Harnessing Publicly Available Factual Data in the Analytical Process

Transcription

1 June 14, 2012 Harnessing Publicly Available Factual Data in the Analytical Process by Benson Margulies, CTO We put the World in the World Wide Web

2 ABOUT BASIS TECHNOLOGY Basis Technology provides so ware solu ons for text analy cs, informa on retrieval, digital forensics, and iden ty resolu on in over forty languages. Our Rose e linguis cs pla orm is a widely used suite of interoperable components that power search, business intelligence, e-discovery, social media monitoring, financial compliance, and other enterprise applica ons. Our linguis cs team is at the forefront of applied natural language processing using a combina on of sta s cal modeling, expert rules, and corpus-derived data. Our forensics team pioneers be er, faster, and cheaper techniques to extract forensic evidence, keeping government and law enforcement ahead of exponen al growth of data storage volumes. So ware vendors, content providers, financial ins tu ons, and government agencies worldwide rely on Basis Technology s solu ons for Unicode compliance, language iden fica on, mul lingual search, en ty extrac on, name indexing, and name transla on. Our products and services are used by over 250 major firms, including Cisco, EMC, Exalead/Dassault Systems, Hewle -Packard, Microso, Oracle, and Symantec. Our text analysis products are widely used in the U.S. defense and intelligence industry by such firms as CACI, Lockheed Mar n, Northrop Grumman, SAIC, and SRI. We are the top provider of mul lingual technology to web and e-commerce search engines, including Amazon.com, Bing, Google, and Yahoo!. Company headquarters are in Cambridge, Massachuse s, with branch offices in San Francisco, Washington, London, and Tokyo. For more informa on, visit Basis Technology Corpora on. Basis Technology, Geoscope, Odyssey Digital Forensics, Rose e, and We put the World in the World Wide Web are registered trademarks of Basis Technology Corpora on. All other trademarks, service marks, and logos used in this document are the property of their respec ve owners. ( )

3 Analysts need to find relevant informa on. More and more informa on is available to them, but the tools to locate and correlate it to the problem at hand have not kept pace. Full text search is a powerful tool, but as the volume of informa on grows, it becomes increasingly difficult to provide analysts with relevant informa on purely by Googling. When users enter search terms, they are overwhelmed by the number of results. Further, some of the mechanisms that Google uses to achieve high relevancy are not applicable to intelligence analysis; no one is adding links between the documents. One avenue for improving the results for analysts is to use pre-exis ng informa on, or facts, to help organize, filter, and explore document sets. Facts can be as simple as Charles Schwab is a person or as complex as a record of a transac on. Facts allow a system to group and filter informa on to improve the relevancy of results. In the simplest case, if the user is searching for informa on about Charles Schwab the company, they don t want to read informa on about Charles Schwab the person. There are two sources of facts wai ng to be exploited. First, there are structured datasets, such as dbpedia, freebase, and GeoNames. Second, there are textual resources, such as the Wikipedia itself. These sources contain very large amounts of uncontroversial, factual informa on. Analysts need access to this informa on, and systems can use this informa on to improve the relevance and organiza on of informa on retrieved from other resources. There are many other poten al sources of facts. Depending on their provenance, they may be more or less trustworthy or relevant. Over the years, there have been many efforts to create data structures that represent knowledge and to use them for informa on retrieval. Efforts along these lines have o en failed due to difficulty of addressing all of the hard problems related to data provenance and reliability, large-scale system design, user interface design, and combining structured fact data with the unstructured document content. This paper iden fies some achievable goals: a set of func onality that Basis Technology believes is within prac cal reach by a combina on of open source data, open source so ware, and Basis Technology s proprietary components. Since facts are anchored in named en es (people, places, and organiza ons), we propose to start by crea ng a name portal that offers analysts high-relevance informa on about named en es in the world, deriving the informa on returned from publically-available facts, Wikipedia, and news. ACHIEVABLE GOALS FOR PROTOTYPE CONSTRUCTION In this prototype, Basis Technology proposes to build a web-based, cloud-scaled portal. Users of this portal will enter names of people, places, and organiza ons, wri en in the La n alphabet or other script. In return, the portal will present results, including structured facts (linked data) and unstructured text hits of documents that men on the name. The prototype will achieve enhanced relevancy through combining search results on structured facts with plain full-text results. Advanced result filtering capabili es are enabled by leveraging the union of the factual and textual data. Further, the system will include basic visualiza on of rela onships induced from facts. The system will deploy analy cs to allow measurement of the usefulness of the system. By logging queries and clicks, user behavior can also be observed. Our preferred assump on is that the system can be exposed to actual users and the logs can be collected. In an ideal world, a controlled Harnessing Publicly Available Factual Data in the Analy cal Process 3

