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Discovering Semantic Metadata to Enhance Search & Content Management Systems

It does sound counter intuitive but all the evidence points to the conclusion most people are more frustrated with their company’s search capabilities than they are when searching the web – they just can never find what they need when they need it.

It seems counter intuitive because of the massive differences in the amount of information being searched in both instances. Logically it should be easier to find information within an organization as the data volumes are so much smaller, yet studies consistently show this is not the case. There are a number of reasons for this but the main reason is that on the web we are generally looking for any document / web site that meets out search needs whereas in enterprise search we are looking for a specific document or all documents relating to our search. This is a very different proposition. Combine this with the fact that we don’t have the same hyperlinks in-house that we have on-line, (hyperlinks are heavily exploited in internet search engines), and we start to understand why it is a more difficult problem to solve effectively.

The task for Information Architects, Library Scientists and Knowledge Managers, who have the responsibility to make organizational information available, is not an enviable one. Information is doubling annually within organizations and there is a lot at stake if these key employees do not find ways to make content highly findable. User frustration is the least of their worries, as often the financial performance of the Company as a whole rests on their shoulders. If employees cannot find the information they need when they need it, they are unable to perform properly and will make poor decisions based on incorrect or out of date information.

A lot of time & resources goes into solving this problem within organizations – the most common approach taken by many companies is to improve the findability of content by manually curating it with tags from organizational taxonomies, much like information is curated in libraries. This new metadata can then be used to significantly improve the performance of the company’s search tools & content management systems. While this leads to reasonably effective results it requires an enormous financial commitment up front to create the taxonomy and additional man power commitments to tag each piece of content with the relevant labels & keep it up to date (a challenge especially in fast moving scientific & technical sectors). For example, research from Taxonomy Strategies LLC indicates that to create a taxonomy for 100,000 documents, curate them & maintain the system over 4 years is more than $1.2M. Recent austerity measures, reductions in budgets and staff cutbacks have made commitments to this type of solution all but impossible. Some organizations have actually cut these staff from their organization altogether!

At Sophia we have been focusing on solving this problem in a cost effective way that meets the needs of the current needs of organizations. We are excited to announce the launch of our Digital Librarian - an automatic content enrichment system which creates semantic metadata for your content. There is no need for a taxonomy, all that is required are the documents that are to be enriched – Sophia reads through the content, understands what it is about, and then extracts topics, subtopics and document tags. It even discovers semantic tags (i.e., tags that do not occur in the document but refer to similar concepts) and adds these to the metadata. In addition, it also identifies which documents are most similar in meaning to every document within the collection.

This knowledge can be used to empower existing search tools, such as MS FAST, Google Search Appliance and Lucene/Solr to search semantically and to significantly improve the findability of indexed content. It can also be used to enrich content held within Content Management Systems (CMS) such as SharePoint, Documentum, and MarkLogic. This is a cost effective solution that can be deployed much faster, with significantly less resources, than traditional Taxonomy based solutions – in days as opposed to months.

Let’s look at a real example of what this Semantic metadata looks like. In this example we used Sophia to index a collection of news stories. We then choose one document from the collection and examined the semantic metadata assigned to it by Sophia

The chosen document read:-.

Civilians Still Aren't Military Targets; The Gods of War

The review of the sickening crimes described in "The Massacre at El Mozote" by Mark Danner (Books of The Times, May 9) arrives at the stunning conclusion that "there is no one to blame except the gods of war. The gods of war in Central America do not reside on Mount Olympus. They are in Washington. As a matter of policy, the United States Government and many United States officials, some named in this book, became accomplices to this genocide.

If you, like me, have never heard of EL Mozote and don’t know where it is, this document could have been written about any conflict in Central America. Generating valuable semantic metadata for this document is quite a challenge for any computerized system – especially for one that does not rely on any background knowledge.

Now let’s look at the tags automatically generated for this document by Sophia. We call this the semantic profile of a document.

Screen Capture Showing the Semantic Metadata Discovered by Sophia

From this we can see that it is quite clearly about El Salvador and the conflict with Leftist Guerrillas – this is information not contained in the document itself but intelligently discovered by Sophia! Sophia has classified this (and other similar documents not shown here) together under the same topic/sub topic (El Salvador). This demonstrates Sophia’s ability to understand the meaning and context of content and shows the quality of semantic metadata that is automatically added to content to make it more findable.

For each document, Sophia will also determine the most semantically similar documents, so for each document a list of other documents that have the most similar meaning are provided. This knowledge can also be leveraged by search tools within organizations to improve the quality of their search results and to ensure users find precisely what they are looking for. So for our example, similar documents identified include:

Screen Capture Showing the documents Sophia has Identified as Most Similar to the Example Document

All these are very relevant to anyone querying on this topic and can be found using any of the terms & phrases provided in the document metadata (semantic profile) created by Sophia.

So how does Sophia do this?

Sophia automatically builds a map of your content & uncovers relationships among documents using its patented suite of algorithms which are based on the principles of Semiotics & Intertextuality. From this map it creates the semantic metadata for each document. This knowledge is then passed back to your existing CMS/ search tool and is indexed by it in the normal way. The Sophia build process is a one off step, and from that point onwards, new content is indexed and added in real time. All communication between Sophia & existing systems is facilitated through simple to use RESTful web services.

We are really proud of the product and it is already proving its worth against systems that are based on Taxonomies. I encourage you to try Sophia’s digital librarian today and see for yourself. Create an account here, then  go to http://services.sophiasearch.com/nyt_metadata_retrieval to try for free. This demo is build based on content from news stories from the New York Times during the period 1987- 2007 – in order to understand it best, choose the document metadata tag option, copy a news story from today’s news & paste it into the box provided. Sophia will then tell you what topic & sub topic it belongs to along with all the other semantic metadata described above.

Let us know what you think!

Read the original blog entry...

More Stories By Bill Roth

Bill Roth is a Silicon Valley veteran with over 20 years in the industry. He has played numerous product marketing, product management and engineering roles at companies like BEA, Sun, Morgan Stanley, and EBay Enterprise. He was recently named one of the World's 30 Most Influential Cloud Bloggers.

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