Pierre Andrews

A classification of semantic annotation systems

The Subject-Predicate-Object triple annotation system is now well adopted in the research community, however, it does not always speak to end-users. In fact, explaining all the complexity of semantic annotation systems to laymen can sometime be difficult. We believe that this communication can be simplified by providing a meaningful abstraction of the state of the art in semantic annotation models and thus, in this article, we describe the issue of semantic annotation and review a number of research and end-user tools in the field. Doing so, we provide a clear classification scheme of the features of annotation systems. We then show how this scheme can be used to clarify requirements of end-user use cases and thus simplify the communication between semantic annotation experts and the actual users of this technology.

Semantic disambiguation in folksonomy: a case study

Social annotation systems such as del.icio.us, Flickr and others have gained tremendous popularity among Web 2.0 users. One of the factors of success was the simplicity of the underlying model, which consists of a resource (e.g., a web page), a tag (e.g., a text string), and a user who annotates the resource with the tag. However, due to the syntactic nature of the underlying model, these systems have been criticised for not being able to take into account the explicit semantics implicitly encoded by the users in each tag. In this article we: a) provide a formalisation of an annotation model in which tags are based on concepts instead of being free text strings; b) describe how an existing annotation system can be converted to the proposed model; c) report on the results of such a conversion on the example of a del.icio.us dataset; and d) show how the quality of search can be improved by the semantic in the converted dataset.

Lightweight parsing of classifications into lightweight ontologies

Understanding metadata written in natural language is a premise to successful automated integration of large scale, language-rich, classifications such as the ones used in digital libraries. We analyze the natural language labels within classification by exploring their syntactic structure, we then show how this structure can be used to detect patterns of language that can be processed by a lightweight parser with an average accuracy of 96.82%. This allows for a deeper understanding of natural language metadata semantics, which we show can improve by almost 18% the accuracy of the automatic translation of classifications into lightweight ontologies required by semantic matching, search and classification algorithms.

Argumentative Human Computer Dialogue for Automated Persuasion

Argumentation is an emerging topic in the field of human computer dialogue. In this paper we describe a novel approach to dialogue management that has been developed to achieve persuasion using a textual argumentation dialogue system. The paper introduces a layered management architecture that mixes task-oriented dialogue techniques with chatbot techniques to achieve better persuasiveness in the dialogue.

Sense induction in folksonomies: a review

Folksonomies, often known as tagging systems, such as the ones used on the popular Delicious or Flickr websites, use a very simple Knowledge Organisation System. Users have thus been quick to adopt this system and create extensive annotations on the Web. However, because of the simplicity of the folksonomy model, the semantics of the tags used is not explicit and can only be inferred from their context of use. This is a barrier for the automatic use of such Knowledge Organisation Systems by computers and new techniques have been developed to extract the semantic of the tags. In this article we discuss the drawbacks of some of these approaches and propose a generalization of the different approaches to detect new senses of terms in a folksonomy. Another weak point of the current state of the art in the field is the lack of formal evaluation methodology; we thus propose a novel evaluation framework. We introduce a dataset and evaluation methodology that enable the comparison of results between different approaches to sense induction in folksonomies. Finally we discuss the performances of different approaches to the task of homonymous/polysemous tag detection and synonymous identification.

Using parliamentary open data to improve participation

For a lay-citizen, it is difficult to keep up to date with all the bills that are being discussed and will be voted at each level of the law making bodies of the government (e.g. a parliament, the congress, the senate). Currently, some citizens are very politically active and are taking part in the legislative process, starting petitions and protests to stop or support particular bills. However, the majority of the population is lost and does not care much for the details of the legislative process. They get politically active when journalists and news outlets raise an issue, but this is intrinsically biased towards the medias agenda and not always close to the citizens personal interests. We are researching ways to automatically summarize and simplify the legislative process in a personalized manner for each citizen. In the way that Netflix or Amazon can learn a customers preferences for movies, books and other products, we have been researching methods to build a system that can learn political preferences and topic of interest and can use these to automatically notify citizens when such topics are being discussed in parliament or when a bill is being voted that the citizen would strongly support or oppose. In this paper we present the state of the open data that is currently made available by legislative bodies to bootstrap such a system and we then discuss a particular use case, Cidadão Automatico that we are developing for monitoring the Brazilian legislative bodies.

System Personality and Persuasion in Human-Computer Dialogue

The human-computer dialogue research field has been studying interaction with computers since the early stage of Artificial Intelligence, however, research has often focused on very practical tasks to be completed with the dialogues. A new trend in the field tries to implement persuasive techniques with automated interactive agents; unlike booking a train ticket, for example, such dialogues require the system to show more anthropomorphic qualities. The influences of such qualities in the effectiveness of persuasive dialogue is only starting to be studied. In this article we focus on one important perceived trait of the system: personality, and explore how it influences the persuasiveness of a dialogue system. We introduce a new persuasive dialogue system and combine it with a state of the art personality utterance generator. By doing so, we can control the system’s extraversion personality trait and observe its influence on the user’s perception of the dialogue and its output. In particular, we observe that the user’s extraversion influences their perception of the dialogue and its persuasiveness, and that the perceived personality of the system can affect its trustworthiness and persuasiveness. We believe that theses observations will help to set up guidelines to tailor dialogue systems to the user’s interaction expectations and improve the persuasive interventions.

Adopting semantic annotation systems for corporate portal management: a Telefonica case study

Corporate portals, such as the one used by the Telefónica group, make an important integral part of the enterprise infrastructure, facilitating the creation, sharing, discovery and consumption of enterprise assets through blogs, news, forums, documents and information in general. However, as the amount of data grows, it becomes much more difficult to access the right asset in the precise moment when it is needed. Annotation systems try to address this problem to a certain extent by allowing the users to collaboratively annotate assets using tags so they can be found more easily by reusing these tags in queries. However, this model often falls short due to mismatches in the vocabularies of different users who use synonymous, polysemous, or more specific (or general) terms in tagging and searching. In this paper we: (a) provide a description of the corporate portal of the Telefónica group; (b) define a semantic annotation model that was developed to address the above-mentioned problems; (c) provide details of the implementation of the annotation model for the Telefónica portal; and (d) report the results of an initial evaluation of a concept-based search enabled by the model.

Supporting semantic annotations in Flickr

In this paper we propose an extension to the tripartite folksonomy model to explicitly encode the semantics of tags. This enables stronger semantic services for the user such as search taking into account synonymy and hypernymy in a knowledge organisation system. However, automatic disambiguation is not yet possible and inputting the semantics of tags manually should not be a chore for the users. We thus propose a set of user interfaces features, illustrated in a working uploader for Flickr, that simplify the semantic annotation of photos before their publication. Finally, we discuss the enabling services for such interfaces, thus providing a complete description of the theoretical and practical issues of semantic annotations on Flickr.

Semantic annotation of images on flickr

In this paper we introduce an application that allows its users to have an explicit control on the meaning of tags they use when uploading photos on Flickr. In fact, this application provides to the users an improved interface with which they can add concepts to photos instead of simple free-text tags. They can thus directly provide semantic tags for their photos that can then be used to improve services such as search.