Halo I: A Controlled Experiment for Large Scale Knowledge Base Development

This paper examines video retrieval based on Query-By-Example (QBE) approach, where shots relevant to a query are retrieved from large-scale video data based on their similarity to example shots. This involves two crucial problems: The first is that similarity in features does not necessarily imply similarity in semantic content. The second problem is an expensive computational cost to compute the similarity of a huge number of shots to example shots. The authors have developed a method that can filter a large number of shots irrelevant to a query, based on a video ontology that is knowledge base about concepts displayed in a shot. The method utilizes various concept relationships (e.g., generalization/specialization, sibling, part-of, and co-occurrence) defined in the video ontology. In addition, although the video ontology assumes that shots are accurately annotated with concepts, accurate annotation is difficult due to the diversity of forms and appearances of the concepts. Dempster-Shafer theory is used to account the uncertainty in determining the relevance of a shot based on inaccurate annotation of this shot. Experimental results on TRECVID 2009 video data validate the effectiveness of the method.

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Predicting Lexical Answer Types in Open Domain QA

International Journal on Semantic Web and Information Systems ◽

10.4018/jswis.2012070104 ◽

2012 ◽

Vol 8 (3) ◽

pp. 74-88 ◽

Cited By ~ 1

Author(s):

Alfio Massimiliano Gliozzo ◽

Aditya Kalyanpur

Keyword(s):

Knowledge Acquisition ◽

Knowledge Base ◽

Large Scale ◽

Question Answering ◽

General Purpose ◽

Open Domain ◽

Lexical Knowledge ◽

Research Challenge ◽

Lexical Knowledge Base

Automatic open-domain Question Answering has been a long standing research challenge in the AI community. IBM Research undertook this challenge with the design of the DeepQA architecture and the implementation of Watson. This paper addresses a specific subtask of Deep QA, consisting of predicting the Lexical Answer Type (LAT) of a question. Our approach is completely unsupervised and is based on PRISMATIC, a large-scale lexical knowledge base automatically extracted from a Web corpus. Experiments on the Jeopardy! data shows that it is possible to correctly predict the LAT in a substantial number of questions. This approach can be used for general purpose knowledge acquisition tasks such as frame induction from text.

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COMBINING DEFAULT LOGIC DATABASES

International Journal of Cooperative Information Systems ◽

10.1142/s0218215794000181 ◽

1994 ◽

Vol 03 (03) ◽

pp. 319-348 ◽

Cited By ~ 4

Author(s):

CHITTA BARAL ◽

SARIT KRAUS ◽

JACK MINKER ◽

V. S. SUBRAHMANIAN

Keyword(s):

Knowledge Representation ◽

Knowledge Base ◽

Large Scale ◽

Future Generation ◽

Knowledge Bases ◽

Default Logic ◽

Integrity Constraints ◽

Specialized Knowledge ◽

The Past ◽

The Future

During the past decade, it has become increasingly clear that the future generation of large-scale knowledge bases will consist, not of one single isolated knowledge base, but a multiplicity of specialized knowledge bases that contain knowledge about different domains of expertise. These knowledge bases will work cooperatively, pooling together their varied bodies of knowledge, so as to be able to solve complex problems that no single knowledge base, by itself, would have been able to address successfully. In any such situation, inconsistencies are bound to arise. In this paper, we address the question: "Suppose we have a set of knowledge bases, KB1, …, KBn, each of which uses default logic as the formalism for knowledge representation, and a set of integrity constraints IC. What knowledge base constitutes an acceptable combination of KB1, …, KBn?"

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Automatic construction of a large-scale and accurate drug-side-effect association knowledge base from biomedical literature

Journal of Biomedical Informatics ◽

10.1016/j.jbi.2014.05.013 ◽

2014 ◽

Vol 51 ◽

pp. 191-199 ◽

Cited By ~ 30

Author(s):

Rong Xu ◽

QuanQiu Wang

Keyword(s):

Knowledge Base ◽

Side Effect ◽

Large Scale ◽

Biomedical Literature ◽

Drug Side Effect ◽

Automatic Construction

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Workflows Científicos com Apoio de Bases de Conhecimento em Tempo Real

10.5753/bresci.2016.9123 ◽

2020 ◽

Author(s):

Victor S. Bursztyn ◽

Jonas Dias ◽

Marta Mattoso

Keyword(s):

Knowledge Base ◽

Domain Knowledge ◽

Large Scale ◽

Workflow Engine ◽

Workflow Execution ◽

Human In The Loop ◽

Domain Specific ◽

Political Sciences ◽

Provenance Data ◽

Domain Specific Knowledge

One major challenge in large-scale experiments is the analytical capacity to contrast ongoing results with domain knowledge. We approach this challenge by constructing a domain-specific knowledge base, which is queried during workflow execution. We introduce K-Chiron, an integrated solution that combines a state-of-the-art automatic knowledge base construction (KBC) system to Chiron, a well-established workflow engine. In this work we experiment in the context of Political Sciences to show how KBC may be used to improve human-in-the-loop (HIL) support in scientific experiments. While HIL in traditional domain expert supervision is done offline, in K-Chiron it is done online, i.e. at runtime. We achieve results in less laborious ways, to the point of enabling a breed of experiments that could be unfeasible with traditional HIL. Finally, we show how provenance data could be leveraged with KBC to enable further experimentation in more dynamic settings.

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Automated Ensemble Framework for Integration of Ontology Based Large Scale Semantic Knowledge Base

Journal of Engineering and Applied Sciences ◽

10.36478/jeasci.2019.399.404 ◽

2019 ◽

Vol 14 (2) ◽

pp. 399-404

Author(s):

G. Silambarasan ◽

J. Anvar Shathik

Keyword(s):

Knowledge Base ◽

Large Scale ◽

Semantic Knowledge ◽

Semantic Knowledge Base

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Cell-based meat: the need to assess holistically

Journal of Animal Science ◽

10.1093/jas/skaa177 ◽

2020 ◽

Vol 98 (8) ◽

Cited By ~ 1

Author(s):

Cameron Faustman ◽

Deb Hamernik ◽

Michael Looper ◽

Steven A Zinn

Keyword(s):

Knowledge Base ◽

Muscle Tissue ◽

Manufacturing Process ◽

Cell Biology ◽

Large Scale ◽

New Product ◽

Subsequent Research ◽

Potential Alternative ◽

Proof Of Principle

Abstract Proof-of-principle for large-scale engineering of edible muscle tissue, in vitro, was established with the product’s introduction in 2013. Subsequent research and commentary on the potential for cell-based meat to be a viable food option and potential alternative to conventional meat have been significant. While some of this has focused on the biology and engineering required to optimize the manufacturing process, a majority of debate has focused on cultural, environmental, and regulatory considerations. Animal scientists and others with expertise in muscle and cell biology, physiology, and meat science have contributed to the knowledge base that has made cell-based meat possible and will continue to have a role in the future of the new product. Importantly, the successful introduction of cell-based meat that looks and tastes like conventional meat at a comparable price has the potential to displace and/or complement conventional meat in the marketplace.

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