| 1. |
- Arrskog, Tobias, et al.
(författare)
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Hyperlocal event extraction of future events
- 2012
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Ingår i: DeRiVE 2012: Detection, Representation, and Exploitation of Events in the Semantic Web (CEUR Workshop Proceedings). - 1613-0073. ; 902, s. 11-21
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Konferensbidrag (refereegranskat)abstract
- From metropolitan areas to tiny villages, there is a wide variety of organizers of cultural, business, entertainment, and social events. These organizers publish such information to an equally wide variety of sources. Every source of published events uses its own document structure and provides dierent sets of information. This raises signicant customization issues. This paper explores the possibilities of extracting future events from a wide range of web sources, to determine if the document structure and content can be exploited for time-ecient hyperlocal event scraping. We report on two experimental knowledge-driven, pattern-based programs that scrape events from web pages using both their content and structure.
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| 2. |
- Abghari, Shahrooz, et al.
(författare)
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An Inductive System Monitoring Approach for GNSS Activation
- 2022
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Ingår i: IFIP Advances in Information and Communication Technology. - Cham : Springer Science+Business Media B.V.. - 9783031083365 ; , s. 437-449
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Konferensbidrag (refereegranskat)abstract
- In this paper, we propose a Global Navigation Satellite System (GNSS) component activation model for mobile tracking devices that automatically detects indoor/outdoor environments using the radio signals received from Long-Term Evolution (LTE) base stations. We use an Inductive System Monitoring (ISM) technique to model environmental scenarios captured by a smart tracker via extracting clusters of corresponding value ranges from LTE base stations’ signal strength. The ISM-based model is built by using the tracker’s historical data labeled with GPS coordinates. The built model is further refined by applying it to additional data without GPS location collected by the same device. This procedure allows us to identify the clusters that describe semi-outdoor scenarios. In that way, the model discriminates between two outdoor environmental categories: open outdoor and semi-outdoor. The proposed ISM-based GNSS activation approach is studied and evaluated on a real-world dataset contains radio signal measurements collected by five smart trackers and their geographical location in various environmental scenarios.
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| 3. |
- Al-Saedi, Ahmed Abbas Mohsin, 1980-, et al.
(författare)
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Context-Aware Edge-Based AI Models for Wireless Sensor Networks-An Overview
- 2022
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 22:15
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Forskningsöversikt (refereegranskat)abstract
- Recent advances in sensor technology are expected to lead to a greater use of wireless sensor networks (WSNs) in industry, logistics, healthcare, etc. On the other hand, advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) are becoming dominant solutions for processing large amounts of data from edge-synthesized heterogeneous sensors and drawing accurate conclusions with better understanding of the situation. Integration of the two areas WSN and AI has resulted in more accurate measurements, context-aware analysis and prediction useful for smart sensing applications. In this paper, a comprehensive overview of the latest developments in context-aware intelligent systems using sensor technology is provided. In addition, it also discusses the areas in which they are used, related challenges, motivations for adopting AI solutions, focusing on edge computing, i.e., sensor and AI techniques, along with analysis of existing research gaps. Another contribution of this study is the use of a semantic-aware approach to extract survey-relevant subjects. The latter specifically identifies eleven main research topics supported by the articles included in the work. These are analyzed from various angles to answer five main research questions. Finally, potential future research directions are also discussed.
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| 4. |
- Bergkvist, Hannes, et al.
(författare)
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Constraining neural networks output by an interpolating loss function with region priors
- 2020
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Ingår i: NeurIPS workshop on Interpretable Inductive Biases and Physically Structured Learning.
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Konferensbidrag (refereegranskat)abstract
- Deep neural networks have the ability to generalize beyond observed training data. However, for some applications they may produce output that apriori is known to be invalid. If prior knowledge of valid output regions is available, one way of imposing constraints on deep neural networks is by introducing these priors in a loss function. In this paper, we introduce a novel way of constraining neural network output by using encoded regions with a loss function based on gradient interpolation. We evaluate our method in a positioning task where a region map is used in order to reduce invalid position estimates. Results show that our approach is effective in decreasing invalid outputs for several geometrically complex environments.
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| 5. |
- Bergkvist, Hannes, et al.
(författare)
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Positioning with Map Matching using Deep Neural Networks
- 2020
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Ingår i: MobiQuitous '20. - New York, NY, USA : Association for Computing Machinery (ACM).
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Konferensbidrag (refereegranskat)abstract
- Deep neural networks for positioning can improve accuracy by adapting to inhomogeneous environments. However, they are still susceptible to noisy data, often resulting in invalid positions. A related task, map matching, can be used for reducing geographical invalid positions by aligning observations to a model of the real world. In this paper, we propose an approach for positioning, enhanced with map matching, within a single deep neural network model. We introduce a novel way of reducing the number of invalid position estimates by adding map information to the input of the model and using a map-based loss function. Evaluating on real-world Received Signal Strength Indicator data from an asset tracking application, we show that our approach gives both increased position accuracy and a decrease of one order of magnitude in the number of invalid positions.
