Difference between revisions of "Scooner"
| Line 1: | Line 1: | ||
| + | '''Introduction''' | ||
| + | ---- | ||
| + | The limitations of key word based search are well known in the information retrieval field. | ||
| + | These are more evident in life sciences where most of the reliable scientific information is spread | ||
| + | across biomedical literature in the form of raw text journal articles. Unlike the Web, these journal | ||
| + | articles are devoid of hyper links and multiple key word based searches need to be performed | ||
| + | while aggregating and organizing search results that the user finds interesting. This makes | ||
| + | literature search a tedious task in life sciences. | ||
| − | + | Knowledge-based search systems are proposed as an improvement over conventional search | |
| + | and has gained popularity especially given the availability many expert curated vocabularies | ||
| + | and taxonomies in the biomedical domains. The different classes in a given taxonomy are used to | ||
| + | provide faceted search over articles that contain the instances of these classes. | ||
| + | The taxonomies and other forms of ontologies are mostly static blocks of well accepted consensual | ||
| + | knowledge. Also, most of these standard ontologies have a limited number of predicates (or relationship | ||
| + | types) such as ``part of'' or ``is a''. We believe the search process can benefit from recently published results that are not well | ||
| + | known in the research community and also by relationship types that go beyond the taxonomic ones. | ||
| + | Scooner is a knowledge-based literature search and exploration system that is built upon these intuitive ideas. | ||
| + | We are working on providing more powerful knowledge-based search where recently published results are | ||
| + | computationally extracted and used a background KB to guide the search process. The key here is that | ||
| + | the knowledge-base that guides search is extracted from the universe of literature that is being searched. | ||
| − | + | In Scooner, search is modeled as an interactive process where, besides providing key words, the points of | |
| + | interaction are based on domain specific assertions (or triples) of the form: subject -> predicate -> object (ex: muscarinic activation -> facilitates -> long-term | ||
| + | potentiation). Raw text results are input to a spotter module that annotates them with entities found in the triples used | ||
| + | as background KB. Clicking on annotated entities displays all triples where it participates as a subject or object. Clicking | ||
| + | on the corresponding object/subject would then bring up articles that potentially contain that triple and in most cases | ||
| + | the original abstract from which the triple was extracted is listed in the top 2 or 3 articles. This way the triples can be | ||
| + | browsed in the context of the abstracts in which they were found. | ||
| − | + | Scooner combines these ideas of triple-based search and exploration with persistent search sessions. Users can create search projects and | |
| − | + | store their search history including the abstracts they felt important, triples they found useful, and also collaborate with colleagues. | |
| − | + | Users can also create new meaningful trails by combining individual triples they explore. The workbench in Scooner facilitates a central aggregation of important abstracts imported for further review. The work bench can be filtered to only show only those abstracts that pertain to a selected set of triples or trails. Additionally, collaborative features were incorporated using which users can create persistent search projects, write comments on abstracts they find relevant, and share the (sub) projects with other users on a public dashboard. | |
| + | Currently Scooner's KB comes from the human | ||
| + | performance and cognition project and the literature explored is the set of all abstracts available via PubMed as of Oct 2010. | ||
| + | The knowledge-base is created for the domain of human performance and cognition and is extracted from articles on PubMed published | ||
| + | by Aug 2008. Initial evaluations of Scooner by researchers at the AFRL indicate that Scooner does better than NLM's PubMed search tool. | ||
Revision as of 20:49, 18 February 2011
Introduction
The limitations of key word based search are well known in the information retrieval field. These are more evident in life sciences where most of the reliable scientific information is spread across biomedical literature in the form of raw text journal articles. Unlike the Web, these journal articles are devoid of hyper links and multiple key word based searches need to be performed while aggregating and organizing search results that the user finds interesting. This makes literature search a tedious task in life sciences.
Knowledge-based search systems are proposed as an improvement over conventional search and has gained popularity especially given the availability many expert curated vocabularies and taxonomies in the biomedical domains. The different classes in a given taxonomy are used to provide faceted search over articles that contain the instances of these classes. The taxonomies and other forms of ontologies are mostly static blocks of well accepted consensual knowledge. Also, most of these standard ontologies have a limited number of predicates (or relationship types) such as ``part of or ``is a. We believe the search process can benefit from recently published results that are not well known in the research community and also by relationship types that go beyond the taxonomic ones. Scooner is a knowledge-based literature search and exploration system that is built upon these intuitive ideas. We are working on providing more powerful knowledge-based search where recently published results are computationally extracted and used a background KB to guide the search process. The key here is that the knowledge-base that guides search is extracted from the universe of literature that is being searched.
In Scooner, search is modeled as an interactive process where, besides providing key words, the points of interaction are based on domain specific assertions (or triples) of the form: subject -> predicate -> object (ex: muscarinic activation -> facilitates -> long-term potentiation). Raw text results are input to a spotter module that annotates them with entities found in the triples used as background KB. Clicking on annotated entities displays all triples where it participates as a subject or object. Clicking on the corresponding object/subject would then bring up articles that potentially contain that triple and in most cases the original abstract from which the triple was extracted is listed in the top 2 or 3 articles. This way the triples can be browsed in the context of the abstracts in which they were found.
Scooner combines these ideas of triple-based search and exploration with persistent search sessions. Users can create search projects and store their search history including the abstracts they felt important, triples they found useful, and also collaborate with colleagues. Users can also create new meaningful trails by combining individual triples they explore. The workbench in Scooner facilitates a central aggregation of important abstracts imported for further review. The work bench can be filtered to only show only those abstracts that pertain to a selected set of triples or trails. Additionally, collaborative features were incorporated using which users can create persistent search projects, write comments on abstracts they find relevant, and share the (sub) projects with other users on a public dashboard. Currently Scooner's KB comes from the human performance and cognition project and the literature explored is the set of all abstracts available via PubMed as of Oct 2010. The knowledge-base is created for the domain of human performance and cognition and is extracted from articles on PubMed published by Aug 2008. Initial evaluations of Scooner by researchers at the AFRL indicate that Scooner does better than NLM's PubMed search tool.