4 experiment comparing the system with and without the important features would be performed. However, even in the more likely scenario of users working with a control version of the system, ques ons can be framed and informa on gathered that will advance the full prototype. It will be important to gather and use actual data for searching to demonstrate that adding the facts to the analysis improves the results. Online news from RSS fields will be used for this purpose. BASIC COMPONENTS AND APPROACH For input data, the prototype system will exploit Wikipedia, dbpedia (the structured form of Wikipedia), and the publicly available Fact Book. The system will store facts as RDF triples in an RDF store. To control costs, an open source triple-store will be incorporated. Full-text indices will be built on the plain text using Apache Lucene, Apache Solr, or, perhaps, an expected open-source cloud-scale alterna ve from another agency within the Defense and Intelligence Community. Addi onal facts will be extracted from the textual content (Wikipedia and the Fact Book) using the Rose e En ty Extractor (REX), combined with some of our internal research prototypes for rela onship extrac on, document clustering, and cross-document co-reference resolu on (coref). Coref is a cri cal enabling technology in linking the factual and non-factual data. It is also an example of a capability that has remained tantalizingly just out of reach in terms of useful deployment in an opera onal environment. Basis Technology is already inves ng building a prac cally-deployable implementa on and would look forward to refining cross-document coref based on the experience of applying it in this effort. The Rose e Name Indexer (RNI) will be used to provide fuzzy name search capabili es and federate the results of name queries with full-text queries. Several approaches to searching facts from user input of simple text will be explored. These start from retrieving the facts that bear upon an en ty from the name index, and expand to experimen ng with trea ng facts as input to algorithms such as locale-sensi ve hashing. The final set of selected approaches will be determined a er some review of academic work in the area. The en re prototype system will be structured as a set of loosely-coupled components. The overall design goal is to func on in the cloud however, limita ons of me and funding might push the ini al implementa on to focus on demonstra ng the value the prototype brings to the analyst as opposed to large scale cloud deployment. All the func onality of the system will be exposed as REST-ful services that are suitable for use by other automated processes or user interfaces. The user interfaces will be built within the guidelines of the Ozone Widget Layer. 4 Harnessing Publicly Available Factual Data in the Analy cal Process

5 OPTIONS/FUTURE DIRECTIONS If this ini al strategy and approach works, the most important con nua on will be progressive refinement. The use of log analy cs should indicate if the system is, in fact, helping its users. The following bullets describe addi onal possible work areas related to the basic prototype: The core accuracy of named en ty extrac on is cri cal to the success of this approach on a very large scale. The accuracy can be improved by feeding a ested facts into the training of the named en ty extractor and other analy c components. Improvements to fact extrac on Improvements to cross-document co-reference resolu on Related approaches can be applied to the subject data of the system as well as trusted data. Document classifica on and other document-level processes, applied to an en re corpus (e.g. all the documents from a par cular exploita on source), produce addi onal facts that the system can leverage to produce more and be er results. In this ini al effort, all data will be treated as fully-trusted. Another important area of expansion is to make use of data that is less than fully trusted, thereby managing issues of human review and provenance. Large scaling and integra on into cloud-based environments will reveal challenges beyond the appropriate scope of an ini al prototype. ENHANCED COLLABORATION VIA OPEN SOURCE The prototype system described above is a set of services that apply components (e.g. named en ty extrac on) to data. The overall service framework could be built as a collec on of GOTS components, but we would recommend and propose an open source development model. If the source code to the prototype is open source, it is guaranteed to be available for other efforts in the future. Since this problem space is not unique to government, the possibility of collabora on with industry is an important factor to consider for future growth and overall success. As a point of comparison, consider the ManifoldCF project at the Apache So ware Founda on (h p://incubator.apache.org/connectors/). This project is building a mul -level secure full-text search framework and is making great progress through open source collabora on. FOR MORE INFORMATION Contact Bill Ray, VP Federal Sales, at or write to info@basistech.com. Harnessing Publicly Available Factual Data in the Analy cal Process 5