On Web-scale Reasoning

Bal, H.E. [Promotor]Harmelen, F.A.H. van [Promotor

U-Pb Geochronology of detrital zircons from the Venezuelan passive margin : implications for an Early Cretaceous Proto-Orinoco river system and Proto-Caribbean ocean basin paleogeography

The Guyana Shield has long been interpreted as the source of siliciclastic detritus within the Cretaceous passive margin strata of northern Venezuela. We have determined U-Pb ages of detrital zircons separated from Early Cretaceous strata of the passive margin. Although the Guyana shield is the probable source for much of the Archean, Paleoproterozoic and early Mesoproterozoic detrital zircon grains, there is a prominent age population (ca.0.95-1.2Ga) that is not easily explained as being derived from the shield. A western source in the Venezuelan and/or northern Colombian Andes is suggested for this detrital component. We propose that a Proto-Orinoco river system drained both the Guyana Shield and the Venezuelan and Colombian Andes and that branches of this river system were funneled through Triassic/Jurassic rift basins that formed during initial opening of the Proto- Caribbean Seaway. The detrital zircon age data have implications for paleogeographic reconstructions of the Caribbean region prior to the breakup of Pangea and the longevity of continental scale river systems

Streaming the Web: Reasoning over dynamic data.

In the last few years a new research area, called stream reasoning, emerged to bridge the gap between reasoning and stream processing. While current reasoning approaches are designed to work on mainly static data, the Web is, on the other hand, extremely dynamic: information is frequently changed and updated, and new data is continuously generated from a huge number of sources, often at high rate. In other words, fresh information is constantly made available in the form of streams of new data and updates. Despite some promising investigations in the area, stream reasoning is still in its infancy, both from the perspective of models and theories development, and from the perspective of systems and tools design and implementation. The aim of this paper is threefold: (i) we identify the requirements coming from different application scenarios, and we isolate the problems they pose; (ii) we survey existing approaches and proposals in the area of stream reasoning, highlighting their strengths and limitations; (iii) we draw a research agenda to guide the future research and development of stream reasoning. In doing so, we also analyze related research fields to extract algorithms, models, techniques, and solutions that could be useful in the area of stream reasoning. © 2014 Elsevier B.V. All rights reserved

Extracting new knowledge from web tables: Novelty or confidence?

To extend the coverage of Knowledge Bases (KBs), it is useful to integrate factual information from public tabular data. Ideally, the extracted information should not only be correct, but also novel. So far, the evaluation of state-of-the-art techniques for this task has focused primarily on the correctness of the extractions, but the novelty is less well analysed. To fill this gap, we replicated the evaluation of two state-of-the-art techniques and analyse the amount of novel extractions using two new metrics. We observe that current techniques are biased towards confidence, but this comes at the expense of novelty. We sketch a possible solution for this problem as part of our ongoing research

Extracting Novel Facts from Tables for Knowledge Graph Completion (Extended version)

We propose a new end-to-end method for extending a Knowledge Graph (KG) from tables. Existing techniques tend to interpret tables by focusing on information that is already in the KG, and therefore tend to extract many redundant facts. Our method aims to find more novel facts. We introduce a new technique for table interpretation based on a scalable graphical model using entity similarities. Our method further disambiguates cell values using KG embeddings as additional ranking method. Other distinctive features are the lack of assumptions about the underlying KG and the enabling of a fine-grained tuning of the precision/recall trade-off of extracted facts. Our experiments show that our approach has a higher recall during the interpretation process than the state-of-the-art, and is more resistant against the bias observed in extracting mostly redundant facts since it produces more novel extractions

DynamiTE: Parallel Materialization of Dynamic RDF Data

One of the main advantages of using semantically annotated data is that machines can reason on it, deriving implicit knowledge from explicit information. In this context, materializing every possible implicit derivation from a given input can be computationally expensive, especially when considering large data volumes. Most of the solutions that address this problem rely on the assumption that the information is static, i.e., that it does not change, or changes very infrequently. However, the Web is extremely dynamic: online newspapers, blogs, social networks, etc., are frequently changed so that outdated information is removed and replaced with fresh data. This demands for a materialization that is not only scalable, but also reactive to changes. In this paper, we consider the problem of incremental materialization, that is, how to update the materialized derivations when new data is added or removed. To this purpose, we consider the ρdf RDFS fragment [12], and present a parallel system that implements a number of algorithms to quickly recalculate the derivation. In case new data is added, our system uses a parallel version of the well-known semi-naive evaluation of Datalog. In case of removals, we have implemented two algorithms, one based on previous theoretical work, and another one that is more efficient since it does not require a complete scan of the input. We have evaluated the performance using a prototype system called DynamiTE, which organizes the knowledge bases with a number of indices to facilitate the query process and exploits parallelism to improve the performance. The results show that our methods are indeed capable to recalculate the derivation in a short time, opening the door to reasoning on much more dynamic data than is currently possible. © 2013 Springer-Verlag

RDSZ: an approach for lossless RDF stream compression

In many applications (like social or sensor networks) the in- formation generated can be represented as a continuous stream of RDF items, where each item describes an application event (social network post, sensor measurement, etc). In this paper we focus on compressing RDF streams. In particular, we propose an approach for lossless RDF stream compression, named RDSZ (RDF Differential Stream compressor based on Zlib). This approach takes advantage of the structural similarities among items in a stream by combining a differential item encoding mechanism with the general purpose stream compressor Zlib. Empirical evaluation using several RDF stream datasets shows that this combi- nation produces gains in compression ratios with respect to using Zlib alone

Colony size predicts division of labour in Attine ants

Division of labour is central to the ecological success of eusocial insects, yet the evolutionary factors driving increases in complexity in division of labour are little known. The size–complexity hypothesis proposes that, as larger colonies evolve, both non-reproductive and reproductive division of labour become more complex as workers and queens act to maximize inclusive fitness. Using a statistically robust phylogenetic comparative analysis of social and environmental traits of species within the ant tribe Attini, we show that colony size is positively related to both non-reproductive (worker size variation) and reproductive (queen–worker dimorphism) division of labour. The results also suggested that colony size acts on non-reproductive and reproductive division of labour in different ways. Environmental factors, including measures of variation in temperature and precipitation, had no significant effects on any division of labour measure or colony size. Overall, these results support the size–complexity hypothesis for the evolution of social complexity and division of labour in eusocial insects. Determining the evolutionary drivers of colony size may help contribute to our understanding of the evolution of social complexity

Colony size predicts division of labour in Attine ants

Division of labour is central to the ecological success of eusocial insects, yet the evolutionary factors driving increases in complexity in division of labour are little known. The size–complexity hypothesis proposes that, as larger colonies evolve, both non-reproductive and reproductive division of labour become more complex as workers and queens act to maximize inclusive fitness. Using a statistically robust phylogenetic comparative analysis of social and environmental traits of species within the ant tribe Attini, we show that colony size is positively related to both non-reproductive (worker size variation) and reproductive (queen–worker dimorphism) division of labour. The results also suggested that colony size acts on non-reproductive and reproductive division of labour in different ways. Environmental factors, including measures of variation in temperature and precipitation, had no significant effects on any division of labour measure or colony size. Overall, these results support the size–complexity hypothesis for the evolution of social complexity and division of labour in eusocial insects. Determining the evolutionary drivers of colony size may help contribute to our understanding of the evolution of social complexity