Knowledge representation and reasoning is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning, such as the application of rules or the relations of sets and subsets.
A semantic network, or frame network is a knowledge base that represents semantic relations between concepts in a network. This is often used as a form of knowledge representation. It is a directed or undirected graph consisting of vertices, which represent concepts, and edges, which represent semantic relations between concepts, mapping or connecting semantic fields. A semantic network may be instantiated as, for example, a graph database or a concept map. Typical standardized semantic networks are expressed as semantic triples.
The Semantic Web, sometimes known as Web 3.0, is an extension of the World Wide Web through standards set by the World Wide Web Consortium (W3C). The goal of the Semantic Web is to make Internet data machine-readable.
A knowledge-based system (KBS) is a computer program that reasons and uses a knowledge base to solve complex problems. The term is broad and refers to many different kinds of systems. The one common theme that unites all knowledge based systems is an attempt to represent knowledge explicitly and a reasoning system that allows it to derive new knowledge. Thus, a knowledge-based system has two distinguishing features: a knowledge base and an inference engine.
Social search is a behavior of retrieving and searching on a social searching engine that mainly searches user-generated content such as news, videos and images related search queries on social media like Facebook, LinkedIn, Twitter, Instagram and Flickr. It is an enhanced version of web search that combines traditional algorithms. The idea behind social search is that instead of ranking search results purely based on semantic relevance between a query and the results, a social search system also takes into account social relationships between the results and the searcher. The social relationships could be in various forms. For example, in LinkedIn people search engine, the social relationships include social connections between searcher and each result, whether or not they are in the same industries, work for the same companies, belong the same social groups, and go the same schools, etc.
Semantic publishing on the Web, or semantic web publishing, refers to publishing information on the web as documents accompanied by semantic markup. Semantic publication provides a way for computers to understand the structure and even the meaning of the published information, making information search and data integration more efficient.
Spreading activation is a method for searching associative networks, biological and artificial neural networks, or semantic networks. The search process is initiated by labeling a set of source nodes with weights or "activation" and then iteratively propagating or "spreading" that activation out to other nodes linked to the source nodes. Most often these "weights" are real values that decay as activation propagates through the network. When the weights are discrete this process is often referred to as marker passing. Activation may originate from alternate paths, identified by distinct markers, and terminate when two alternate paths reach the same node. However brain studies show that several different brain areas play an important role in semantic processing.
Ontotext is a software company with offices in Europe and USA. It is the semantic technology branch of Sirma Group. Its main domain of activity is the development of software based on the Semantic Web languages and standards, in particular RDF, OWL and SPARQL. Ontotext is best known for the Ontotext GraphDB semantic graph database engine. Another major business line is the development of enterprise knowledge management and analytics systems that involve big knowledge graphs. Those systems are developed on top of the Ontotext Platform that builds on top of GraphDB capabilities for text mining using big knowledge graphs.
A triplestore or RDF store is a purpose-built database for the storage and retrieval of triples through semantic queries. A triple is a data entity composed of subject–predicate–object, like "Bob is 35" or "Bob knows Fred".
OpenCog is a project that aims to build an open source artificial intelligence framework. OpenCog Prime is an architecture for robot and virtual embodied cognition that defines a set of interacting components designed to give rise to human-equivalent artificial general intelligence (AGI) as an emergent phenomenon of the whole system. OpenCog Prime's design is primarily the work of Ben Goertzel while the OpenCog framework is intended as a generic framework for broad-based AGI research. Research utilizing OpenCog has been published in journals and presented at conferences and workshops including the annual Conference on Artificial General Intelligence. OpenCog is released under the terms of the GNU Affero General Public License.
Yebol was a vertical "decision" search engine that had developed a knowledge-based, semantic search platform. Based in San Jose, California, Yebol's artificial intelligence human intelligence-infused algorithms automatically cluster and categorize search results, web sites, pages and contents that it presents in a visually indexed format that is more aligned with initial human intent. Yebol used association, ranking and clustering algorithms to analyze related keywords or web pages. Yebol presented as one of its goals the creation of a unique "homepage look" for every possible search term.
In computing, a graph database (GDB) is a database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data. A key concept of the system is the graph. The graph relates the data items in the store to a collection of nodes and edges, the edges representing the relationships between the nodes. The relationships allow data in the store to be linked together directly and, in many cases, retrieved with one operation. Graph databases hold the relationships between data as a priority. Querying relationships is fast because they are perpetually stored in the database. Relationships can be intuitively visualized using graph databases, making them useful for heavily inter-connected data.
The Google Knowledge Graph is a knowledge base used by Google and its services to enhance its search engine's results with information gathered from a variety of sources. The information is presented to users in an infobox next to the search results. These infoboxes were added to Google's search engine in May 2012, starting in the United States, with international expansion by the end of the year. Google refers to the infoboxes, which appear to the right of search results, as "knowledge panels".
In natural language processing, entity linking, also referred to as named-entity linking (NEL), named-entity disambiguation (NED), named-entity recognition and disambiguation (NERD) or named-entity normalization (NEN) is the task of assigning a unique identity to entities mentioned in text. For example, given the sentence "Paris is the capital of France", the idea is to determine that "Paris" refers to the city of Paris and not to Paris Hilton or any other entity that could be referred to as "Paris". Entity linking is different from named-entity recognition (NER) in that NER identifies the occurrence of a named entity in text but it does not identify which specific entity it is.
The Self-Service Semantic Suite (S4) provides on-demand access to text mining and linked open data technology in the cloud. The S4 stack is based on enterprise-grade technology from Ontotext including their leading RDF engine and high performance text mining solutions successfully applied in some of the largest enterprises in the world.
Semantic queries allow for queries and analytics of associative and contextual nature. Semantic queries enable the retrieval of both explicitly and implicitly derived information based on syntactic, semantic and structural information contained in data. They are designed to deliver precise results or to answer more fuzzy and wide open questions through pattern matching and digital reasoning.
Semantic Scholar is an artificial-intelligence backed search engine for academic publications developed at the Allen Institute for AI and publicly released in November 2015. It uses advances in natural language processing to provide summaries for scholarly papers. The Semantic Scholar team is actively researching the use of artificial-intelligence in natural language processing, machine learning, Human-Computer interaction, and information retrieval.
In knowledge representation and reasoning, knowledge graph is a knowledge base that uses a graph-structured data model or topology to integrate data. Knowledge graphs are often used to store interlinked descriptions of entities – objects, events, situations or abstract concepts – while also encoding the semantics underlying the used terminology.
A knowledge graph is a knowledge base that uses a graph-structured data model.
Ontotext GraphDB is a graph database and knowledge discovery tool compliant with RDF and SPARQL and available as a high-availability cluster. Ontotext GraphDB is used in various European research projects.
