Leveraging Semantics for Large-Scale Knowledge Graph Evaluation
Knowledge graphs (KG) are being used extensively in different industries for data driven applicat... more Knowledge graphs (KG) are being used extensively in different industries for data driven applications. These industrial knowledge graphs, due to their large scale and heterogeneity, are often constructed using automated information extraction (IE) toolkits. Owing to the diverse nature of the sources, such extractions are often noisy and contain many semantic inaccuracies. High quality, consistent KGs are critical to effective predictive analytics and decision support. For example, many commercial question answering systems rely heavily on accurate and consistent knowledge graphs generated from life sciences content. These systems typically require an extensible, scalable, and generalizable framework. To address these issues, we build on previous work in ontology and instance data evaluation and propose a method for Large-Scale Knowledge Graph Evaluation. The approach leverages domain ontologies to detect possible inconsistencies. We construct an RDF/RDFS knowledge graph from the output of a state-of-the-art biomedical IE system, ODIN, and demonstrate that it is easy to construct general inconsistency rules for quality control. In this paper we present our results after applying these rules to the KG and then discuss how our approach and implementation can generalize to many large scale industrial knowledge graphs.
Numerous RDF vocabularies and OWL, KIF, and other knowledge representation language ontologies ha... more Numerous RDF vocabularies and OWL, KIF, and other knowledge representation language ontologies have been contributed to the growing body of ontologies available in the public domain over the last ten years. Many of these were created with government-funded research support in the US and EU. Only a small subset is reusable, and fewer are appropriate for use in applications supporting evolving Intelligence Community requirements. This is partly due to decreasing funding available in the US in particular, but also because of lack of well-specified policies for vocabulary management, metadata, and provenance specification. In this paper we will highlight some of the challenges we have faced in developing and attempting to reuse ontologies in support of DARPA and US Department of Defense initiatives, and provide fodder for discussion of requirements for public domain ontologies.
A Representational Analysis of the API4KP Metamodel
Lecture notes in business information processing, 2015
The API for Knowledge Platforms (API4KP) provides a common abstraction interface for discovery, e... more The API for Knowledge Platforms (API4KP) provides a common abstraction interface for discovery, exploration of metadata and querying of different types of knowledge bases. It targets the basic administration services as well as the retrieval and the modification of expressions in machine-readable knowledge representation and reasoning (KRR) languages, such as RDF(S), OWL, RuleML and Common Logic, within heterogeneous and possibly distributed (multi-language, multi-nature) knowledge platforms. This paper introduces typical use cases for API4KP and, based on their ontological requirements, analyses the representational completeness and clarity of its ontological metamodel.
Proceedings of the second international joint conference on Autonomous agents and multiagent systems - AAMAS '03, 2003
This paper presents current research on a semantically rich, graphical representation of ontologi... more This paper presents current research on a semantically rich, graphical representation of ontologies and its utility for collaborative construction based on requirements outlined by the Agentcities initiative. A new tool, called the Visual Ontology Modeler, is described and evaluated in the context of Agentcities. Its distinguishing qualities include: ease of use, multiuser configuration management, integrated consistency and completeness checking, automated export of DAML+OIL code. The application domain is an open, dynamic test-bed for agent deployment; the ontologies are encoded in DAML+OIL and explicitly designed to be shared by several agent-based services within this environment.
API4KP (API for Knowledge Platforms) is a standard development effort that targets the basic admi... more API4KP (API for Knowledge Platforms) is a standard development effort that targets the basic administration services as well as the retrieval, modification and processing of expressions in machinereadable languages, including but not limited to knowledge representation and reasoning (KRR) languages, within heterogeneous (multilanguage, multi-nature) knowledge platforms. KRR languages of concern in this paper include but are not limited to RDF(S), OWL, RuleML and Common Logic, and the knowledge platforms may support one or several of these. Additional languages are integrated using mappings into KRR languages. A general notion of structure for knowledge sources is developed using monads. The presented API4KP metamodel, in the form of an OWL ontology, provides the foundation of an abstract syntax for communications about knowledge sources and environments, including a classification of knowledge source by mutability, structure, and an abstraction hierarchy as well as the use of performatives (inform, query, ...), languages, logics, dialects, formats and lineage. Finally, the metamodel provides a classification of operations on knowledge sources and environments which may be used for requests (message-passing).
This paper contains a taxonomy of the uses of ontologies, intended as motivation for the Ontology... more This paper contains a taxonomy of the uses of ontologies, intended as motivation for the Ontology Definition Metamodel development effort by the Object Management Group. It describes several usage scenarios for ontologies and proposes example applications for use in these scenarios. Many of the scenarios and applications are based on efforts currently underway in industry and academia. The scenarios descriptions are followed by goals for the Ontology Definition Metamodel.
As science informatics and e-Science blossom around the world, teams of collaborating researchers... more As science informatics and e-Science blossom around the world, teams of collaborating researchers are fi nding needs for next-generation cyberinfrastructure along with knowledge and tool support for data-intensive scientifi c research. M any geosciences researchers are taking advantage of the emergence of virtual repositories and observatories, such as those in astronomy, heliophysics, environmental science, hydrology, and solar-terrestrial physics, where distributed and often heterogeneous collections of scientifi c data are made available transparently. 2
Procede de mise en correspondance de contexte semantique pour permettre un interfonctionnement entre des sources disparates
Il est possible d'acceder a une collection repartie d'applications et de referentiels pre... more Il est possible d'acceder a une collection repartie d'applications et de referentiels presentant des caracteristiques syntactiques et semantiques dissemblables comme si elles ne constituaient qu'une seule entite. Des interrogations sont effectuees de maniere transparente sans qu'il ne soit necessaire a l'utilisateur de comprendre ou d'avoir connaissance des caracteristiques de n'importe quelle source individuelle. Ceci est execute par l'emploi d'un vocabulaire commun et de mises en correspondance semantique avec les referentiels de source. Un vocabulaire commun est utilise pour obtenir la transparence voulue. Des mises en correspondance etablissent la correspondance entre le vocabulaire commun et chacun des vocabulaires specifiques aux sources a integrer. Le vocabulaire commun peut etre adapte aux besoins d'un individu ou d'un groupe d'utilisateurs et/ou d'applications et il forme la base d'une resolution de conflits syntactiqu...
Application of Ontology-Based Knowledge Representation to Design Reuse
Ontolingua, a language for ontology-based knowledge representation, provides the capability to co... more Ontolingua, a language for ontology-based knowledge representation, provides the capability to construct comprehensive characterizations of knowledge bases. While the ability to characterize the content of a knowledge base is not new, Ontolingua includes a number of features that greatly enhance conventional data representation and modeling technologies through the incorporation of semantic context. In addition to supporting object-oriented modeling techniques, Ontolingua enables representation of constraints, definitions, and relationships among terms within ontologies. This facility provides a framework that supports automated translation among knowledge bases with differing data models and physical implementations. The ability to formally describe and unambiguously distinguish between diverse data sources is essential to enabling reuse of intellectual property. This paper presents a high-level view of ontology-based knowledge representation and an approach to solving the intellectual property reuse problem through the application of this technology.
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