Dataset search is a special application of information retrieval, which aims to help scientists with finding the datasets they want. Current dataset search engines are query-driven, which implies that the results are limited by the ability of the user to formulate the appropriate query. In this paper we aim to solve this limitation by framing dataset search as a recommendation task: given a dataset by the user, the search engine recommends similar datasets. We solve this dataset recommendation task using a similarity approach. We provide a simple benchmark task to evaluate different approaches for this dataset recommendation task. We also evaluate the recommendation task with several similarity approaches in the biomedical domain. We benchmark 8 different similarity metrics between datasets, including both ontology-based techniques and techniques from machine learning. Our results show that the task of recommending scientific datasets based on meta-data as it occurs in realistic dataset collections is a hard task. None of the ontology-based methods manage to perform well on this task, and are outscored by the majority of the machine-learning methods. Of these ML methods only one of the approaches performs reasonably well, and even then only reaches 70% accuracy.

Entity disambiguation is a widely investigated topic, and many matching algorithms have been proposed. However, this task has not yet been satisfactorily addressed when the domain of interest provides poor or incomplete data with little discriminating power. In these cases, the use of content fields such as name and date is not enough and the simple use of relations with other entities is not of much help when these related entities also need disambiguation before they can be used. Therefore, we propose an approach for the disambiguation of clustered resources using context (related entities that are also clustered) as evidence for reconciling matched entities. We test the proposed method on datasets of historical records from Amsterdam in the 17th century for which context is available, and we compare the results of the proposed approach to a gold standard generated by three experts, which we make available online. The results show that the proposed approach manages to meaningfully use context for isolating identity sub-clusters with higher quality by eliminating potentially false positive matches.

Language systems have been of great interest to the research community and have recently reached the mass market through various assistant platforms on the web. Reinforcement Learning methods that optimize dialogue policies have seen successes in past years and have recently been extended into methods that personalize the dialogue, e.g. take the personal context of users into account. These works, however, are limited to personalization to a single user with whom they require multiple interactions and do not generalize the usage of context across users. This work introduces a problem where a generalized usage of context is relevant and proposes two Reinforcement Learning (RL)-based approaches to this problem. The first approach uses a single learner and extends the traditional POMDP formulation of dialogue state with features that describe the user context. The second approach segments users by context and then employs a learner per context. We compare these approaches in a benchmark of existing non-RL and RL-based methods in three established and one novel application domain of financial product recommendation. We compare the influence of context and training experiences on performance and find that learning approaches generally outperform a handcrafted gold standard.

Linking between entities in different datasets is a crucial element of the Semantic Web architecture, since those links allow us to integrate datasets without having to agree on a uniform vocabulary. However, it is widely acknowledged that the owl:sameAs construct is too blunt a tool for this purpose. It entails full equality between two resources independent of context. But whether or not two resources should be considered equal depends not only on their intrinsic properties, but also on the purpose or task for which the resources are used. We present a system for constructing context-specific equality links. In a first step, our system generates a set of probable links between two given datasets. These potential links are decorated with rich metadata describing how, why, when and by whom they were generated. In a second step, a user then selects the links which are suited for the current task and context, constructing a context-specific "Lenticular Lens". Such lenses can be combined using operators such as union, intersection, difference and composition. We illustrate and validate our approach with a realistic application that supports researchers in social science.

In this paper we present a framework for answering queries over inconsistent ontologies by using contrastive reasoning, the reasoning of contrasts which are expressed as contrary conjunctions like the word "but" in natural language. We argue that contrastive answers are more informative for reasoning with inconsistent ontologies, as compared with the usual simple boolean answer, i.e., either "yes" or "no". We propose a general framework for contrastive reasoning with inconsistent ontologies. The proposed approach has been implemented in the system CRION (Contrastive Reasoning with Inconsistent Ontologies) as a reasoning plug-in in the LarKC (Large Knowledge Collider) platform. We report several experiments in which we apply the CRION system to some realistic ontologies. This evaluation shows that contrastive reasoning is a useful extension to the existing approaches of reasoning with inconsistent ontologies.

Semantic Web data exhibits very skewed frequency distributions among terms. Efficient large-scale distributed reasoning methods should maintain load-balance in the face of such highly skewed distribution of input data. We show that term-based partitioning, used by most distributed reasoning approaches, has limited scalability due to load-balancing problems.We address this problem with a method for data distribution based on clustering in elastic regions. Instead of as- signing data to fixed peers, data flows semi-randomly in the network. Data items speed-date while being temporarily collocated in the same peer. We introduce a bias in the routing to allow semantically clustered neighborhoods to emerge. Our approach is self-organising, efficient and does not require any central coordination.We have implemented this method on the MaRVIN platform and have performed experiments on large real-world datasets, using a cluster of up to 64 nodes. We compute the RDFS closure over different datasets and show that our clustering algorithm drastically reduces computation time, calculating the RDFS closure of 200 million triples in 7.2 minutes.

