Semantic similarity in heterogeneous ontologies

AI*IA 2011: Artificial Intelligence Around Man and Beyond

Osborne

2011

Self Cite

Abstract. In this paper we address the problem of propagating user interests in ontology-based user models. Our ontology-based user model (OBUM) is devised as an overlay over the domain ontology. Using ontologies as the basis of the user profile allows the initial user behavior to be matched with existing concepts in the domain ontology. Such ontological approach to user profiling has been proven successful in addressing the cold-start problem in recommender systems, since it allows for propagation from a small number of initial concepts to other related domain concepts by exploiting the ontological structure of the domain. The main contribution of the paper is the novel algorithm for propagation of user interests which takes into account i) the ontological structure of the domain and, in particular, the level at which each domain item is found in the ontology; ii) the type of feedback provided by the user, and iii) the amount of past feedback provided for a certain domain object.

Section: Discussionmentioning

confidence: 99%

Propagating User Interests in Ontology-Based User Model

Cena

AI*IA 2011: Artificial Intelligence Around Man and Beyond

Osborne

2011

Self Cite

“…-A pertinence module, which evaluates the pertinence between a newly inserted fact and those existing in the repository. The measure of pertinence we use is introduced in [14] and is based on the friendship between involved people, on the colocation between facts (co-occurrence in space and time), and on semantic similarity between the mentioned entities, computed using the distance based approach proposed in [6].…”

Section: Telleatmentioning

confidence: 99%

“…-different facts use different predicates taken from the predicate ontology to express their content; -each fact has only one predicate since every fact can be decomposed into more facts with only one predicate; -each predicate has a set of associated values for different role fillers; -each role might have 0 or more role fillers; -the semantic similarity between two predicates or two role fillers is calculated using a measure presented in [6]; -co-location estimates the possibility for the actors to meet while performing the described actions.…”

Section: Server-side Architecturementioning

confidence: 99%

“…Within this framework, we proposed an approach to foster collective storytelling [14], devising a structured language and representational form to describe users' story fragments, investigating measures that capture similarity [6] and pertinence relations between them, and studying linking patterns that can be used to thread fragments into story-like narratives.…”

Section: Introductionmentioning

confidence: 99%

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TellEat: Sharing Experiences on the Move

Chiabrando

Furnari

Human-Computer Interaction. Applications and Services

et al. 2014

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Abstract. In a context where, due to the proliferation of mobile devices, virtual social environments on the Web are taking up a very concrete role in the way people experience their surroundings, the Future Internet seems to be headed toward a mixture of Social Web, Semantic Web and Augmented Reality. As a part of a larger project that aims at building a social network of both people and things, we designed and developed TellEat, an iPhone-based application that allows users in mobility to share facts concerning people or objects that participate in the social network, and to discover pertinent events that have been told by others. In this paper we discuss both the client application, with the interaction model and interface metaphors that have been designed to make the experience as playful as possible for users, and the server-side services that provide the necessary knowledge and reasoning mechanisms. We also present the results of preliminary tests with users.

“…Further improvement of distance-based propagation can be obtained with conceptual distance where the main idea is to modify the lengths of the edges in the ontology graph, as initially proposed in [5]. First, the set of relevant concepts is determined.…”

Section: Propagating User Interest Valuesmentioning

confidence: 99%

Granular Semantic User Similarity in the Presence of Sparse Data

Osborne

AI*IA 2013: Advances in Artificial Intelligence

Cena

2013

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Abstract. Finding similar users in social communities is often challenging, especially in the presence of sparse data or when working with heterogeneous or specialized domains. When computing semantic similarity among users it is desirable to have a measure which allows to compare users w.r.t. any concept in the domain. We propose such a technique which reduces the problems caused by data sparsity, especially in the cold start phase, and enables granular and contextbased adaptive suggestions. It allows referring to a certain set of most similar users in relation to a particular concept when a user needs suggestions about a certain topic (e.g. cultural events) and to a possibly completely different set when the user is interested in another topic (e.g. sport events). Our approach first uses a variation of the spreading activation technique to propagate the users' interests on their corresponding ontology-based user models, and then computes the concept-biased cosine similarity (CBC similarity), a variation of the cosine similarity designed for privileging a particular concept in an ontology. CBC similarity can be used in many adaptation techniques to improve suggestions to users. We include an empirical evaluation on a collaborative filtering algorithm, showing that the CBC similarity works better than the cosine similarity when dealing with sparse data.