Mike Dean

An evaluation of triple-store technologies for large data stores

This paper presents a comparison of performance of various triple-store technologies currently in either production release or beta test. Our comparison of triple-store technologies is biased toward a deployment scenario where the triple-store needs to load data and respond to queries over a very large knowledge base (on the order of hundreds of millions of triples.) The comparisons in this paper are based on the Lehigh University Benchmark (LUBM) software tools. We used the LUBM university ontology, datasets, and standard queries to perform our comparisons. We find that over our test regimen, the triple-stores based on the DAML DB and BigOWLIM technologies exhibit the best performance among the triple-stores tested.

A Geo-ontology Design Pattern for Semantic Trajectories

Trajectory data have been used in a variety of studies, including human behavior analysis, transportation management, and wildlife tracking. While each study area introduces a different perspective, they share the need to integrate positioning data with domain-specific information. Semantic annotations are necessary to improve discovery, reuse, and integration of trajectory data from different sources. Consequently, it would be beneficial if the common structure encountered in trajectory data could be annotated based on a shared vocabulary, abstracting from domain-specific aspects. Ontology design patterns are an increasingly popular approach to define such flexible and self-contained building blocks of annotations. They appear more suitable for the annotation of interdisciplinary, multi-thematic, and multi-perspective data than the use of foundational and domain ontologies alone. In this paper, we introduce such an ontology design pattern for semantic trajectories. It was developed as a community effort across multiple disciplines and in a data-driven fashion. We discuss the formalization of the pattern using the Web Ontology Language (OWL) and apply the pattern to two different scenarios, personal travel and wildlife monitoring.

Semantic Web Rules

In this paper we present the well founded variants of ambiguity blocking and ambiguity propagating defeasible logics. We also show how to extend SPINdle, a state of the art, defeasible logic implementation to handle all such variants of defeasible logic.

Application of ontology translation

An ontology provides a precise specification of the vocabulary used by a community of interest (COI). Multiple communities of interest may describe the same concept using the same or different terms. When such communities interact, ontology alignment and translation is required. This is typically a time consuming process. This paper describes Snoggle, an open source tool designed to ease development of ontology translation rules, and discusses its application to geospatial ontologies.

Semantic Web Rules: Covering the Use Cases

Rules represent the next step for the Semantic Web. A number of use cases for Semantic Web Rules have been formally and informally proposed, including ontology extension, ontology translation, data expansion, portable axiomatic semantics, matching, monitoring, and profile and process descriptions for Semantic Web Services. This talk will describe each of these use cases, provide examples, and assess the degree to which each is addressed by the Semantic Web Rule Language (SWRL) and other current alternatives.

Towards a theory of semantic communication

This paper studies methods of quantitatively measuring semantic information in communication. We review existing work on quantifying semantic information, then investigate a model-theoretical approach for semantic data compression and reliable semantic communication. We relate our approach to the statistical measurement of information by Shannon, and show that Shannons source and channel coding theorems have semantic counterparts.

Preserving quality of information by using semantic relationships

We show how semantic relationships that exist within an information-rich source can be exploited for achieving parsimonious communication between a pair of semantically-aware nodes that preserves quality of information. We extend the source coding theorem of classical information theory to encompass semantics in the source and show that by utilizing semantic relations between source symbols, higher rate of lossless compression may be achieved compared to traditional syntactic compression methods. We define the capacity of a semantic source as the mutual information between its models and syntactic messages, and show that it equals the average semantic entropy of its messages. We further show the duality of semantic redundancy and semantic ambiguity in compressing semantic data, and establish the semantic capacity of a source as the lower bound on semantic compression. Finally, we give a practical semantic compression algorithm that exploits the graph structure of a shared knowledge base to facilitate semantic communication between a pair of nodes.