Maria-Esther Vidal

GADES: A Graph-based Semantic Similarity Measure

Knowledge graphs encode semantics that describes resources in terms of several aspects, e.g., neighbors, class hierarchies, or node degrees. Assessing relatedness of knowledge graph entities is crucial for several data-driven tasks, e.g., ranking, clustering, or link discovery. However, existing similarity measures consider aspects in isolation when determining entity relatedness. We address the problem of similarity assessment between knowledge graph entities, and devise GADES. GADES relies on aspect similarities and computes a similarity measure as the combination of these similarity values. We empirically evaluate the accuracy of GADES on knowledge graphs from different domains, e.g., proteins, and news. Experiment results indicate that GADES exhibits higher correlation with gold standards than studied existing approaches. Thus, these results suggest that similarity measures should not consider aspects in isolation, but combinations of them to precisely determine relatedness.

FuhSen: A Federated Hybrid Search Engine for Building a Knowledge Graph On-Demand (Short Paper)

A vast amount of information about various types of entities is spread across the Web, e.g., people or organizations on the Social Web, product offers on the Deep Web or on the Dark Web. These data sources can comprise heterogeneous data and are equipped with different search capabilities e.g., Search API. End users such as investigators from law enforcement institutions searching for traces and connections of organized crime have to deal with these interoperability problems not only during search time but also while merging data collected from different sources. We devise FuhSen, a keyword-based federated engine that exploits the search capabilities of heterogeneous sources during query processing and generates knowledge graphs on-demand applying an RDF-Molecule integration approach in response to keyword-based queries. The resulting knowledge graph describes the semantics of entities collected from the integrated sources, as well as relationships among these entities. Furthermore, FuhSen utilizes ontologies to describe the available sources in terms of content and search capabilities and exploits this knowledge to select the sources relevant for answering a keyword-based query. We conducted a user evaluation where FuhSen is compared to traditional search engines. FuhSen semantic search capabilities allow users to complete search tasks that could not be accomplished with traditional Web search engines during the evaluation study.

Towards an Integrated Graph Algebra for Graph Pattern Matching with Gremlin

Graph data management has revealed beneficial characteristics in terms of flexibility and scalability by differently balancing between query expressivity and schema flexibility. This has resulted into an rapid developing new task specific graph systems, query languages and data models, such as property graphs, key-value, wide column, resource description framework (RDF), etc. Present day graph query languages are focused towards flexible graph pattern matching (aka sub-graph matching), where as graph computing frameworks aim towards providing fast parallel (distributed) execution of instructions. The consequence of this rapid growth in the variety of graph based data management systems has resulted in a lack of standardization. Gremlin, a graph traversal language and machine, provides a common platform for supporting any graph computing system (such as an OLTP graph database or OLAP graph processors). We present a formalization of graph pattern matching for Gremlin queries. We also study, discuss and consolidate various existing graph algebra operators into an integrated graph algebra.

Alligator: A Deductive Approach for the Integration of Industry 4.0 Standards

Industry 4.0 standards, such as AutomationML, are used to specify properties of mechatronic elements in terms of views, such as electrical and mechanical views of a motor engine. These views have to be integrated in order to obtain a complete model of the artifact. Currently, the integration requires user knowledge to manually identify elements in the views that refer to the same element in the integrated model. Existing approaches are not able to scale upi¾?to large models where a potentially large number of conflicts may exist across the different views of an element. To overcome this limitation, we developed Alligator, a deductive rule-based system able to identify conflicts between AutomationML documents. We define a Datalog-based representation of the AutomationML input documents, and a set of rules for identifying conflicts. A deductive engine is used to resolve the conflicts, to merge the input documents and produce an integrated AutomationML document. Our empirical evaluation of the quality of Alligator against a benchmark of AutomationML documents suggest that Alligator accurately identifies various types of conflicts between AutomationML documents, and thus helps increasing the scalability, efficiency, and coherence of models for Industry 4.0 manufacturing environments.

QAestro – Semantic-Based Composition of Question Answering Pipelines

The demand for interfaces that allow users to interact with computers in an intuitive, effective, and efficient way is increasing. Question Answering (QA) systems address this need by answering questions posed by humans using knowledge bases. In recent years, many QA systems and related components have been developed both by practitioners and the research community. Since QA involves a vast number of (partially overlapping) subtasks, existing QA components can be combined in various ways to build tailored QA systems that perform better in terms of scalability and accuracy in specific domains and use cases. However, to the best of our knowledge, no systematic way exists to formally describe and automatically compose such components. Thus, in this work, we introduce QAestro, a framework for semantically describing both QA components and developer requirements for QA component composition. QAestro relies on a controlled vocabulary and the Local-as-View (LAV) approach to model QA tasks and components, respectively. Furthermore, the problem of QA component composition is mapped to the problem of LAV query rewriting, and state-of-the-art SAT solvers are utilized to efficiently enumerate the solutions. We have formalized 51 existing QA components implemented in 20 QA systems using QAestro. Our empirical results suggest that QAestro enumerates the combinations of QA components that effectively implement QA developer requirements.

