Group items tagged

# The primary intention of creating aTags is not the categorization of the document, but the representation of the key facts inside the document. Key facts in the biomedical domain might be, for example, "Protein A interacts with protein B" or "Overexpression of protein A in tissue B is the cause of disease C". # An aTag is comprised of a set of associated entities. The size of the set is arbitrary, but will typically lie between 2 and 5 entities. For example, the fact "Protein A binds to protein B" can be represented with an aTag comprising of the three entities "Protein A", "Molecular interaction" and "Protein B". Similarly, the fact "Overexpression of protein A in tissue B is the cause of disease C" can be represented with an aTag comprising of the four entities "Overexpression", "Protein A", "Tissue B" and "Disease C". # Each document or database entry can be described with an arbitrary number of such aTags. Each aTag can be associated with the relevant portions of text or data in a fine granularity. # The entities in an aTag are not simple strings, but resources that are part of ontologies and RDF/OWL-enabled databases. For example, "Protein A" and "Protein B" are resources that are defined in the UniProt database, whereas "Molecular Interaction" is a class in the branch of biological processes of the Gene Ontology. They are identified with their URIs.

Jigsaw provides a collection of visualizations that each portray different aspects of the documents. We particularly focus on presenting the identifiable important entities (people, places, organizations, etc.) and their direct or indirect connections. Textual processing extracts the important entities from the documents and then the visualizations help an analyst to explore the relationships and connections among the entities. The system includes graph, calendar, scatterplot and and tabular connections-based views, as well as views of individual document's text and the report collections as a whole. Jigsaw essentially acts as a visual index onto the document collection, helping analysts identify particular documents to read and examine next.

OYSTER stands for Open sYSTem Entity Resolution, a project to build a configurable, open-source entity resolution engine.

The OKKAM project aims at enabling the Web of Entities, namely a virtual space where any collection of data and information about any type of entities (e.g. people, locations, organizations, events, products, ...) published on the Web can be integrated into a single virtual, decentralized, open knowledge base

Unstructured Information Management applications are software systems that analyze large volumes of unstructured information in order to discover knowledge that is relevant to an end user. UIMA is a framework and SDK for developing such applications. An example UIM application might ingest plain text and identify entities, such as persons, places, organizations; or relations, such as works-for or located-at. UIMA enables such an application to be decomposed into components, for example "language identification" -> "language specific segmentation" -> "sentence boundary detection" -> "entity detection (person/place names etc.)". Each component must implement interfaces defined by the framework and must provide self-describing metadata via XML descriptor files. The framework manages these components and the data flow between them. Components are written in Java or C++; the data that flows between components is designed for efficient mapping between these languages. UIMA additionally provides capabilities to wrap components as network services, and can scale to very large volumes by replicating processing pipelines over a cluster of networked nodes.

Dubai Knowledge Village (DKV), launched in 2003, places the Middle East on the map as a destination for learning excellence. Its 1 KM long picturesque campus provides a ready environment for a variety of knowledge-based entities including training centres and learning support entities.

YAGO is a huge semantic knowledge base. Currently, YAGO knows more than 2 million entities (like persons, organizations, cities, etc.). It knows 20 million facts about these entities. Unlike many other automatically assembled knowledge bases, YAGO has a manually confirmed accuracy of 95%

KIM is a software platform for: * Semantic annotation of text At more length: automatic ontology population and open-domain dynamic semantic annotation of unstructured and semi-structured content for Semantic Web and KM applications * Indexing and retrieval (semantically-enabled and IE-enhanced search technology) * Query and exploration of formal knowledge * Co-occurrence tracking and ranking of entities * Entity popularity timelines analysis

The aims of clinical and translational research are to achieve a better understanding of the pathogenesis of human disease in order to develop effective diagnostic, therapeutic and prevention strategies. Biomedical informatics can play an important role in supporting this research by facilitating the management, integration, analysis and exchange of data derived from and related to the research problems being studied. A key aspect of this support is to bring clarity, rigor and formalism to the representation of 1. disease initiation, progression, pathogenesis, signs, symptoms, assessments, clinical and laboratory findings, disease diagnosis, treatment, treatment response and outcome, and 2. the interrelations between these distinct entities both in patient management and in clinical research, thus allowing the data to be more readily retrievable and shareable, and more able to serve in the support of algorithmic reasoning.