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| 6. |
- Exner, Peter, et al.
(författare)
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A Distant Supervision Approach to Semantic Role Labeling
- 2015
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Ingår i: Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics (*SEM 2015). - 9781941643396 ; , s. 239-248
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Konferensbidrag (refereegranskat)abstract
- Semanticrolelabelinghasbecomeakeymodule for many language processing applications such as question answering, information extraction, sentiment analysis, and machine translation. To build an unrestricted semantic role labeler, the first step is to develop a comprehensive proposition bank. However, creating such a bank is a costly enterprise, which has only been achieved for a handful of languages. In this paper, we describe a technique to build proposition banks for new languages using distant supervision. Starting from PropBank inEnglishandlooselyparallelcorporasuchas versions of Wikipedia in different languages, we carried out a mapping of semantic propositions we extracted from English to syntactic structures in Swedish using named entities. We trained a semantic parser on the generated Swedishpropositionsandwereporttheresults we obtained. Using the CoNLL 2009 evaluation script, we could reach the scores of 52.25 for labeled propositions and 62.44 for the unlabeled ones. We believe our approach can be appliedtotrainsemanticrolelabelersforother resource-scarce languages.
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| 7. |
- Exner, Peter
(författare)
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Constructing Large Multilingual Proposition Databases
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis explores methods for generating proposition databases in a large-scale and multilingual setting. Our methods are centered on using semantic role labeling for extracting predicate-argument structures, and the subsequent transformation of such structures for knowledge base population and generation. By extending semantic role labeling with entity detection, we demonstrate how predicate-argument structures can be transformed to represent real world concepts and also act as a bridge connecting relational facts in multiple languages.We introduce a framework, KOSHIK, for large scale extraction of propositions from unstructured text and an annotation model for the incremental addition of annotation layers. In addition, we introduce an alignment method based on entities for aligning disparate ontologies and also for generating ontologies for new proposition databases. Using KOSHIK, we perform large-scale natural language processing of the entire English, Swedish, and French editions of Wikipedia. By transforming the structures extracted from Wikipedias, we extend existing knowledge bases in addition to generating new proposition databases. We demonstrate how generated proposition databases in Swedish and French can be used to effectively train semantic role labelers.
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| 8. |
- Exner, Peter, et al.
(författare)
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Constructing large proposition databases
- 2012
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Ingår i: Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12). - 9782951740877 ; , s. 3836-3839
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Konferensbidrag (refereegranskat)abstract
- With the advent of massive online encyclopedic corpora such as Wikipedia, it has become possible to apply a systematic analysis to a wide range of documents covering a significant part of human knowledge. Using semantic parsers, it has become possible to extract such knowledge in the form of propositions (predicate―argument structures) and build large proposition databases from these documents. This paper describes the creation of multilingual proposition databases using generic semantic dependency parsing. Using Wikipedia, we extracted, processed, clustered, and evaluated a large number of propositions. We built an architecture to provide a complete pipeline dealing with the input of text, extraction of knowledge, storage, and presentation of the resulting propositions
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| 9. |
- Exner, Peter, et al.
(författare)
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Entity extraction: From unstructured text to DBpedia RDF triples
- 2012
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Ingår i: Proceedings of the Web of Linked Entities Workshop in conjuction with the 11th International Semantic Web Conference (ISWC 2012). - 1613-0073. ; , s. 58-69
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Konferensbidrag (refereegranskat)abstract
- In this paper, we describe an end-to-end system that automatically extracts RDF triples describing entity relations and properties from unstructured text. This system is based on a pipeline of text processing modules that includes a semantic parser and a coreference solver. By using coreference chains, we group entity actions and properties described in different sentences and convert them into entity triples. We applied our system to over 114,000 Wikipedia articles and we could extract more than 1,000,000 triples. Using an ontology-mapping system that we bootstrapped using existing DBpedia triples, we mapped 189,000 extracted triples onto the DBpedia namespace. These extracted entities are availableonline in the N-Triple format. 1 1 http://semantica.cs.lth.se/
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| 10. |
- Exner, Peter, et al.
(författare)
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KOSHIK: A large-scale distributed computing framework for NLP
- 2014
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Ingår i: 3rd International Conference on Pattern Recognition Applications and Methods (ICPRAM 2014). - 9789897580185 ; , s. 464-470
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Konferensbidrag (refereegranskat)abstract
- In this paper, we describe KOSHIK, an end-to-end framework to process the unstructured natural language content of multilingual documents. We used the Hadoop distributed computing infrastructure to build this framework as it enables KOSHIK to easily scale by adding inexpensive commodity hardware. We designed an annotation model that allows the processing algorithms to incrementally add layers of annotation without modifyingtheoriginaldocument. We used the Avro binary format to serialize th edocuments. Avro is designed for Hadoop and allows other data warehousing tools to directly query the documents. This paper reports the implementation choices and details of the framework,the annotation model,the options for querying processed data, and the parsing results on the English and Swedish editions of Wikipedia.
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