Semantic search thesis

Called Latent Semantic Indexing because of its ability to correlate semantically related terms that are latent in a collection of text, it was first applied to text at Bellcore in the late 1980s. The method, also called latent semantic analysis (LSA), uncovers the underlying latent semantic structure in the usage of words in a body of text and how it can be used to extract the meaning of the text in response to user queries, commonly referred to as concept searches. Queries, or concept searches, against a set of documents that have undergone LSI will return results that are conceptually similar in meaning to the search criteria even if the results don’t share a specific word or words with the search criteria.

See also the activity news for an account of recent events, publications, etc. For links to tools, books, further details on the technologies, you can also refer to the Semantic Web Standards Wiki (and you are welcome to modify those pages when necessary and appropriate). You may also want to look at the collection of SW Case Studies and Use Cases to see how organizations are using these technologies today. Finally, for an exhaustive list of all the specifications published by the activity, please refer to the separate list of publications .

Dan Povey is a Research Consultant at Semantic Machines working on speech recognition and machine learning. He completed his PhD at Cambridge University in 2003, and after spending just under ten years working for industry research labs (IBM Research and then Microsoft Research), joined Johns Hopkins University in 2012. His thesis work introduced several practical innovations for discriminative training of models for speech recognition, and made those techniques widely popular. At IBM Research he introduced feature-space discriminative training, which has become a common feature of state-of-the art systems. He also devised the Subspace Gaussian Mixture Model – a modeling technique which enhances the Gaussian Mixture Model framework by using subspace ideas similar to those used in speaker identification. At Microsoft Research and then at Johns Hopkins University, he has been creating a speech recognition toolkit "Kaldi", which aims to make state-of-the-art speech recognition techniques widely accessible. 

Whereas infallibilism supports (S2) by demanding that an agent should be able to know the denials of all error-possibilities, closure merely demands that the agent knows the denials of those error-possibilities that are known to be logical consequences of what one knows. For example, if one knows the ordinary proposition that one is currently seated, and one further knows that if one is seated then one is not a BIV, then one must also know that one is not a BIV. Conversely, if one does not know that one is not a BIV then, given that one knows the entailment in question (which ought to be uncontroversial), one thereby lacks knowledge of the ordinary proposition in question, just as (S2) says. And note that, unlike (S2), the plausibility of closure is not merely prima facie . After all, we reason in conformity with closure all the time in cases where we gain knowledge of previously unknown propositions via knowledge of other propositions and the relevant entailment. Indeed, closure is in this respect far more compelling than infallibilism, since what credibility the latter thesis has is gained by philosophical argument rather than by prima facie reflection on our actual epistemic practice. The theoretical burden imposed upon anyone who advocates the denial of (S2) is thus very strong, since it requires a principled rejection of the intuitive principle of closure.

WSN nodes resource constrained. In order to keep the size and the cost of the nodes down, the nodes have limited processing power, memory and radio range. However, the resource constraint which has the most significant impact on many WSNs is the constraint on energy. WSN nodes are battery operated. Many wireless sensor networks are deployed in locations where battery replacement is not feasible. A node has to be discarded when the battery depletes. Energy scavenging may alleviate this problem in some sensor networks. Most WSN protocols are very conscious of the limited supply of energy, and try to conserve energy.

Semantic search thesis

semantic search thesis

Whereas infallibilism supports (S2) by demanding that an agent should be able to know the denials of all error-possibilities, closure merely demands that the agent knows the denials of those error-possibilities that are known to be logical consequences of what one knows. For example, if one knows the ordinary proposition that one is currently seated, and one further knows that if one is seated then one is not a BIV, then one must also know that one is not a BIV. Conversely, if one does not know that one is not a BIV then, given that one knows the entailment in question (which ought to be uncontroversial), one thereby lacks knowledge of the ordinary proposition in question, just as (S2) says. And note that, unlike (S2), the plausibility of closure is not merely prima facie . After all, we reason in conformity with closure all the time in cases where we gain knowledge of previously unknown propositions via knowledge of other propositions and the relevant entailment. Indeed, closure is in this respect far more compelling than infallibilism, since what credibility the latter thesis has is gained by philosophical argument rather than by prima facie reflection on our actual epistemic practice. The theoretical burden imposed upon anyone who advocates the denial of (S2) is thus very strong, since it requires a principled rejection of the intuitive principle of closure.

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