The mistake is that semantic search engines present us with Google-like search box and allow us to enter free form queries. So we type the things that we are used to asking - primitive queries. It never occurs to us to type in What actor starred in both Pulp Fiction and Saturday Night Fever? or What two US Senators received donations from a foreign entity? We type simple questions, but this is not where the power of semantic search lies. Lets look at the spectrum of semantic technologies from Google, to SearchMonkey, to Powerset, and Freebase to understand what is going on.

Alchemy is a software package providing a series of algorithms for statistical relational learning and probabilistic logic inference, based on the Markov logic representation. Alchemy allows you to easily develop a wide range of AI applications, including: * Collective classification * Link prediction * Entity resolution * Social network modeling * Information extraction

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.

"The semantic web proposes to inject machine meaningful data into the existing human language oriented web. As part of this effort, on the semantic web, URIs are used to identify entities. But there is currently no standard way to specify what it is that any given URI is to identify, or to whom, or when. Recent work in linguistics offers ideas for a solution to this lack. It focuses on the pragmatics of actual language use among ensembles of people. Also, the World Wide Web provides a set of technologies, in the form of socially constructed web sites, that could be employed to provide a solution. In this paper, I suggest how such socially constructed web sites could be used to address the problem of establishing common ground among a community of machines of the referent of a URI used on the semantic web. The result is a proposal to automate social meaning by creating societies of machines that share knowledge representations identified by URIs."

What tagging does point to convincingly is the social aspect of naming. In a given natural language, many sorts of identifiers, such as common words, are socially centralized. Other sorts of identifiers, such as proper names, are socially decentralized, varying from local context to local context. Black has noticed a correspondence between this socially grounded identification process and the use of socially constructed Web sites.

The YAGO-NAGA project started in 2006 with the goal of building a conveniently searchable, large-scale, highly accurate knowledge base of common facts in a machine-processible representation. We have already harvested knowledge about millions of entities and facts about their relationships, from Wikipedia and WordNet with careful integration of these two sources. The resulting knowledge base, coined YAGO, has very high precision and is freely available. The facts are represented as RDF triples, and we have developed methods and prototype systems for querying, ranking, and exploring knowledge. Our search engine NAGA provides ranked answers to queries based on statistical models.

The GoodRelations ontology provides the vocabulary for annotating e-commerce offerings (1) to sell, lease, repair, dispose, and maintain commodity products and (2) to provide commodity services. GoodRelations allows describing the relationship between (1) Web resources, (2) offerings made by those Web resources, (3) legal entities, (4) prices, (5) terms and conditions, and the aforementioned ontologies for products and services (6). For more information, see http://purl.org/goodrelations/ Note: The base URI of GoodRelations has changed to http://purl.org/goodrelations/v1. Please make sure you are only using element identifiers in this namespace, e.g. http://purl.org/goodrelations/v1#BusinessEntity. T

Identifying aliases is an important component of the criminal justice system. Accurately identifying a person of interest or someone who has been arrested can significantly reduce the costs within the entire criminal justice system. This paper examines the problem domain of matching and relating identities, examines traditional approaches to the problem, and applies the identity resolution approach described by Jeff Jonas and relationship awareness to the specific case of client identification for the indigent defense office. The combination of identify resolution and relationship awareness offered improved accuracy in matching identities

Further work building on Jeff Jonas' "data finds data", and his his article in IEEE Security and Privacy entitled "Threat and Fraud Intelligence, Las Vegas Style"

Matching and relating identities is of the utmost importance for Las Vegas casinos. The author describes a specific matching technique known as identity resolution. This approach provides superior results over traditional identity matching systems.

High flair, non academic case for semantic reconciliation and indexing. No tech detail, but clear and useful principles.