Expressing No-Value Information in RDF
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Minute Madness Slide for ISWC 2015 Narration: We all know Obama (yet another Semantic Web example with Obama :-) We know Obama has no sons. However, RDF does not know. So then, when asking for schools of Obama's sons, we know there must be no answer. SPARQL gives no answer, but does it know if it's due to incompleteness or non-existence of information?
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This was presented at ISWC 2013 in Sydney, Australia.
Abstract:
With thousands of RDF data sources available on the Web covering disparate and possibly overlapping knowledge domains, the problem of providing high-level descriptions (in the form of metadata) of their content becomes crucial. In this paper we introduce a theoretical framework for describing data sources in terms of their completeness. We show how existing data sources can be described with completeness statements expressed in RDF. We then focus on the problem of the completeness of query answering over plain and RDFS data sources augmented with completeness statements. Finally, we present an extension of the completeness framework for federated data sources.
Testing in Python: doctest and unittest (Updated)
The document discusses testing in Python. It defines testing vs debugging, and explains why testing is important even for professional programmers. It provides examples of manually testing a square area function that initially had a bug, and how the bug was detected and fixed. It then introduces doctest and unittest as systematic ways to test in Python, providing examples of using each. Finally, it discusses test-driven development as a software development method where tests are defined before writing code.
Testing in Python: doctest and unittest
The document discusses testing in Python. It defines testing vs debugging, and explains why testing is important even for professional programmers. It introduces doctest and unittest as systematic ways to test Python code. Doctest allows embedding tests in docstrings, while unittest involves writing separate test files. The document also covers test-driven development, which involves writing tests before coding to define desired behavior.
Dissertation Defense - Managing and Consuming Completeness Information for RD...
The ever increasing amount of Semantic Web data gives rise to the question: How complete is the data? Though generally data on the Semantic Web is incomplete, many parts of data are indeed complete, such as the children of Barack Obama and the crew of Apollo 11. This thesis aims to study how to manage and consume completeness information about Semantic Web data. In particular, we first discuss how completeness information can guarantee the completeness of query answering. Next, we propose optimization techniques of completeness reasoning and conduct experimental evaluations to show the feasibility of our approaches. We also provide a technique to check the soundness of queries with negation via reduction to query completeness checking. We further enrich completeness information with timestamps, enabling query answers to be checked up to when they are complete. We then introduce two demonstrators, i.e., CORNER and COOL-WD, to show how our completeness framework can be realized. Finally, we investigate an automated method to generate completeness statements from text on the Web via relation cardinality extraction.
Research Writing - 2018.07.18
The document provides information about research writing. It discusses that everyone can be considered a researcher through everyday activities like using social media or traveling. Research is defined as a careful, diligent search to establish new facts or reach conclusions. The constituents of research are outlined as defining problems, formulating hypotheses, collecting and analyzing data, and validating conclusions. The document emphasizes that research writing is important and discusses choosing the right research topic and venue for publication. It provides tips for writing different sections of a research paper and following the common three-phase model of initial workshop or conference papers leading to a journal publication.
KOI - Knowledge Of Incidents - SemEval 2018
We present KOI (Knowledge Of Incidents), a system that given news articles as input, builds a knowledge graph (KOI-KG) of incidental events.
KOI-KG can then be used to efficiently answer questions such as "How many killing incidents happened in 2017 that involve Sean?" The required steps in building the KG include:
(i) document preprocessing involving word sense disambiguation, named-entity recognition, temporal expression recognition and normalization, and semantic role labeling;
(ii) incidental event extraction and coreference resolution via document clustering; and (iii) KG construction and population.
Slides made and presented by Paramita.
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[ISWC 2013] Completeness statements about RDF data sources and their use for ...
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Editor's Notes
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