ACRONYM

With the advent of online social networks and User-Generated Content (UGC), the social Web is experiencing an explosion of audio-visual data. However, the usefulness of the collected data is in doubt, given that the means of retrieval are limited by the semantic gap between them and people’s perceived understanding of the memories they represent. Whereas machines interpret UGC media as series of binary audio-visual data, humans perceive the context under which the content is captured and the people, places, and events represented. The Annotation CReatiON for Your Media (ACRONYM) framework addresses the semantic gap by supporting the creation of a layer of explicit machine-interpretable meaning describing UGC context. This paper presents an overview of a use case of ACRONYM for semantic annotation of personal photographs. The authors define a set of recommendation algorithms employed by ACRONYM to support the annotation of generic UGC multimedia. This paper introduces the context metrics and combination methods that form the recommendation algorithms used by ACRONYM to determine the people represented in multimedia resources. For the photograph annotation use case, these result in an increase in recommendation accuracy. Context-based algorithms provide a cheap and robust means of UGC media annotation that is compatible with and complimentary to content-recognition techniques.

Semi-Automatic Ontology Construction by Exploiting Functional Dependencies and Association Rules

This paper presents a novel semi-automatic approach to construct conceptual ontologies over structured data by exploiting both the schema and content of the input dataset. It effectively combines two well-founded database and data mining techniques, i.e., functional dependency discovery and association rule mining, to support domain experts in the construction of meaningful ontologies, tailored to the analyzed data, by using Description Logic (DL). To this aim, functional dependencies are first discovered to highlight valuable conceptual relationships among attributes of the data schema (i.e., among concepts). The set of discovered correlations effectively support analysts in the assertion of the Tbox ontological statements (i.e., the statements involving shared data conceptualizations and their relationships). Then, the analyst-validated dependencies are exploited to drive the association rule mining process. Association rules represent relevant and hidden correlations among data content and they are used to provide valuable knowledge at the instance level. The pushing of functional dependency constraints into the rule mining process allows analysts to look into and exploit only the most significant data item recurrences in the assertion of the Abox ontological statements (i.e., the statements involving concept instances and their relationships).

A Theory of Social Agentivity and its Integration into the Descriptive Ontology for Linguistic and Cognitive Engineering

The agentivity of social entities has posed problems for ontologies of social phenomena, especially in the Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) designed for use in the semantic web. This article elucidates a theory by which physical and social objects can take action, but that also recognizes the different ways in which they act. It introduces the “carry” relationship, through which social actions can occur when a physical action is taken in the correct circumstances. For example, the physical action of a wave of a hand may carry the social action of saying hello when entering a room. This article shows how a system can simultaneously and in a noncontradictory manner handle statements and queries in which both nonphysical social agents and physical agents take action by the carry relationship and the use of representatives. A revision of DOLCE’s taxonomic structure of perdurants is also proposed. This revision divides perdurants into physical and nonphysical varieties at the same ontological level at which endurants are so divided.

Semantics Discovery via Human Computation Games

The effective acquisition of (semantic) metadata is crucial for many present day applications. Games with a purpose address this issue by transforming computational problems into computer games. The authors present a novel approach to metadata acquisition via Little Search Game (LSG) – a competitive web search game, whose purpose is the creation of a term relationship network. From a player perspective, the goal is to reduce the number of search results returned for a given search term by adding negative search terms to a query. The authors describe specific aspects of the game’s design, including player motivation and anti-cheating issues. The authors have performed a series of experiments with Little Search Game, acquired real-world player input, gathered qualitative feedback from the players, constructed and evaluated term relationship network from the game logs and examined the types of created relationships.

AL-QuIn

Onto-Relational Learning is an extension of Relational Learning aimed at accounting for ontologies in a clear, well-founded and elegant manner. The system QuIn supports a variant of the frequent pattern discovery task by following the Onto-Relational Learning approach. It takes taxonomic ontologies into account during the discovery process and produces descriptions of a given relational database at multiple granularity levels. The functionalities of the system are illustrated by means of examples taken from a Semantic Web Mining case study concerning the analysis of relational data extracted from the on-line CIA World Fact Book.

