ICP - Intelligent components producing and consuming knowledge and data

Intelligent components, by which we mean agents, processes and services, are characterised by complex decision making in order to achieve some objectives. Usually this activity is based on knowledge about the agents themselves, the environment in which they are situated and on the communication patterns they engage in. Often the knowledge needed to make complex decisions is owned by the agent, however with novel models of interactions especially within open environments, this knowledge needs to be shared in order to support opportunistic collaboration between autonomous disparate entities. Ontologies play a pivotal role in enabling agents to share knowledge.
Ontologies aim to capture consensual knowledge and they may be reused and shared across applications and by different stakeholders. A growing number of ontologies are already available to formally and explicitly represent semantics in order for different processes to produce and consume knowledge and data in knowledge-intensive applications. Ontologies can model knowledge about data and about processes, both general and particular ones. Processes can be described by their starting and stopping points and by the kinds of changes that take place in between. However, this modelling area has not been researched and exploited so much in comparison with the progress made in developing ontologies for representing data.

More recently the Linked Data initiative has spun a plethora of datasets that are published and accessed on the Web and whose content is formally captured by schemas and ontologies, thus promoting the ability to link data from diverse domains. The ability to integrate and perform some form of analysis over this data is becoming increasingly important in applications that make use of data and knowledge in order to achieve a given task.

The type of environment in which the agents or services are situated poses constraints on the type and the way knowledge is used. Open environments like the web require the ability to share data and knowledge in a seamless opportunistic fashion with very little or no assumption on the way the content is modelled and represented. The only assumption is that knowledge and data are represented according to some common format, but their content and the constraints on their use are not assumed to be known a priori.

Two crucial questions arise given the context we outlined above: (1) “How can semantics help to represent processes and data?” and (2) “How can the use of semantics (knowledge and data) support intelligent decision making especially when little is known of the environment and/or other components?”.

The answer to the first question requires developing ontologies, according to some well founded methodology, that facilitate sharing knowledge about both data and processes in open environments. The answer to the second question refers to how agents ad services can make use of the knowledge encoded in ontologies and the data in order to perform flexible, opportunistic and autonomous decision making in order to meet particular established objectives (e.g., the ability to learn, to make a plan or decision). These objectives often require that agents are able to interact with other agents, perceive and respond in a timely fashion, through learning or using knowledge, to changes occurring in their environment to satisfy their objectives.

Semantic technologies, including linked data, render dynamic, heterogeneous, distributed, shared content equally accessible to both humans and software agents. Currently, semantic technologies are sufficiently established to build intelligent applications that take advantage of the semantics associated with web resources. Intelligent agents can use Semantic Web content to gather and aggregate knowledge, reason and infer new results towards achieving their goals and generating new knowledge. Semantic technologies, especially ontologies that represent processes and data, can therefore help intelligent agents in performing rational decision making and that these types of agents can benefit from the reuse and sharing of knowledge encoded in ontologies and linked datasets.

This special session aims at discussing the synergy between semantic technologies, including linked data, and intelligent software components (agents and processes/services). Topics of interest span a wide spectrum in both theory and practice of ontology development methodologies and guidelines, knowledge management, linked data creation, data validation, context-aware intelligent agents, mobile agents, multi-agent systems, collaboration and cooperation, information retrieval agent communities, knowledge enabled services, recommender systems, and so on.

Topics

Agent-based Linked (Open) Data
Ontologies and agents for specific areas and domains
Ontology-based multi-agent systems
Ontology engineering for open agent environments
Ontology integration (mapping, matching, alignment, merging)
Ontology reuse methods and approaches
Ontology search and repositories
Crawling, caching and querying Linked Data
Evaluation of semantic technologies and agents
Languages, tools, and methodologies for representing and managing semantic data
Guidelines, methods, and tools for Linked Data engineering and their use in agent systems
Semantic-aware intelligent agents/processes/services
Technologies for the Semantic Web and for Linked Data, including languages, tools and infrastructures for knowledge representation and rational agents

Organizers

Mari Carmen Suárez-Figueroa - Ontology Engineering Group (OEG), Universidad Politécnica de Madrid (Spain)
Valentina Tamma - University of Liverpool (United Kingdom)