Semantics is one of those terms that often pops up in conversation, arguments, and articles, but still has a fuzzy definition for many of us. We all nod and say, “Yes, well, semantics, you know…”, assuming we all have the same understanding. Further, do we recognize the power of semantics as a tool for not only conveying information, but also finding it?
Let’s take a few minutes to 1) agree on what semantics is and 2) look at what it can do for you as a prior art search aid.
Semantics & Semantic Searching Defined
Semantics is the basis for all we communicate and know. It brings meaning to configurations of words. The objective sources tell us semantics is:
- the branch of linguistics and logic concerned with meaning (Dictonary.com)
- the study of meanings (Merriam-Webster.com)
- the linguistic and philosophical study of meaning, in language, programming languages, formal logics, and semiotics (Wikipedia)
A keyword search (ironically enough) for semantic search on the Web produces multiple variations of the definition. Common terms are understanding, intent, context, ideas, correlations, and natural language. A semantic search system identifies what you want, finds the relationships between what you are asking for and what exits in the body of data, and then returns the most relevant results. It is a combination of known language patterns and evolving machine learning technologies that perform precise search and retrieval actions.
Semantic searching is also the basis for discovery. When you put forth a question, the most meaningful answer is often unpredictable. Semantic searching of databases helps you dig through information and find connections that you might not realize exist. It uses a type of artificial intelligence to identify meaning within context, not just count keywords and measure proximity of terms as keyword engines do.
To help you understand the value of semantic searching, let’s look at how it progressed.
Early Semantic Searching
Although the development of semantic modeling began in the 1970s, semantic searching is less than 20 years old. It took a little while for computer language to catch up to natural language. And for those of us that cut our digital teeth on floppy disks, natural language search entries still might seem a little, well, unnatural. We have been used to controlling the search output by controlling the input with keywords, punctuation, and operators. The problem is, limited input also limits opportunities for discovery.
In the mid-2010s, Google introduced algorithms to interpret user phrases and begin to understand natural language. This included a system that taught itself how to break down complex queries into manageable chunks from which it could decipher meaning. Still, the initial purpose of web searching was to find a piece of information and stop. Even though we had a little more flexibility with the input as we entered phrases, we needed more progress in terms of what the output could do for us.
Present Semantic Searching
Today, finding straightforward information is like a baby-step. You, as professionals in patent searching, competitive intelligence, business strategy, or research and development, look for more substantive answers, solutions to problems, and information that leads to next steps. To meet your needs, developments in machine learning and artificial intelligence focus on identifying meaning within relevant context. Better query processing with stronger semantic engines produces search results with great accuracy. You receive precise material to ingest and further analyze. At the next stage in the process, you apply human experience and use available analytics tools to discover new truths, identify new directions to take, or uncover new applications for methods.
Keep in mind that as access to and the quantity of information increase, you must also manage your data pools. Like the “you are what you eat” mantra, a system that grows based on machine learning is only as healthy as the data on which it feeds. Is the system consuming information from millions of sources of varying quality and credibility filled with billions of ideas (also of varying quality and credibility)? Or, does it have a healthy diet of select databases that contain information significant to your purposes? This is the difference between a search engine that learns from the Web and an engine that learns from, say, hundreds of thousands of cataloged technical documents.
Use Semantic Searching for IP Discovery
Meaning has value. In intellectual property protection and patent searching, the stakes are high. You can’t afford to miss anything in a prior art search, a competitive intelligence study, or a freedom to operate examination. You each have your specialty area, which is why a semantic-based engine is needed to interpret the meaning of your query and connect it to documents which respond to that meaning. Semantic searching facilitates a conversation between you and the data. Your most valuable search strategy harnesses a combination of advanced while ever-improving semantic processing capabilities, a continuously growing corpus of technical documentation, and tools to assist with your analysis. This leads you to practicable discovery.
To learn more about the value of semantic searching for IP discovery, databases dedicated to quality technical content, and analytics tools to help you decipher it all, contact IP.com.