Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N

Joint Video and Text Parsing for Understanding Events and Answering Queries

We propose a framework for parsing video and text jointly for understanding events and answering user queries. Our framework produces a parse graph that represents the compositional structures of spatial information (objects and scenes), temporal information (actions and events) and causal information (causalities between events and fluents) in the video and text. The knowledge representation of our framework is based on a spatial-temporal-causal And-Or graph (S/T/C-AOG), which jointly models possible hierarchical compositions of objects, scenes and events as well as their interactions and mutual contexts, and specifies the prior probabilistic distribution of the parse graphs. We present a probabilistic generative model for joint parsing that captures the relations between the input video/text, their corresponding parse graphs and the joint parse graph. Based on the probabilistic model, we propose a joint parsing system consisting of three modules: video parsing, text parsing and joint inference. Video parsing and text parsing produce two parse graphs from the input video and text respectively. The joint inference module produces a joint parse graph by performing matching, deduction and revision on the video and text parse graphs. The proposed framework has the following objectives: Firstly, we aim at deep semantic parsing of video and text that goes beyond the traditional bag-of-words approaches; Secondly, we perform parsing and reasoning across the spatial, temporal and causal dimensions based on the joint S/T/C-AOG representation; Thirdly, we show that deep joint parsing facilitates subsequent applications such as generating narrative text descriptions and answering queries in the forms of who, what, when, where and why. We empirically evaluated our system based on comparison against ground-truth as well as accuracy of query answering and obtained satisfactory results

A semantic and agent-based approach to support information retrieval, interoperability and multi-lateral viewpoints for heterogeneous environmental databases

PhDData stored in individual autonomous databases often needs to be combined and interrelated. For example, in the Inland Water (IW) environment monitoring domain, the spatial and temporal variation of measurements of different water quality indicators stored in different databases are of interest. Data from multiple data sources is more complex to combine when there is a lack of metadata in a computation forin and when the syntax and semantics of the stored data models are heterogeneous. The main types of information retrieval (IR) requirements are query transparency and data harmonisation for data interoperability and support for multiple user views. A combined Semantic Web based and Agent based distributed system framework has been developed to support the above IR requirements. It has been implemented using the Jena ontology and JADE agent toolkits. The semantic part supports the interoperability of autonomous data sources by merging their intensional data, using a Global-As-View or GAV approach, into a global semantic model, represented in DAML+OIL and in OWL. This is used to mediate between different local database views. The agent part provides the semantic services to import, align and parse semantic metadata instances, to support data mediation and to reason about data mappings during alignment. The framework has applied to support information retrieval, interoperability and multi-lateral viewpoints for four European environmental agency databases. An extended GAV approach has been developed and applied to handle queries that can be reformulated over multiple user views of the stored data. This allows users to retrieve data in a conceptualisation that is better suited to them rather than to have to understand the entire detailed global view conceptualisation. User viewpoints are derived from the global ontology or existing viewpoints of it. This has the advantage that it reduces the number of potential conceptualisations and their associated mappings to be more computationally manageable. Whereas an ad hoc framework based upon conventional distributed programming language and a rule framework could be used to support user views and adaptation to user views, a more formal framework has the benefit in that it can support reasoning about the consistency, equivalence, containment and conflict resolution when traversing data models. A preliminary formulation of the formal model has been undertaken and is based upon extending a Datalog type algebra with hierarchical, attribute and instance value operators. These operators can be applied to support compositional mapping and consistency checking of data views. The multiple viewpoint system was implemented as a Java-based application consisting of two sub-systems, one for viewpoint adaptation and management, the other for query processing and query result adjustment

Proceedings of The Multi-Agent Logics, Languages, and Organisations Federated Workshops (MALLOW 2010)

http://ceur-ws.org/Vol-627/allproceedings.pdfInternational audienceMALLOW-2010 is a third edition of a series initiated in 2007 in Durham, and pursued in 2009 in Turin. The objective, as initially stated, is to "provide a venue where: the cost of participation was minimum; participants were able to attend various workshops, so fostering collaboration and cross-fertilization; there was a friendly atmosphere and plenty of time for networking, by maximizing the time participants spent together"

Explorations in graphical argumentation:The use of external representations of argumentation in collaborative problem solving.

Van Bruggen, J. M. (2003). Explorations in graphical argumentation The use of external representations of argumentation in collaborative problem solving. Unpublished doctoral dissertation, Open University of the Netherlands. The Netherlands

Bio-JOIE: Joint Representation Learning of Biological Knowledge Bases

The widespread of Coronavirus has led to a worldwide pandemic with a high mortality rate. Currently, the knowledge accumulated from different studies about this virus is very limited. Leveraging a wide-range of biological knowledge, such as gene ontology and protein-protein interaction (PPI) networks from other closely related species presents a vital approach to infer the molecular impact of a new species. In this paper, we propose the transferred multi-relational embedding model Bio-JOIE to capture the knowledge of gene ontology and PPI networks, which demonstrates superb capability in modeling the SARS-CoV-2-human protein interactions. Bio-JOIE jointly trains two model components. The knowledge model encodes the relational facts from the protein and GO domains into separated embedding spaces, using a hierarchy-aware encoding technique employed for the GO terms. On top of that, the transfer model learns a non-linear transformation to transfer the knowledge of PPIs and gene ontology annotations across their embedding spaces. By leveraging only structured knowledge, Bio-JOIE significantly outperforms existing state-of-the-art methods in PPI type prediction on multiple species. Furthermore, we also demonstrate the potential of leveraging the learned representations on clustering proteins with enzymatic function into enzyme commission families. Finally, we show that Bio-JOIE can accurately identify PPIs between the SARS-CoV-2 proteins and human proteins, providing valuable insights for advancing research on this new disease.Comment: ACM BCB 2020, Best Student Pape

The use of data-mining for the automatic formation of tactics

This paper discusses the usse of data-mining for the automatic formation of tactics. It was presented at the Workshop on Computer-Supported Mathematical Theory Development held at IJCAR in 2004. The aim of this project is to evaluate the applicability of data-mining techniques to the automatic formation of tactics from large corpuses of proofs. We data-mine information from large proof corpuses to find commonly occurring patterns. These patterns are then evolved into tactics using genetic programming techniques