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A Semantic Map is kind of like a dictionary, in that it's a representation of Cognition's ability to define things. Cognition claims that its Semantic Map has over 10 million semantic connections; over 4 million semantic contexts (word meanings that create contexts for specific meanings of other related words); over 536,000 word senses (word and phrase meanings); 75,000 concept classes (or synonym classes of word meanings); 7,500 nodes in the technology's ontology or classification scheme; and 506,000 word stems (roots of words) for the English language.

The semantic mapping of the English language is the key to making Natural Language Processing (NLP) effective. Cognition's unique Semantic Map, which it built over the past 24 years, is the most comprehensive and complete map of the English language available today. It can be used in support of the Semantic Web for semantic search, search tools, business analytics, machine translation, document search, context search, and much more.

Andreas Hotho's talk more specifically addressed the search for methods to identify tags which describe the same concept (or a more specific / a more general concept respectively) within a folksonomy. He suggested two approaches: 1. Applying measures directly to folksonomy statistics, allowing to describe tags as a vector; e.g. co-occurrence frequency and FolkRank could serve as a similarity measure (with these two having a tendency towards high-frequency tags) or a cosine method (which is more likely to produce "siblings") 2. Looking up tags in an external thesaurus/vocabulary (for instance achieving semantic grounding by mapping a tag and its most similar tags with Wordnet Synsets)

Integrate. Relate. Infer. Share. IRIS is a semantic desktop application framework that enables users to create a "personal map" across their office-related information objects. IRIS includes a machine-learning platform to help automate this process. It provides "dashboard" views, contextual navigation, and relationship-based structure across an extensible suite of office applications, including a calendar, web and file browser, e-mail client, and instant messaging client.

In this paper an application of methodologies to automatically acquire semantic word classes and to use them in text information retrieval systems is described.

Sensemaking is the process through which humans put together understanding of related information. Sensemaking has been said to involve changes in cognitive representations during a human information processing task. Collection sensemaking involves understanding a collection of media entities, as a whole. One example of a sensemaking task is to compare the damage from Hurricane Katrina to homes, personal effects, and community buildings in different areas of New Orleans. Connected visual and semantic representations provide perspective to support users involved in collection sensemaking tasks. A zoomable map organizes images based on location at varying scales. Multiscale clusters based on zoom level organize images associated with events. The clusters afford contextualized thumbnail browsing and also maintain uniform information density on the map. Metadata enhances context and memory in the process of collection sensemaking.

Crosswalks are documents that map metadata elements between different metadata standards. The crosswalk may be presented as a document for humans to read, in which case the crosswalking process must be performed by humans who are referencing the document. Alternatively, a crosswalk document may be expressed in such a way that a computer can automatically perform a mapping from one metadata standard to another.

OntoWiki facilitates the visual presentation of a knowledge base as an information map, with different views on instance data. It enables intuitive authoring of semantic content, with an inline editing mode for editing RDF content, similar to WYSIWIG for text documents.

In order to keep data structures in Semantic CrunchBase close to the source API, I used a 1-to-1 mapping between CrunchBase JSON keys and RDF terms (with only a few exceptions). This was helpful for people knowing the JSON API, but it wasn't easy to interlink the converted information with existing SemWeb data such as FOAF, or the various LOD sources.

The Object-Oriented Hypermedia Design Method (OOHDM) (and its successor, the Semantic Hypermedia Design Method, SHDM) allow the concise specification and implementation of hypermedia (web) applications. This is achieved based on various models describing information (conceptual), navigation and interface aspects of these applications, and the mapping of these models into running applications, in various environements.

Control information overload. Emails, links, attachments, blog posts, news-all relevant data is organized and prioritized by contact.