An Enhanced Semantic Layer for Hybrid Recommender Systems

Recommender systems have achieved success in a variety of domains, as a means to help users in information overload scenarios by proactively finding items or services on their behalf, taking into account or predicting their tastes, priorities, or goals. Challenging issues in their research agenda include the sparsity of user preference data and the lack of flexibility to incorporate contextual factors in the recommendation methods. To a significant extent, these issues can be related to a limited description and exploitation of the semantics underlying both user and item representations. The authors propose a three-fold knowledge representation, in which an explicit, semantic-rich domain knowledge space is incorporated between user and item spaces. The enhanced semantics support the development of contextualisation capabilities and enable performance improvements in recommendation methods. As a proof of concept and evaluation testbed, the approach is evaluated through its implementation in a news recommender system, in which it is tested with real users. In such scenario, semantic knowledge bases and item annotations are automatically produced from public sources.

A Modal Defeasible Reasoner of Deontic Logic for the Semantic Web

Defeasible logic is a non-monotonic formalism that deals with incomplete and conflicting information, whereas modal logic deals with the concepts of necessity and possibility. These types of logics play a significant role in the emerging Semantic Web, which enriches the available Web information with meaning, leading to better cooperation between end-users and applications. Defeasible and modal logics, in general, and, particularly, deontic logic provide means for modeling agent communities, where each agent is characterized by its cognitive profile and normative system, as well as policies, which define privacy requirements, access permissions, and individual rights. Toward this direction, this article discusses the extension of DR-DEVICE, a Semantic Web-aware defeasible reasoner, with a mechanism for expressing modal logic operators, while testing the implementation via deontic logic operators, concerned with obligations, permissions, and related concepts. The motivation behind this work is to develop a practical defeasible reasoner for the Semantic Web that takes advantage of the expressive power offered by modal logics, accompanied by the flexibility to define diverse agent behaviours. A further incentive is to study the various motivational notions of deontic logic and discuss the cognitive state of agents, as well as the interactions among them.

Concept Induction in Description Logics Using Information-Theoretic Heuristics

This paper presents an approach to ontology construction pursued through the induction of concept descriptions expressed in Description Logics. The author surveys the theoretical foundations of the standard representations for formal ontologies in the Semantic Web. After stating the learning problem in this peculiar context, a FOIL-like algorithm is presented that can be applied to learn DL concept descriptions. The algorithm performs a search through a space of candidate concept definitions by means of refinement operators. This process is guided by heuristics that are based on the available examples. The author discusses related theoretical aspects of learning with the inherent incompleteness underlying the semantics of this representation. The experimental evaluation of the system DL-Foil, which implements the learning algorithm, was carried out in two series of sessions on real ontologies from standard repositories for different domains expressed in diverse description logics.

Data Linking for the Semantic Web

By specifying that published datasets must link to other existing datasets, the 4th linked data principle ensures a Web of data and not just a set of unconnected data islands. The authors propose in this paper the term data linking to name the problem of finding equivalent resources on the Web of linked data. In order to perform data linking, many techniques were developed, finding their roots in statistics, database, natural language processing and graph theory. The authors begin this paper by providing background information and terminological clarifications related to data linking. Then a comprehensive survey over the various techniques available for data linking is provided. These techniques are classified along the three criteria of granularity, type of evidence, and source of the evidence. Finally, the authors survey eleven recent tools performing data linking and we classify them according to the surveyed techniques.

Numeric Query Answering on the Web

Query answering usually assumes that the asker is looking for a single correct answer to the question. When retrieving a textual answer this is often the case, but when searching for numeric answers, there are additional considerations. In particular, numbers often have units associated with them, and the asker may not care whether the raw answer is in feet or meters. Also, numbers usually denote a precision. In a few cases, the precision may be explicit, but normally, there is an implied precision associated with every number. Finally, an association between different reliability levels to different sources can be made. In this paper, the authors experimentally show that, in the context of conflicting answers from multiple sources, numeric query accuracy can be improved by taking advantage of units, precision, and the reliability of sources.