Search

The amount of information available on the Web has grown considerably in recent years, leading to the need to structure it in order to access it in a quick and accurate way. In order to develop techniques to automate the structuring process, the Knowledge Base Population (KBP) track of the Text Analysis Conference (TAC) was created. This forum aims to encourage research in automated systems capable of capturing knowledge in unstructured information. One of the tasks proposed in the context of the KBP track is named entity linking, and its goal is to link named entities mentioned in a document to instances in a reference knowledge base built from Wikipedia. This paper focuses on the entity linking task in the context of KBP 2010, where two different varieties of this task were considered, depending on whether the use of the text from Wikipedia was allowed or not. Specifically, the paper proposes a set of modifications to a system that participated in KBP 2010, named WikiIdRank, in order to improve its performance. The different modifications were evaluated in the official KBP 2010 corpus, showing that the best combination increases the accuracy of the initial system in a 7.04%. Though the resultant system, named WikiIdRank++, is unsupervised and does not take advantage of Wikipedia text, a comparison with other approaches in KBP indicates that the system would rank as 4th (out of 16) in the global comparison, outperforming other approaches that use human supervision and take advantage of Wikipedia textual contents. Furthermore, the system would rank as 1st in the category of systems that do not use Wikipedia text. [ABSTRACT FROM AUTHOR]

Scientific practice has been rapidly evolving in the last years under the pressure of developments in computer science and technology. In this paper we present some of the results of our research activity at the boundary between computer science and philosophy of science started in 1997 under Marco Somalvico's impulse and guidance. In particular, we discuss two roles that multiagent systems can play in scientific discovery. From the one hand, they can support scientific practice; from the other hand, they can represent scientific results. The theoretical framework presented in this paper is exemplified in concrete by illustrating specific implemented systems, both taken from the literature and developed by ourselves. [ABSTRACT FROM AUTHOR]

Clustering has been applied in a wide variety of disciplines and has also been utilized in many scientific areas. Usually, clustering algorithms construct either clusters of rows or clusters of columns of the input data matrix. Biclustering is a methodology where biclusters are formed by both a subset of rows and a subset of columns, such that objects represented by the first are the most similar to each other when compared over the latter. In this paper, we introduce a new biclustering technique, based on association rule mining, which can support different well-known biclustering models proposed in the literature. Experimental tests demonstrate the accuracy and efficiency of the proposed technique with respect to well known related ones. [ABSTRACT FROM AUTHOR]

The evaluation of subjective aspects of HCI, such as human-likeness, likeability or users' emotions towards computers is still quite a neglected issue, especially in the field of non-task oriented conversational systems (chatterbots). In this paper we try to bridge this gap by proposing a new methodology of evaluation. The methods presented were tested in our research on humor-equipped chatterbots. We describe them in details, discuss their drawbacks and usability. In one of the presented methods we used an emotiveness analysis system, which itself can be considered an AI tool, as it was used to detect users' emotions towards conversational systems, and to perform their automatic evaluation. We also propose some methods that we have not used yet, which, however, seem applicable in this field, such as brain scanning techniques. Finally, we give some ideas that should be addressed in the future. [ABSTRACT FROM AUTHOR]

This paper presents an original dynamic subsumption technique for Boolean CNF formulae. It exploits simple and sufficient conditions to detect, during conflict analysis, clauses from the formula that can be reduced by subsumption. During the learnt clause derivation, and at each step of the associated resolution process, checks for backward subsumption between the current resolvent and clauses from the original formula are efficiently performed. The resulting method allows the dynamic removal of literals from the original clauses. Experimental results show that the integration of our dynamic subsumption technique within the state-of-the-art SAT solvers Minisat and Rsat particularly benefits to crafted problems. [ABSTRACT FROM AUTHOR]

Life sciences, and biology in particular, are heavily impacted by the development of methods for data collection and data analysis. Taking advantage of the availability of data, modeling biological effects is becoming more and more popular and relevant in life sciences, due to the diffuse and wide use of information technology (IT) tools. IT is increasing the availability of models, the horizon and complexity of modeling activities, reaching new targets, and boosting "virtuality". Modeling, as any inductive activity, originates from two needs: to predict future outcomes using previous experience, and to explain observations, or in other terms to infer knowledge. In this paper we analyze criteria, problems, possibilities and advancements which indicate that the time for e-modeling in biology and other life sciences is ripe. To do this, we take as example a particular field of biological sciences, the computational toxicity (CT) of chemicals, and its usual QSAR (Quantitative Structure Activity Relationships) approach. The open possibilities are evaluated, including a reshaping of the interface between toxicology, chemistry and computer science. The epistemological problem about "what models are" is approached in a pragmatic sense. [ABSTRACT FROM AUTHOR]

This paper presents a fast Neural Network algorithm, in which the step is regarded as the function of the error and the output function of network node, and weight is calculated by different step. By adopting the fast NN algorithm, we developed a speaker-independent speech recognition system. The experiment shows that the new algorithm is over 10 times faster than the traditional BP algorithm and has better performance and spreading ability. [ABSTRACT FROM AUTHOR]