriented languages

Semantic Web applications share a large portion of development effort with database-driven Web applications. Existing approaches for development of these database-driven applications cannot be directly applied to Semantic Web data due to differences in the underlying data model. We develop a mapping approach that embeds Semantic Web data into object-oriented languages and thereby enables reuse of existing Web application frameworks. We analyse the relation between the Semantic Web and the Web, and survey the typical data access patterns in Semantic Web applications. We discuss the mismatch between object-oriented programming languages and Semantic Web data, for example in the semantics of class membership, inheritance relations, and object conformance to schemas. We present ActiveRDF, an object-oriented API for managing RDF data that offers full manipulation and querying of RDF data, does not rely on a schema and fully conforms to RDF(S) semantics. ActiveRDF can be used with different RDF data stores: adapters have been implemented to generic SPARQL endpoints, Sesame, Jena, Redland and YARS and new adapters can be added easily. We demonstrate the usage of ActiveRDF and its integration with the popular Ruby on Rails framework which enables rapid development of Semantic Web applications.

Extended RDF as a Semantic Foundation of Rule Markup Languages

Ontologies and automated reasoning are the building blocks of the Semantic Web initiative. Derivation rules can be included in an ontology to define derived concepts, based on base concepts. For example, rules allow to define the extension of a class or property, based on a complex relation between the extensions of the same or other classes and properties. On the other hand, the inclusion of negative information both in the form of negation-as-failure and explicit negative information is also needed to enable various forms of reasoning. In this paper, we extend RDF graphs with weak and strong negation, as well as derivation rules. The ERDF stable model semantics of the extended framework (Extended RDF) is defined, extending RDF(S) semantics. A distinctive feature of our theory, which is based on Partial Logic, is that both truth and falsity extensions of properties and classes are considered, allowing for truth value gaps. Our framework supports both closed-world and open-world reasoning through the explicit representation of the particular closed-world assumptions and the ERDF ontological categories of total properties and total classes.

ActiveRDF: Embedding Semantic Web Data Into Object-Oriented Languages

SemanticWeb applications share a large portion of development effort with database-drivenWeb applications. Existing approaches for development of these database-driven applications cannot be directly applied to Semantic Web data due to differences in the underlying data model. We develop a mapping approach that embeds Semantic Web data into object-oriented languages and thereby enables reuse of existingWeb application frameworks. We analyse the relation between the Semantic Web and the Web, and survey the typical data access patterns in Semantic Web applications. We discuss the mismatch between object-oriented programming languages and Semantic Web data, for example in the semantics of class membership, inheritance relations, and object conformance to schemas. We present ActiveRDF, an object-oriented API for managing RDF data that offers full manipulation and querying of RDF data, does not rely on a schema and fully conforms to RDF(S) semantics. ActiveRDF can be used with different RDF data stores: adapters have been implemented to generic SPARQL endpoints, Sesame, Jena, Redland and YARS and new adapters can be added easily. We demonstrate the usage of ActiveRDF and its integration with the popular Ruby on Rails framework which enables rapid development of SemanticWeb applications.

Interpreting SWRL Rules in RDF Graphs

An unresolved issue in SWRL (the Semantic Web Rule Language) is whether the intended semantics of its RDF representation can be described as an extension of the W3C RDF semantics. In this paper we propose to make the model-theoretic semantics of SWRL compatible with RDF by interpreting SWRL rules in RDF graphs. For dealing with SWRL/RDF rules, we regard ‘Implies’ as an OWL class, and extract all ‘Implies’ rules from an RDF database that represents a SWRL knowledge base. Each ‘Implies’ rule is grounded through mappings built into the semantic conditions of the model theory. Based on the fixpoint semantics, a bottom-up strategy is employed to compute the least Herbrand models.