9 research outputs found
Modelling Knowledge about Software Processes using Provenance Graphs and its Application to Git-based Version Control Systems
Using the W3C PROV data model, we present a general provenance model for software development processes and, as an example, specialized models for git services, for which we generate provenance graphs. Provenance graphs are knowledge graphs, since they have defined semantics, and can be analyzed with graph algorithms or semantic reasoning to get insights into processes
RiMOM Results for OAEI 2015
Abstract. This paper presents the results of RiMOM in the Ontology Alignment Evaluation Initiative (OAEI) 2015. We only participated in Instance Matching@OAEI2015. We first describe the overall framework of our matching System (RiMOM); then we detail the techniques used in the framework for instance matching. Last, we give a thorough analysis on our results and discuss some future work on RiMOM
Static Analysis of Graph Database Transformations
We investigate graph transformations, defined using Datalog-like rules based
on acyclic conjunctive two-way regular path queries (acyclic C2RPQs), and we
study two fundamental static analysis problems: type checking and equivalence
of transformations in the presence of graph schemas. Additionally, we
investigate the problem of target schema elicitation, which aims to construct a
schema that closely captures all outputs of a transformation over graphs
conforming to the input schema. We show all these problems are in EXPTIME by
reducing them to C2RPQ containment modulo schema; we also provide matching
lower bounds. We use cycle reversing to reduce query containment to the problem
of unrestricted (finite or infinite) satisfiability of C2RPQs modulo a theory
expressed in a description logic
Provenienz-basierte Prozessanalyse von GitLab-Projekten am Beispiel der DLR-Software BACARDI
Ziel dieser Bachelorarbeit war es einen Ansatz zu entwickeln, der es ermöglicht die Entwicklungsprozesse von GitLab Projekten basierend auf den Provenienzgraphen der Projekte zu untersuchen
Robust Entity Linking in Heterogeneous Domains
Entity Linking is the task of mapping terms in arbitrary documents to entities in a knowledge base by identifying the correct semantic meaning. It is applied in the extraction of structured data in RDF (Resource Description Framework) from textual documents, but equally so in facilitating artificial intelligence applications, such as Semantic Search, Reasoning and Question and Answering. Most existing Entity Linking systems were optimized for specific domains (e.g., general domain, biomedical domain), knowledge base types (e.g., DBpedia, Wikipedia), or document structures (e.g., tables) and types (e.g., news articles, tweets). This led to very specialized systems that lack robustness and are only applicable for very specific tasks. In this regard, this work focuses on the research and development of a robust Entity Linking system in terms of domains, knowledge base types, and document structures and types.
To create a robust Entity Linking system, we first analyze the following three crucial components of an Entity Linking algorithm in terms of robustness criteria: (i) the underlying knowledge base, (ii) the entity relatedness measure, and (iii) the textual context matching technique. Based on the analyzed components, our scientific contributions are three-fold. First, we show that a federated approach leveraging knowledge from various knowledge base types can significantly improve robustness in Entity Linking systems. Second, we propose a new state-of-the-art, robust entity relatedness measure for topical coherence computation based on semantic entity embeddings. Third, we present the neural-network-based approach Doc2Vec as a textual context matching technique for robust Entity Linking.
Based on our previous findings and outcomes, our main contribution in this work is DoSeR (Disambiguation of Semantic Resources). DoSeR is a robust, knowledge-base-agnostic Entity Linking framework that extracts relevant entity information from multiple knowledge bases in a fully automatic way. The integrated algorithm represents a collective, graph-based approach that utilizes semantic entity and document embeddings for entity relatedness and textual context matching computation. Our evaluation shows, that DoSeR achieves state-of-the-art results over a wide range of different document structures (e.g., tables), document types (e.g., news documents) and domains (e.g., general domain, biomedical domain). In this context, DoSeR outperforms all other (publicly available) Entity Linking algorithms on most data sets
Proceedings of The Tenth International Workshop on Ontology Matching (OM-2015)
shvaiko2016aInternational audienceno abstrac