Analyzing the Benefits of Domain Knowledge in Substructure Discovery

AAAI Conferences

However, scientists working with a database in their area of expertise often search for predetermined types of structures, or for structures exhibiting characteristics specific to the domain. This paper presents a method for guiding the discovery process with domain-specific knowledge.


The Enterprise Knowledge Graph

#artificialintelligence

Best conceived of as a "company brain," this knowledge graph focuses on integrating an organization's assortment of people, skills, experiences, materials, essential company databases, and projects, which greatly improves its self-knowledge and thereby yields competitive advantage. Compiled from combing through myriad databases, including those for human resources, emails, and manifold other sources, this knowledge graph provides the foundation for a rapid, detailed assessment of what knowledge and skills a company has at its disposal--and their relation to one another. This graph is designed to create better services and is extremely specific to an organization's industry, line of business, and area of specialization. For example, Google's and Yahoo's search engine endeavors mandate that they collect knowledge about every entity or subject in the world, so they can offer the most relevant, revealing information to their users. LinkedIn's knowledge graph, on the other hand, details people's professions, resumes, and career opportunities.1 Again, the relationships between these nodes are paramount.


Pragmatic Querying in Heterogeneous Knowledge Graphs

AAAI Conferences

Knowledge Graphs with rich schemas can allow for complex querying. My thesis focuses on providing accessible Knowledge using Gricean notions of Cooperative Answering as a motivation. More specifically, using Query Reformulations, Data Awareness, and a Pragmatic Context, along with the results they can become more responsive to user requirements and user context.


Bridging the Gap Between Schank and Montague

AAAI Conferences

Documents that people write to communicate with other people are rarely as precise as a formal logic. Yet people can read those documents and relate them to formal notations for science, mathematics, and computer programming. They can derive whatever information they need, reason about it, and apply it at an appropriate level of precision. Unlike theorem provers, people rely on analogies for their reasoning. Even mathematicians use analogies to discover their theorems and formal proofs to verify and codify their discoveries. This article shows how a high-speed analogy engine is used to analyze natural language texts and relate the results to both structured and unstructured representations. The degree of precision in the results depends more on the precision in the knowledge sources used to analyze the documents than on the precision of the language in the documents themselves.


Introducing a Graph-based Semantic Layer in Enterprises

@machinelearnbot

Things, not Strings Entity-centric views on enterprise information and all kinds of data sources provide means to get a more meaningful picture about all sorts of business objects. This method of information processing is as relevant to customers, citizens, or patients as it is to knowledge workers like lawyers, doctors, or researchers. People actually do not search for documents, but rather for facts and other chunks of information to bundle them up to provide answers to concrete questions. Strings, or names for things are not the same as the things they refer to. Still, those two aspects of an entity get mixed up regularly to nurture the Babylonian language confusion.