Co-evolution of RDF Datasets

Linking Data initiatives have fostered the publication of large number of RDF datasets in the Linked Open Data (LOD) cloud, as well as the development of query processing infrastructures to access these data in a federated fashion. However, different experimental studies have shown that availability of LOD datasets cannot be always ensured, being RDF data replication required for envisioning reliable federated query frameworks. Albeit enhancing data availability, RDF data replication requires synchronization and conflict resolution when replicas and source datasets are allowed to change data over time, i.e., co-evolution management needs to be provided to ensure consistency. In this paper, we tackle the problem of RDF data co-evolution and devise an approach for conflict resolution during co-evolution of RDF datasets. Our proposed approach is property-oriented and allows for exploiting semantics about RDF properties during co-evolution management. The quality of our approach is empirically evaluated in different scenarios on the DBpedia-live dataset. Experimental results suggest that proposed proposed techniques have a positive impact on the quality of data in source datasets and replicas.

Proactive Prevention of False-Positive Conflicts in Distributed Ontology Development

A Version Control System (VCS) is usually required for successful ontology development in distributed settings. VCSs enable the tracking and propagation of ontology changes, as well as collecting metadata to describe changes, e.g., who made a change at which point in time. Modern VCSs implement an optimistic approach that allows for simultaneous changes of the same artifact and provides mechanisms for automatic as well as manual conflict resolution. However, different ontology development tools serialize the ontology artifacts in different ways. As a consequence, existing VCSs may identify a huge number of false-positive conflicts during the merging process, i.e., conflicts that do not result from ontology changes but the fact that two ontology versions are differently serialized. Following the principle of prevention is better than cure, we designed SerVCS, an approach that enhances VCSs to cope with different serializations of the same ontology. SerVCS is based on a unique serialization of ontologies to reduce the number of false-positive conflicts produced whenever different serializations of the same ontology are compared. We implemented SerVCS on top of Git, utilizing tools such as Rapper and Rdf-toolkit for syntax validation and unique serialization, respectively. We have conducted an empirical evaluation to determine the conflict detection accuracy of SerVCS whenever simultaneous changes to an ontology are performed using different ontology editors. The evaluation results suggest that SerVCS empowers VCSs by preventing them from wrongly identifying serialization related conflicts.

Classifying Data Heterogeneity within Budget and Spending Open Data

Open data has gained momentum for the past few years, but not much consumption was done over published open budget and spending datasets. Many challenges to consume open budget and spending data are still open. One of the challenges is the heterogeneity of these datasets. We analyze more than 75 different budget and spending datasets released by different public administrations from various levels of administrations and locations. We select five datasets, then present and illustrate several types of budget and spending heterogeneities. We compare these heterogeneities with state of the art fiscal data models, the OpenBudgets.eu (OBEU) data model and Fiscal Data Package (FDP) which are designed specifically for representing budget and spending datasets. The comparison provides hints for both datasets publishers and technical/research communities that deal with open data in budget and spending domain.

Diefficiency Metrics: Measuring the Continuous Efficiency of Query Processing Approaches

During empirical evaluations of query processing techniques, metrics like execution time, time for the first answer, and throughput are usually reported. Albeit informative, these metrics are unable to quantify and evaluate the efficiency of a query engine over a certain time period – or diefficiency –, thus hampering the distinction of cutting-edge engines able to exhibit high-performance gradually. We tackle this issue and devise two experimental metrics named dief@t and dief@k, which allow for measuring the diefficiency during an elapsed time period t or while k answers are produced, respectively. The dief@t and dief@k measurement methods rely on the computation of the area under the curve of answer traces, and thus capturing the answer concentration over a time interval. We report experimental results of evaluating the behavior of a generic SPARQL query engine using both metrics. Observed results suggest that dief@t and dief@k are able to measure the performance of SPARQL query engines based on both the amount of answers produced by an engine and the time required to generate these answers.

Integration of Scholarly Communication Metadata Using Knowledge Graphs

Important questions about the scientific community, e.g., what authors are the experts in a certain field, or are actively engaged in international collaborations, can be answered using publicly available datasets. However, data required to answer such questions is often scattered over multiple isolated datasets. Recently, the Knowledge Graph (KG) concept has been identified as a means for interweaving heterogeneous datasets and enhancing answer completeness and soundness. We present a pipeline for creating high quality knowledge graphs that comprise data collected from multiple isolated structured datasets. As proof of concept, we illustrate the different steps in the construction of a knowledge graph in the domain of scholarly communication metadata (SCM-KG). Particularly, we demonstrate the benefits of exploiting semantic web technology to reconcile data about authors, papers, and conferences. We conducted an experimental study on an SCM-KG that merges scientific research metadata from the DBLP bibliographic source and the Microsoft Academic Graph. The observed results provide evidence that queries are processed more effectively on top of the SCM-KG than over the isolated datasets, while execution time is not negatively affected.