The extensive work on Knowledge Engineering in the 1990s has resulted in a systematic analysis of task-types, and the corresponding problem solving methods that can be deployed for different types of tasks. That analysis was the basis for a sound and widely accepted methodology for building knowledge-based systems, and has made it is possible to build libraries of reusable models, methods and code.In this paper, we make a first attempt at a similar analysis for Semantic Web applications. We will show that it is possible to identify a relatively small number of task-types, and that, somewhat surprisingly, a large set of Semantic Web applications can be described in this typology. Secondly, we show that it is possible to decompose these task-types into a small number of primitive (atomic) inference steps. We give semi-formal definitions for both the task-types and the primitive inference steps that we identify. We substantiate our claim that our task-types are sufficient to cover the vast majority of Semantic Web applications by showing that all entries of the Semantic Web Challenges of the last 3 years can be classified in these task-types.

Discovering mappings between concept hierarchies is widely regarded as one of the hardest and most urgent problems facing the Semantic Web. The problem is even harder in domains where concepts are inherently vague and ill-defined, and cannot be given a crisp definition. A notion of approximate concept mapping is required in such domains, but until now, no such notion is vailable.

It is a truism that as the Web grows in size and scope, it becomes harder to find what we want, to identify like-minded people and communities, to find the best ads to offer, and to have applications work together smoothly. Services don't interoperate; queries yield long lists of results, most of which seem to miss the point. If the Web were a person, we would expect richer and more successful interactions with it - interactions that were, quite literally, more meaningful. That's because in human discourse, it is shared meaning that gives us real communication. Yet with the current Web, meaning cannot be found.Much recent work has aspired to change this, both for human-machine interchange and machine-machine synchronization. Certainly the "semantic web" looks to add meaning to our current simplistic matching of mere strings of characters against mere "bags" of words. But can we legislate meaning from on high? Isn't meaning organic and determined by use, a moving and context-dependent target? But if meaning is an evolving organic soup, how are humans able to get anything done with one another? Don't we love to "define our terms"? But then again, is real definition even possible?These questions have daunted philosophers for years, and we probably won't solve them here. But we'll try to understand what's at the root of our own current religious debate: should meaning on the Web be evolutionary, driven organically through the bottom-up human assignment of tags? Or does it need to be carefully crafted and managed by a higher authority, using structured representations with defined semantics? Without picket signs or violence (we hope), our panelists will explore the two extreme ends of the spectrum - and several points in between.

Content-related metadata plays an important role in intelligent information systems. Especially on the world-wide web meaningful metadata describing the contents of a web-site is the key to intelligent retrieval and access of information. Metadata description standards like RDF and RDF schema have been developed and work in progress addresses the use of ontologies to provide a logical foundation for metadata. However, the acquisition of appropriate metadata is still a problem. The main part of the paper is concerned with the specification of ontologies and metadata models. We describe the Spectacle approach, a knowledge-based approach for metadata validation and generation as well as tools related to the ontology language OIL. We conclude that the specification of ontologies and the generation of metadata models are processes that supplement each other and propose a method for semi-automatic generation of metadata models on the basis of ontologies.

Article Share on Enabling knowledge representation on the Web by extending RDF schema Authors: Jeen Broekstra Aidministrator Nederland bv, Holland Aidministrator Nederland bv, HollandView Profile , Michel Klein Vrije Universiteit Amsterdam, Holland Vrije Universiteit Amsterdam, HollandView Profile , Stefan Decker Department of Computer Science, Stanford University, Stanford Department of Computer Science, Stanford University, StanfordView Profile , Dieter Fensel Vrije Universiteit Amsterdam, Holland Vrije Universiteit Amsterdam, HollandView Profile , Frank van Harmelen Vrije Universiteit Amsterdam, Holland Vrije Universiteit Amsterdam, HollandView Profile , Ian Horrocks Department of Computer Science, University of Manchester, UK Department of Computer Science, University of Manchester, UKView Profile Authors Info & Claims WWW '01: Proceedings of the 10th international conference on World Wide WebMay 2001 Pages 467–478https://doi.org/10.1145/371920.372105Online:01 April 2001Publication History 50citation1,138DownloadsMetricsTotal Citations50Total Downloads1,138Last 12 Months20Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

A number of formal specification languages for knowledge-based systems has been developed. Characteristics for knowledge-based systems are a complex knowledge base and an inference engine which uses this knowledge to solve a given problem. Specification languages for knowledge-based systems have to cover both aspects. They have to provide the means to specify a complex and large amount of knowledge and they have to provide the means to specify the dynamic reasoning behavior of a knowledge-based system. We focus on the second aspect. For this purpose, we survey existing approaches for specifying dynamic behavior in related areas of research. In fact, we have taken approaches for the specification of information systems (Language for Conceptual Modeling and TROLL), approaches for the specification of database updates and logic programming (Transaction Logic and Dynamic Database Logic) and the generic specification framework of abstract state machines.