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Top-N recommendations from implicit feedback leveraging linked open data
TLDR
SPrank is presented, a novel hybrid recommendation algorithm able to compute top-N item recommendations from implicit feedback exploiting the information available in the so called Web of Data.
SPrank: Semantic Path-Based Ranking for Top-N Recommendations Using Linked Open Data
TLDR
SPrank is presented, a novel hybrid recommendation algorithm able to compute top-N recommendations exploiting freely available knowledge in the Web of Data and employs DBpedia, a well-known encyclopedic knowledge base in the Linked Open Data cloud, to extract semantic path-based features.
Ranking the Linked Data: The Case of DBpedia
TLDR
A new hybrid methodology to rank resources exploiting the graphbased nature of the underlying RDF structure, context independent semantic relations in the graph and external information sources such as classical search engine results and social tagging systems is proposed.
A system for principled matchmaking in an electronic marketplace
TLDR
This work formalizes general properties a matchmaker should have, then it presents a matchmaking facilitator, compliant with desired properties, that embeds a NeoClassic reasoner, whose structural subsumption algorithm has been modified to allow match categorization into potential and partial, and ranking of matches within categories.
Semantic Wonder Cloud: Exploratory Search in DBpedia
TLDR
Semantic Wonder Cloud is a tool that helps users in knowledge exploration within the DBpedia dataset by adopting a hybrid approach and exploits not only pure semantic connections in the underlying RDF graph but it mixes the meaning of such information with external non-semantic knowledge sources, such as web search engines and tagging systems.
Semantic Matchmaking as Non-Monotonic Reasoning: A Description Logic Approach
TLDR
This work addresses the problem of matchmaking from a knowledge representation perspective, with a formalization based on Description Logics suitable for modeling matchmaking in a logical framework, and presents reasonable algorithms for semantic matchmaking based on the devised inferences.
Concept abduction and contraction in semantic-based P2P environments
TLDR
A revised lightweight version of abduction and contraction algorithms for matchmaking in Description Logics in mobile ad-hoc contexts based on a simplified Bluetooth interaction paradigm is presented.
Semantic-Based Resource Discovery and Orchestration in Home and Building Automation: A Multi-Agent Approach
TLDR
A power-management problem in HBA is presented as a case study to better clarify the proposal and assess its effectiveness and a more flexible multi-agent approach, leveraging semantic-based resource discovery and orchestration for HBA applications is proposed.
A Formal Approach to Ontology-Based Semantic Match of Skills Descriptions
TLDR
An approach to Ontology-Based Semantic Matchmaking between Skills demand and supply, devised as a virtual marketplace of knowledge, overcomes simple subsumption matching and allows match ranking and categorization.
Abductive Matchmaking using Description Logics
TLDR
This work devise suitable definitions of the problem, and show how they can model commonsense reasoning usually employed in analyzing classified announcements having a standardized terminology, and describe a system partially implementing these ideas.
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