Contextualizing Citations for Scientific Summarization using Word Embeddings and Domain Knowledge

Citation texts are sometimes not very informative or in some cases inaccurate by themselves; they need the appropriate context from the referenced paper to reflect its exact contributions. To address this problem, we propose an unsupervised model that uses distributed representation of words as well as domain knowledge to extract the appropriate context from the reference paper. Evaluation results show the effectiveness of our model by significantly outperforming the state-of-the-art. We furthermore demonstrate how an effective contextualization method results in improving citation-based summarization of the scientific articles.Comment: SIGIR 201

Semantic learning webs

By 2020, microprocessors will likely be as cheap and plentiful as scrap paper,scattered by the millions into the environment, allowing us to place intelligent systems everywhere. This will change everything around us, including the nature of commerce, the wealth of nations, and the way we communicate, work, play, and live. This will give us smart homes, cars, TVs , jewellery, and money. We will speak to our appliances, and they will speak back. Scientists also expect the Internet will wire up the entire planet and evolve into a membrane consisting of millions of computer networks, creating an “intelligent planet.” The Internet will eventually become a “Magic Mirror” that appears in fairy tales, able to speak with the wisdom of the human race. Michio Kaku, Visions: How Science Will Revolutionize the Twenty - First Century, 1998 If the semantic web needed a symbol, a good one to use would be a Navaho dream-catcher: a small web, lovingly hand-crafted, [easy] to look at, and rumored to catch dreams; but really more of a symbol than a reality. Pat Hayes, Catching the Dreams, 2002 Though it is almost impossible to envisage what the Web will be like by the end of the next decade, we can say with some certainty that it will have continued its seemingly unstoppable growth. Given the investment of time and money in the Semantic Web (Berners-Lee et al., 2001), we can also be sure that some form of semanticization will have taken place. This might be superficial - accomplished simply through the addition of loose forms of meta-data mark-up, or more principled – grounded in ontologies and formalised by means of emerging semantic web standards, such as RDF (Lassila and Swick, 1999) or OWL (Mc Guinness and van Harmelen, 2003). Whatever the case, the addition of semantic mark-up will make at least part of the Web more readily accessible to humans and their software agents and will facilitate agent interoperability. If current research is successful there will also be a plethora of e-learning platforms making use of a varied menu of reusable educational material or learning objects. For the learner, the semanticized Web will, in addition, offer rich seams of diverse learning resources over and above the course materials (or learning objects) specified by course designers. For instance, the annotation registries, which provide access to marked up resources, will enable more focussed, ontologically-guided (or semantic) search. This much is already in development. But we can go much further. Semantic technologies make it possible not only to reason about the Web as if it is one extended knowledge base but also to provide a range of additional educational semantic web services such as summarization, interpretation or sense-making, structure-visualization, and support for argumentation

Standards and infrastructure for managing experimental metadata

*See also the "related presentation":http://precedings.nature.com/documents/3145/version/1*

We present an infrastructure that leverages synergistic reporting standards and ontologies^1,2,3,4,5^ to create a common structured representation and storage mechanism for experimental metadata from biological and biomedical investigations ranging from simple single-assay studies to complex, methodologically diverse multi-assay studies. 

The infrastructure’s components include: a data capture and editing tool (_ISAcreator_); validator (_ISAvalidator_); database (_BioInvestigation Index_); and converter (_ISAconverter_); and a BioConductor analysis package (_R-ISApackage_). The components are designed for local installation, and can work independently, or as unified system.

View the "public instance":http://www.ebi.ac.uk/bioinvindex running at EBI and/or "download the components":http://isatab.sf.net for your local use.

*References*
1. Taylor CF, Field D, Sansone SA,… Rocca-Serra P et al. (2008) The MIBBI Project. _Nature Biotechnology_ Aug;26(8):889-896. "http://www.mibbi.org":http://www.mibbi.org

2. Smith B, Ashburner M, Rosse C,… Rocca-Serra P, …Sansone SA et al. (2007) The OBO Foundry. _Nature Biotechnology_ Nov;25(11):1251-5. "http://www.obofoundry.org":http://www.obofoundry.org

3. Ontology for Biomedical Investigations (OBI) "http://obi-ontology.org":http://obi-ontology.org 

4. Sansone SA, Rocca-Serra P, Brandizi M,… Taylor CF et al. (2008) The First MGED RSBI (ISA-TAB) Workshop. _OMICS_. Jun;12(2):143-9. "http://isatab.sf.net":http://isatab.sf.net

5. Jones AR, Miller M, Aebersold R,… Sansone SA et al. (2007) The Functional Genomics Experiment model (FuGE). _Nature Biotechnology_ Oct;25(10):1127-1133. "http://fuge.sf.net":http://fuge.sf.ne

Context-based task ontologies for clinical guidelines

Evidence-based medicine relies on the execution of clinical practice guidelines and protocols. A great deal of of effort has been invested in the development of various tools which automate the representation and execution of the recommendations contained within such guidelines and protocols by creating Computer Interpretable Guideline Models (CIGMs). Context-based task ontologies (CTOs), based on standard terminology systems like UMLS, form one of the core components of such a model. We have created DAML+OIL-based CTOs for the tasks mentioned in the WHO guideline for hypertension management, drawing comparisons also with other related guidelines. The advantages of CTOs include: contextualization of ontologies, providing ontologies tailored to specific aspects of the phenomena of interest, dividing the complexity involved in creating ontologies into different levels, providing a methodology by means of which the task recommendations contained within guidelines can be integrated into the clinical practices of a health care set-up

A community based approach for managing ontology alignments

The Semantic Web is rapidly becoming a defacto distributed repository for semantically represented data, thus leveraging on the added on value of the network effect. Various ontology mapping techniques and tools have been devised to facilitate the bridging and integration of distributed data repositories. Nevertheless, ontology mapping can benefitfrom human supervision to increase accuracy of results. The spread of Web 2.0 approaches demonstrate the possibility of using collaborative techniques for reaching consensus. While a number of prototypes for collaborative ontology construction are being developed, collaborative ontology mapping is not yet well investigated. In this paper, we describe a prototype that combines off-the-shelf ontology mapping tools with social software techniques to enable users to collaborate on mapping ontologies

Ontology based annotation of contextualized vital signs

Representing the kinetic state of a patient (posture, motion, and activity) during vital sign measurement is an important part of continuous monitoring applications, especially remote monitoring applications. In contextualized vital sign representation, the measurement result is presented in conjunction with salient measurement context metadata. We present an automated annotation system for vital sign measurements that uses ontologies from the Open Biomedical Ontology Foundry (OBO Foundry) to represent the patient’s kinetic state at the time of measurement. The annotation system is applied to data generated by a wearable personal status monitoring (PSM) device. We demonstrate how annotated PSM data can be queried for contextualized vital signs as well as sensor algorithm configuration parameters