Dörthe Arndt

The MASSIF platform: a modular and semantic platform for the development of flexible IoT services

In the Internet of Things (IoT), data-producing entities sense their environment and transmit these observations to a data processing platform for further analysis. Applications can have a notion of context awareness by combining this sensed data, or by processing the combined data. The processes of combining data can consist both of merging the dynamic sensed data, as well as fusing the sensed data with background and historical data. Semantics can aid in this task, as they have proven their use in data integration, knowledge exchange and reasoning. Semantic services performing reasoning on the integrated sensed data, combined with background knowledge, such as profile data, allow extracting useful information and support intelligent decision making. However, advanced reasoning on the combination of this sensed data and background knowledge is still hard to achieve. Furthermore, the collaboration between semantic services allows to reach complex decisions. The dynamic composition of such collaborative workflows that can adapt to the current context, has not received much attention yet. In this paper, we present MASSIF, a data-driven platform for the semantic annotation of and reasoning on IoT data. It allows the integration of multiple modular reasoning services that can collaborate in a flexible manner to facilitate complex decision-making processes. Data-driven workflows are enabled by letting services specify the data they would like to consume. After thorough processing, these services can decide to share their decisions with other consumers. By defining the data these services would like to consume, they can operate on a subset of data, improving reasoning efficiency. Furthermore, each of these services can integrate the consumed data with background knowledge in its own context model, for rapid intelligent decision making. To show the strengths of the platform, two use cases are detailed and thoroughly evaluated.

A Distance-Based Approach for Semantic Dissimilarity in Knowledge Graphs

In this paper, we introduce a distance-based approach for measuring the semantic dissimilarity between two concepts in a knowledge graph. The proposed Normalized Semantic Web Distance (NSWD) extends the idea of the Normalized Web Distance, which is utilized to determine the dissimilarity between two textural terms, and utilizes additional semantic properties of nodes in a knowledge graph. We evaluate our proposal on the knowledge graph Freebase, where the NSWD achieves a correlation of up to 0.58 with human similarity assessments on the established Miller-Charles benchmark of 30 term-pairs. These preliminary results indicate that the proposed NSWD is a promising approach for assessing semantic dissimilarity in very large knowledge graphs.

Ontology Reasoning using Rules in an eHealth Context

Traditionally, nurse call systems in hospitals are rather simple: patients have a button next to their bed to call a nurse. Which specific nurse is called cannot be controlled, as there is no extra information available. This is different for solutions based on semantic knowledge: if the state of care givers (busy or free), their current position, and for example their skills are known, a system can always choose the best suitable nurse for a call. In this paper we describe such a semantic nurse call system implemented using the EYE reasoner and Notation3 rules. The system is able to perform OWL-RL reasoning. Additionally, we use rules to implement complex decision trees. We compare our solution to an implementation using OWL-DL, the Pellet reasoner, and SPARQL queries. We show that our purely rule-based approach gives promising results. Further improvements will lead to a mature product which will significantly change the organization of modern hospitals.

Improving OWL RL Reasoning in N3 by Using Specialized Rules

Semantic Web reasoning can be a complex task: depending on the amount of data and the ontologies involved, traditional OWL DL reasoners can be too slow to face problems in real time. An alternative is to use a rule-based reasoner together with the OWL RL/RDF rules as stated in the specification of the OWL 2 language profiles. In most cases this approach actually improves reasoning times, but due to the complexity of the rules, not as much as it could. In this paper we present an improved strategy: based on the TBoxes of the ontologies involved in a reasoning task, we create more specific rules which then can be used for further reasoning. We make use of the EYE reasoner and its logic Notation3. In this logic, rules can be employed to derive new rules which makes the rule creation a reasoning step on its own. We evaluate our implementation on a semantic nurse call system. Our results show that adding a pre-reasoning step to produce specialized rules improves reasoning times by around 75i¾?%.

Using rule-based reasoning for RDF validation

The success of the Semantic Web highly depends on its ingredients. If we want to fully realize the vision of a machine-readable Web, it is crucial that Linked Data are actually useful for machines consuming them. On this background it is not surprising that (Linked) Data validation is an ongoing research topic in the community. However, most approaches so far either do not consider reasoning, and thereby miss the chance of detecting implicit constraint violations, or they base themselves on a combination of different formalisms, e.g. Description Logics combined with SPARQL. In this paper, we propose using Rule-Based Web Logics for RDF validation focusing on the concepts needed to support the most common validation constraints, such as Scoped Negation As Failure (SNAF), and the predicates defined in the Rule Interchange Format (RIF). We prove the feasibility of the approach by providing an implementation in Notation3 Logic. As such, we show that rule logic can cover both validation and reasoning if it is expressive enough.

Normalized Semantic Web Distance

In this paper, we investigate the Normalized Semantic Web Distance NSWD, a semantics-aware distance measure between two concepts in a knowledge graph. Our measure advances the Normalized Web Distance, a recently established distance between two textual terms, to be more semantically aware. In addition to the theoretic fundamentals of the NSWD, we investigate its properties and qualities with respect to computation and implementation. We investigate three variants of the NSWD that make use of all semantic properties of nodes in a knowledge graph. Our performance evaluation based on the Miller-Charles benchmark shows that the NSWD is able to correlate with human similarity assessments on both Freebase and DBpedia knowledge graphs with values upi¾źto 0.69. Moreover, we verified the semantic awareness of the NSWD on a set of 20 unambiguous concept-pairs. We conclude that the NSWD is a promising measure with 1 a reusable implementation across knowledge graphs, 2 sufficient correlation with human assessments, and 3i¾źawareness of semantic differences between ambiguous concepts.

Semantics of Notation3 Logic: A Solution for Implicit Quantification

Since the development of Notation3 Logic, several years have passed in which the theory has been refined and used in practice by different reasoning engines such as cwm, FuXi or EYE. Nevertheless, a clear model-theoretic definition of its semantics is still missing. This leaves room for individual interpretations and renders it difficult to make clear statements about its relation to other logics such as DL or FOL or even about such basic concepts as correctness. In this paper we address one of the main open challenges: the formalization of implicit quantification. We point out how the interpretation of implicit quantifiers differs in two of the above mentioned reasoning engines and how the specification, proposed in the W3C team submission, could be formalized. Our formalization is then put into context by integrating it into a model-theoretic definition of the whole language. We finish our contribution by arguing why universal quantification should be handled differently than currently prescribed.

SENSdesc: Connect Sensor Queries and Context

Modern developments confront us with an ever increasing amount of streaming data: different sensors in environments like hospitals or factories communicate their measurements to other applications. Having this data at disposal faces us with a new challenge: the data needs to be integrated to existing frameworks. As the availability of sensors can rapidly change, these need to be flexible enough to easily incorporate new systems without having to be explicitly configured. Semantic Web applications offer a solution for that enabling computers to ‘understand’ data. But for them the pure amount of data and different possible queries which can be performed on it can form an obstacle. This paper tackles this problem: we present a formalism to describe stream queries in the ontology context in which they might become relevant. These descriptions enable us to automatically decide based on the actual setting and the problem to be solved which and how sensors should be monitored further. This helps us to limit the streaming data taken into account for reasoning tasks and make stream reasoning more performant. We illustrate our approach on a health-care use case where different sensors are used to measure data on patients and their surrounding in a hospital.