Theoretical Foundations for Enabling a Web of Knowledge

Embley, David W.; Zitzelberger, Andrew

doi:10.1007/978-3-642-11829-6_15

Cited by 5 publications

(2 citation statements)

References 29 publications

(29 reference statements)

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“…To answer queries quickly, we must semantically crawl and index resources in advance. To create semantic indexes, we apply conceptual-model-based extraction ontologies to resources; we also pre-execute inference rules so that we index not only stated assertions but also inferred assertions [EZ10].…”

Section: Conceptual Modeling To the Rescuementioning

confidence: 99%

Big Data—Conceptual Modeling to the Rescue

Embley

Liddle

2013

Conceptual Modeling

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Every day humans generate several petabytes of data [ZEd + 11] from a variety of sources such as orbital weather satellites, ground-based sensor networks, mobile computing devices, digital cameras, and retail point-of-sale registers. Companies, governments, and individuals store this data in a wide variety of structured, semistructured, and unstructured formats. However, most of this data either languishes in underutilized storage repositories or is never stored in the first place. Ironically, in an era of unprecedented access to a veritable gold mine of information, it is increasingly difficult to unlock the value stored within our data. The essential problem of "Big Data" is that we are accumulating data faster than we can process it, and this trend is accelerating.The so-called "four V's" characterize Big Data:-Volume: applications sometimes exceeding petabytes 3 -Variety: widely varying heterogeneous information sources and hugely diverse application needs -Velocity: phenomenal rate of data acquisition, real-time streaming data, and variable time-value of data -Veracity: trustworthiness and uncertainty, beyond the limits of humans to checkWe should expect conceptual modeling to provide some answers since its historical perspective has always been about structuring information-making its volume searchable, harnessing its variety uniformly, mitigating its velocity with automation, and checking its veracity with application constraints. We do not envision any silver bullets that will slay the "werewolf" of Big Data, but conceptual modeling can help, as we illustrate with an example from our project that seeks to superimpose a web of knowledge over a rapidly growing heterogeneous collection of historical documents whose storage requirements are likely to eventually exceed many exabytes.3 Having successfully communicated the terms "mega-," "giga-," and "tera-byte," in the Big Data era we now need to teach users about "peta-," "exa-," "zetta-," and even "yotta-bytes." The NSA data center being built in Utah within 35km of our university purportedly is designed to store at least zettabytes (10 21 bytes) and perhaps yottabytes (10 24 bytes) of data.

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Section: Conceptual Modeling To the Rescuementioning

confidence: 99%

Big Data—Conceptual Modeling to the Rescue

Embley

Liddle

2013

Conceptual Modeling

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“…When designing a multimodal ontology one must also take into account the problems related to designing multimodal systems as described in [25], and also the medium in which these agents will evolve because an agent that needs to evolve in the urban computing environment [34] will have different needs than an agent that just surfs the web. The focus of research is usually on multimodal fusion, but a recent survey [9] shows that the interest in multimedia fission is increasing. Designing a multimodal ontology thus requires taking into account all these findings because the agent must be able to give us a response not only to understand our requirements.…”

Section: Generic Multimodal Ontologies For Human-agent Interactionmentioning

confidence: 99%

Generic Multimodal Ontologies for Human-Agent Interaction

Brașoveanu

Manolescu

Spînu

2010

INT J COMPUT COMMUN

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Watching the evolution of the Semantic Web (SW) from its inception to these days we can easily observe that the main task the developers face while building it is to encode the human knowledge into ontologies and the human reasoning into dedicated reasoning engines. Now, the SW needs to have efficient mechanisms to access information by both humans and artificial agents. The most important tools in this context are ontologies. The last years have been dedicated to solving the infrastructure problems related to ontologies: ontology management, ontology matching, ontology adoption, but as time goes by and these problems are better understood the research interests in this area will surely shift towards the way in which agents will use them to communicate between them and with humans. Despite the fact that interface agents could be bilingual, it would be more efficient, safe and swift that they should use the same language to communicate with humans and with their peers. Since anthropocentric systems entail nowadays multimodal interfaces, it seems suitable to build multimodal ontologies. Generic ontologies are needed when dealing with uncertainty. Multimodal ontologies should be designed taking into account our way of thinking (mind maps, visual thinking, feedback, logic, emotions, etc.) and also the processes in which they would be involved (multimodal fusion and integration, error reduction, natural language processing, multimodal fission, etc.). By doing this it would be easier for us (and also fun) to use ontologies, but in the same time the communication with agents (and also agent to agent talk) would be enhanced. This is just one of our conclusions related to why building generic multimodal ontologies is very important for future semantic web applications.

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